Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections

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NANTOD-100833; No. of Pages 32

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Review

Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections Shilong Jiao a,1 , Xianwei Fu b,1 , Li Zhang b , Yu-Jia Zeng a,∗ , Hongwen Huang b,∗ a b

College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, PR China College of Materials Science and Engineering, Hunan University, Changsha, Hunan 410082, PR China

a r t i c l e

i n f o

Article history: Received 18 July 2019 Received in revised form 16 September 2019 Accepted 26 December 2019 Available online xxx Keywords: Defect Electrocatalysis Electron distribution Advanced characterization Energy and environment

a b s t r a c t Conflicts between the limited energy resources and the continuingly growing demand of the industry accompanied by worsening climate change have pushed the development of energy storage and conversion devices to the center of the research society. Electrocatalysis, which converts chemical energy and electricity to one another, has emerged as a potential solution for providing affordable clean energy and alleviating the environmental crisis. Defects with zero to three dimensions, which can intrinsically influence the electronic structure of the electrocatalysts and its interaction with the surrounding environment, has made the rational design and preparation of electrocatalysts with high activity, long-term stability and acceptable Faradaic efficiency be readily guaranteed. Here, we review the significant role of the defects in selected electrocatalytic processes based on the understanding of the link between the physics and chemistry of the defects. After a background introduction, we give a brief discussion of the formation process of the defects, along with the emerging preparation methods to generate imperfections in the electrocatalysts. Furthermore, a basic-theory-and-practice-combined introduction of the advanced characterization methods for the identification and analysis of defects will be presented. Next, we focus on the recent progress that has been achieved in developing efficient defect-featured electrocatalysts in a wide range of electrocatalytic processes. Finally, we conclude the challenges and opportunities in this promising and thriving field from our perspective. © 2019 Elsevier Ltd. All rights reserved.

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Defect formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Classification of defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Thermodynamic aspects for defect formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Kinetic aspects for defect formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Generation of defects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Solvo-(Hydro-)thermal synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Plasma treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Ion beam irradiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 High-temperature pyrolysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Doping strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Understanding of the roles of defects in electrocatalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Generation of the new band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Shift of band center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Regulation of the bandgap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

∗ Corresponding authors. E-mail addresses: [email protected] (Y.-J. Zeng), [email protected] (H. Huang). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.nantod.2019.100833 1748-0132/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: S. Jiao, X. Fu, L. Zhang et al., Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections, Nano Today, https://doi.org/10.1016/j.nantod.2019.100833

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Modulation of catalysis-evolved electron density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Advanced characterization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Microscopy techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .00 High-angle annular dark-field scanning transmission Electron microscopy (HAADF-STEM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Spectroscopy techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 X-ray absorption spectra (XAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 X-ray photoelectron spectroscopy (XPS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Electron paramagnetic resonance (EPR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Diffuse reflectance infrared fourier transform (DRIFT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Positron annihilation Spectrum (PAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Modern computational techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Density functional theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Molecular dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Monte carlo method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Other analysis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Defect engineering in electrocatalytic ORR, ECR, and NRR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Defects in oxygen reduction reaction (ORR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Defects in electrochemical CO2 reduction (ECR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Defects in N2 reduction reaction (NRR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

Introduction The fast decline of fossil-fuel-based energy resources with the increasing level of pollution and climate change has amplified the urgency of exploring new types of clean renewable-energy storage and conversion systems, which presents a great and meaningful mission for material chemists [1,2]. To alleviate the crisis, the development of the advanced energy conversion and storage systems such as water electrolysis, carbon dioxide electrolysis, fuel cells, metal-air batteries and so on is highly desired. The electrochemical approach is a conceptually common method for efficiently realizing energy storage and conversion by applying appropriate potentials for chemical bond breakage and reformation during the reaction process [3–5]. For the energy-related applications in electrocatalysis, the water cycle, the carbon cycle, and the emerging nitrogen cycle are subjected to intensive investigations due to their irreplaceable potentials in the future society [6–11]. Central to the water cycle is a series of hydrogen- and oxygen-related catalytic processes including oxygen reduction reaction (ORR), hydrogen oxidation reaction (HOR), hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) [12]. The CO2 transformation process is dominated by the nucleophilic attacks at the carbon, which requires a substantial input of energy (∼750 kJ mol−1 for the dissociation of the C O bond) [4,13]. And the activation of the N N triple bond (∼941 kJ mol−1 of the bond energy) in the dinitrogen molecules is the key in addressing the nitrogen reduction reactions (NRR) [14–16]. In order to fully address the facing challenges, a fundamental understanding of the basic principles, concepts, and mechanisms of the electrocatalysis are required in order to realize the next-generation energy devices. Inspired by the thriving hydrogen economy, the electrocatalytic water cycle has become a prominent area of research [17–20]. Generally, the HER and HOR processes that involve two-electron transfer are relatively simple, while the multi-electron transfer that often delivered sluggish kinetics in the OER and ORR processes limit the overall performance of corresponding energy devices [12,21,22]. The conventional noble-metal electrocatalysts can offer us with satisfactory reaction rates, yet the poor stability and the high-cost prohibit their large-scale application [21,22]. Therefore, the rational design and optimization of high-performance and lowcost alternatives are urgently demanded. As for the carbon cycle, electrochemical CO2 reduction reaction (ECR) is challenged by the

poor selectivity and the extremely stable CO2 molecules, which results in the large kinetic barriers [13]. Hence, a sufficient understanding of the relationship between the physical and electronic structures with the reaction mechanism is the key to addressing the contradiction. While the nitrogen reduction reaction emphasizes the selectivity of NRR over HER, the energy efficiency of the overall process and the system yield of NH3 [15]. Thus, the challenges to achieving high reaction selectivity, low kinetic overpotential and fast device output are the focuses of the field. As stated above, the solution for the drawbacks lies in the establishment of the relation concerning the chemical activity of the electrocatalysts and the electronic structures of them, and the theory-guided preparation of high-performance electrocatalysts that features high activity, high surface area, good electrical conductivity and long-term stability [12]. Various optimization protocols including defect engineering [23–25], strain engineering [26,27], hybrid [28–30], alloying [31], etc. have been developed to modify the physical, chemical, electronic and surface properties of the electrocatalysts. Among them, defect engineering, which intentionally introduces imperfections with different dimensions into the electrocatalysts, has drawn much attention for its feasibility to operate and obvious influence on the electronic structure, surface composition, the conductivity of the electrocatalysts and local environment of the active sites [23–25]. Usually, by deliberately generating imperfections in the crystalline of the electrocatalysts, optimized adsorption energy of the intermediate can be anticipated, which is crucial for enhanced electrocatalysis performances, according to the density functional theory (DFT)-based calculation results [12]. Apart from the theoretical investigations, various advanced microscopic and spectroscopic characterization methods have been used for the identification of the defects, and investigation of the effect of the defects on the local chemical environment around them, which is another thriving field for researchers [32]. In this review, from the point view of “electron distribution engineering”, we aim to give a comprehensive overview of the recent advances in the defect-featured electrocatalysts that apply in the environmental-related ORR (water cycle), ECR (carbon cycle), NRR (nitrogen cycle) and other catalysis reactions. We begin by giving a brief introduction of the classification of defects and their wide applications in chemical and physical areas. In the following section, we offer a short summary of the thermodynamic and kinetic aspects of the defect formation. Then we emphasize the under-

Please cite this article as: S. Jiao, X. Fu, L. Zhang et al., Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections, Nano Today, https://doi.org/10.1016/j.nantod.2019.100833

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Fig. 1. Schematic illustration of various types of imperfections in nanocrystals.

Fig. 2. Different types of point defects. (a) Vacancy. (b) Interstitial defects. (c) Substitutional defects. (d) Frenkel defect and Schottky defect.

Please cite this article as: S. Jiao, X. Fu, L. Zhang et al., Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections, Nano Today, https://doi.org/10.1016/j.nantod.2019.100833

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standing of defects in optimizing the catalysis performances in a wide range of different catalytic reactions by altering the electron distribution via four main ways. For the detection and characterization of the defects in the electrocatalysts, basic theory and their applications of several advanced characterization methods in the defect identification will be introduced, including microscopic and spectroscopic approaches. Then the recent advances in tailoring electrocatalysts from the aspects of constructing defectfeatured motifs for versatile electrochemical energy conversions (mainly ORR, ECR, and NRR) will be summarized. Finally, we provide insights into the challenges and future directions in this field from our point of view.

Defect formation Classification of defects As a matter of fact, the existence of perfect crystalline without any deviations from the theoretical descriptions of the crystals is fictitious. The imperfections of the crystals can even expand its territory to the nanoscale with a much bigger influence on the properties of the nanomaterials than that on the bulk materials due to the strong perturbation of the electron distribution on the nanoscale. As it is classically described, the imperfections of the crystals can be divided into several types according to the dimensionality or geometry, as illustrated in Fig. 1. The most often studied crystalline irregularity is the zero-dimensional defects (point defects), which are localized disruptions in an otherwise perfect atomic or ionic arrangement in a crystal structure. Traditionally, one-dimensional defect (line defect) is referred to as dislocations, including screw dislocations, edge dislocations, and mix dislocations, which are introduced into the crystals during solidification of the nanomaterials. Surface defects, the main twodimensional defects, are the boundaries, or planes that separate the nanomaterials into regions with the same crystal structure but different orientations. Three-dimensional defects (bulk or volume defects) are pores, cracks, foreign inclusions and other phases that are normally introduced during processing and fabrication steps. Among the above-mentioned imperfections in the crystals, the point defect has drawn intensive attention due to its feasibility for preparation and significant influence on the electronic structure of the nanomaterials. As the most important defect, which has a profound effect on the properties of nanomaterials, point defects have been discussed enormously on its large scale of applications, including photocatalysis [33], electrocatalysis [34,35], gas sensor [36,37], energy storage [38,39], solar cells [40–44], field-effect transistor (FET) devices [45], photodetectors [46–49], thermoelectric devices [48–51], surface-enhanced Raman scattering (SERS) [52,53] and so on. These imperfections can be introduced by the movement of atoms or ions when they gain energy by physical or chemical treatment during the preparation of the nanomaterials or by introductions of other atoms [23,24]. According to the position or composition of the imperfections, the point defect can be further classified into vacancy (including anion and cation vacancies which are formed by the disappearance of atoms from its normal positions, Fig. 2a), interstitials (which are formed when an extra atom or ion is inserted into the normally unoccupied positions, Fig. 2b), substitutional defects (which are formed when one or more atoms are replaced by a different type of atoms or ions, Fig. 2c), Frenkel defects (which are formed when an ion jumps from a normal lattice point to an interstitial site, Fig. 2d) and Schottky defects (which are formed when a stoichiometric number of anions and cations are missing from the normally neutrally charged crystals, Fig. 2d). In this review, we will focus mainly on the point defect

Fig. 3. Schematic illustration of the variation of Gibbs free energy with vacancy concentration at a fixed temperature.

because they are the elementary motifs for endowing the functions of defect-based electrocatalysts from the multi-dimensional scope. Thermodynamic aspects for defect formation Among different types of point defects, the vacancies are commonly observed due to the high temperature or physical treatment-induced deviation from chemical stoichiometry during the synthesis process. By a simple statistical thermodynamic approach, we take a perfect crystal of an elemental solid E with N atoms as starting material to get a basic understanding of the defect formation process. Vacancies can be readily generated by moving an atom from the bulk to the surface of the crystals, introducing the change in Gibbs energy that can be expressed in terms of the enthalpy of formation of each vacancy (G) and the entropy associated with the formation of defects (S). The entropy change can be further divided into a configurational entropy (arising from the distribution of n vacancies among all possible sites) and a vibrational entropy (arising from the change in vibrational modes of the atoms around the vacancy), thus the change in Gibbs free energy resulted from the formation of nv vacancies can be expressed as follows: G = nv (H − TSvib ) − TSconf By further approximation, the fraction of the total number of sites that are vacant under equilibrium can be concluded (Fig. 3). The equilibrium number of vacancies Nv for a given quantity of material can be determined by Boltzmann distribution according to the statistical thermodynamics (Hv = Hv - TSvib ):

 H  v

nv = N exp

−

kT

in which, N is the total number of atomic sites, Hv is the energy required for the formation of a vacancy, T is the absolute temperature in kelvins, and k is the Boltzmann’s constant. It should be pointed out that the above equation often underestimates the number of vacancies because of the nonequilibrium vacancies generated during the crystal growth or post-treatment process [54–57]. Similarly, the equilibrium number of Frenkel defect and Schottky defect can be defined as follows: 1

nF = (NNi ) 2 exp

 H  F −

2kT

Please cite this article as: S. Jiao, X. Fu, L. Zhang et al., Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections, Nano Today, https://doi.org/10.1016/j.nantod.2019.100833

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the rate theory and dynamic theory can be used to determine the activation law for migration. Further, as a result of the thermal agitation, the defects can wander through the lattice, which can be described using Fick’s law: j = −D∇ c where, j is the flux of atoms which diffuse across the unit area of a plane in unit time, and ∇ c = ∂c/∂x is the concentration gradient of the defect. Generation of defects

Fig. 4. Schematic representation of different diffusion mechanisms. (a) Interstitial mechanism. (b) Interstitialcy mechanism. (c) Exchange mechanism. (d) Ring mechanism. and (e) Vacancy mechanism.

Fig. 5. Sketch of the energy barrier along the migration path.

where nF is the number of Frenkel defects in an MX crystal, Ni is the number of interstitial sites available in the crystal, HF is the energy needed for the formation of a Frenkel defect. nS = N exp

 H  S −

2kT

where nS is the number of Schottky defects, HS is the energy needed for the formation of a Schottky defect. Kinetic aspects for defect formation Depending on whether the defect is substitutional or interstitial, the mechanisms that allow for the defect to move through a lattice can be described in two classes (Fig. 4). The migration of an atom occurs when it moves from its stable positions (Q) to a neighboring equivalent one (R). The rate of this process can be concluded in the framework of Born-Oppenheimer approximation where the electrons and the nuclei motions are decoupled. By minimizing the energy difference between the initial and final positions (Fig. 5), the rate K can be intuitively described in the form as follows: K = v exp

 E  −

kT

Where the exp(-E/kT) is the possibility of excitation over the barrier and v is the effective frequency. Two different methods, namely

For its significance in changing the electronic structure in the catalyst, controlled defect preparations have drawn much attention. Here, we give a brief overview of the commonly used methods to introduce imperfections into nanomaterials, including but not limited to chemical etching, plasma treatment, solvo/hydrothermal synthesis, chemical/physical vapor deposition (CVD/PVD), sol-gel synthesis, sonochemical method, electrochemical deposition, high temperature pyrolysis, exfoliation, thermal conversion synthesis and so on. Solvo-(Hydro-)thermal synthesis As it is known that the hydrothermal method is one of the most often used solution-based approaches that exploit water or organic solvents under certain temperatures and pressures. The defects can be formed in-situ during the reaction process and it is rather fluky to obtain certain kinds of defects with appropriate concentration. Although massive reports have proved its practicability in generating defects, the inner mechanism lied behind the phenomenon remains undiscovered for the lack of in-situ observation techniques. Recently, Song [58] et al. prepared Ni(OH)2 nanomaterials with Ni vacancies by carefully controlling the hydrolysis process with an appropriate amount of water, which delivered boosted OER (Oxygen Evolution Reaction) and UOR (Urea Oxidation Reaction) performances. Xie’s group [59–65] has investigated in detail various defects generated during the hydrothermal process and their influence on the catalysis performances, which validated the effectiveness of hydro(solvo)thermal method in preparing defected materials. Plasma treatment By subjecting a neutral gas to a strong electromagnetic field, plasma can be artificially generated, which can be used for the surface treatment of the electrocatalysts, resulting in the formation of defects. Zheng [66] et al. treated MoS2 with mild Ar plasma and induced S vacancies into the basal planes of MoS2 , as a result of which, the commonly inert basal planes were activated and the catalyst delivered enhanced HER performances compared to the untreated sample. Wang [67–73] et al. have expanded the scale of application of the plasma and various defected nanocatalysts have been obtained, and due to the presence of vacancies, the electrocatalysts delivered excellent catalysis performances in OER, ORR and HER processes. Ion beam irradiation The ion beam has shown its capability to modify and tailor the properties of versatile 2D materials by manipulating the energies, species, fluencies, incident angles, etc. of the ion beams, leading to the discovery of novel properties of the electrocatalysts [74–77]. Recently, Drndic [74] et al. created nanoporous MoS2 membranes containing angstrom-size defects via the treatment with Ga+ ion beams, which possessed almost the same size with the irradiation dose surpassing the threshold. The experiments indicated that the

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conductance must occur through the few large pores within the distribution and the majority of the defects did not allow the ions to pass. High-temperature pyrolysis By treating metal-contained organic molecules or structures, such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), or just simple coordination compound, single metal atoms anchored carbon materials can be readily obtained [78–83]. Recently, Jiang [84] et al. have obtained single atom iron-implanted N-doped porous carbon (FeSA -N-C) via pyrolysis of porphyrinic metal-organic frameworks. With a high content of single Fe atoms, the FeSA -N-C catalyst exhibited excellent oxygen reduction activity and long-term stability, surpassing almost all non-noble-metal catalysts and state-of-the-art Pt/C, in both alkaline and acidic media. Doping strategy The introduction of foreign atoms into the lattice of pristine nanomaterials can greatly influence the electronic structures, favoring the electrocatalytic process. By intentionally doping Pd atoms into the basal plane of two-dimensional MoS2 , the electrocatalyst delivered enhanced activity towards HER with an overpotential of 78 mV at a current density of 10 mA cm−2 and an exchange current density of 805 ␮A [19]. In addition to the doping with one kind of atoms, co-doping that involves more than one kind of element is also proved effective in optimizing the electrocatalytic process. By doping the graphene framework with nitrogen and phosphorus, excellent electrocatalytic activity towards ORR and OER can be achieved with an ORR overpotential of 0.845 V vs. RHE at 3 mA cm−2 and an OER potential of 1.55 V at 10 mA cm-2 [85]. In addition to the aforementioned methods, conventional approaches (chemical etching [67,86,87], exfoliation [69,70,88], CVD/PVD [66,89–91] and electrochemical method [92,93], etc.) have also shown their capability in creating various types of defects in the electrocatalysts and more detailed introduction can be found elsewhere [23–25]. Understanding of the roles of defects in electrocatalysis Unraveling the diverse descriptors of the catalytic activity is essential for the rational design of electrocatalysts. It has been demonstrated that the reaction rate for an electrocatalytic process is significantly dependent on the intrinsic electronic structure of the electrocatalysts, which determines the thermodynamic and kinetic aspects in a specific electrochemical reaction by altering the intermediate adsorption energy, reaction barrier and activation energies [94,95]. Thus, optimization of the electron distribution for the preferential adsorption/desorption behavior of the important intermediates offers us a rational way for the realization of efficient electrocatalysts with high intrinsic activity, long-term stability, and desired selectivity. Among all the surface properties of the electrocatalysts, the defects with various dimensions are of the most common aspects that directly influence the electron distributions by breaking the long-range symmetry of the perfect lattices [11]. Here, in this section, we summarize the four aspects via which the defect affects the electron distribution in the defect-featured electrocatalysts, which may help to a better understanding of the defect effect in the electrocatalysis. Generation of the new band Certain impurities and imperfections drastically affect the electrical properties of the materials. By adding a controlled amount of

specific dopant or impurity atoms (including vacancies), the electrical characteristics of the extrinsic electrocatalysts can be altered greatly. Depending on the intrinsic property of the heteroatom, different types of the intermediate band can be generated, which can favorably change the catalysis process in the electro- or photoelectrocatalysis reactions (Fig. 6a). As for the donor states, the extra electron moves in the Coulomb potential of the impurity of ion in the crystal lattice. By proper simplification of the models, we can reach the conclusion that the ionization energy for the donor impurity can be expressed as: Ed =

e4 me 2 ∈ 2 2

where me is the effective mass of the electron, ∈ is the dielectric constant. This gives rise of an extra band near the bottom of the conduction band (while an extra band near the top of the valence band in the case of acceptors), namely the intermediate band. Herein, by rational design and generation of different species of imperfections into the perfect lattices of the electrocatalysts, an intentionally introduced intermediate band can be readily positioned, which can potentially enhance the intrinsic activity of the active sites and increase the number of active sites. What needs our attention is the fact that the defects can also act as the recombination centers in the electrocatalysis process, so there always exists an optimal amount of defects for enhanced electrocatalytic processes [66,96]. Recently, Xie [97] et al. for the first time realized infrared (IR)-driven CO2 overall splitting by the introduction of the intermediate band between the valance band and conduction band, which was achieved by adjusting the concentrations of oxygen vacancies in the ultrathin WO3 semiconductor. The authors argued that a high concentration of oxygen vacancies in the WO3 favors the adsorption of CO2 and activation into COOH* radical, guaranteeing increased CO and O2 formation rates. In addition, Wang [98] et al. have successfully filled the oxygen vacancies in the Co3 O4 with phosphorus (P-Co3 O4 ), in which case the band gap reduced from 1.5 eV for the pure Co3 O4 to about 0.3 eV for the P-Co3 O4 due to the introduction of the new electronic states in the conduction band. Benefited by the combination of improved conductivity and favorable intermediate binding strength, the P-Co3 O4 exhibited excellent electrocatalytic activity in the HER and OER processes. Shift of band center As it is well documented that the binding strength between the catalytic surface and the reaction intermediates governs the reaction rate, which is commonly referred to as the reaction descriptor [10]. And the binding energy of an adsorbate to the transitional metal surface is largely dependent on the electronic structure of the surface itself [11,99,100]. Due to the fact that all transition metals have a half-filled, broad s band, the adsorption energy can be written as [101–103]: E = E0 + Ed where E0 is the bond energy contribution from the free-electronlike sp electrons, which is independent of the metal and Ed is the contribution from the extra interaction with the transition metal d electrons. With the simplification of the Newns-Anderson model, the Ed can be linked with the d-band center position, making it the most important variable (Fig. 6b). The conclusion by Norskøv (d-band theory) indicates that the position of the average energy of the d electrons relative to the Fermi level should to a first approximation to determine the variations in the interaction energy [103]. According to the d-band theory in the ORR process, the upshift of the d-band center causes the stronger interaction with the adsorbates, while the downshift of the d-band center causes the weakening of

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Fig. 6. Schematic illustration of the band alignment difference before and after the introduction of defects into the electrocatalysts. (a) Generation of the new band. (b) Shift of band center. (c) Regulation of bandgap. (d) Modulation of catalysis-evolved electron density.

the interaction [99], which implies the possibility of modifying the electronic structure and thus the catalytic activity by the defect engineering in a given electrocatalyst. As mentioned, the bandwidth depends on the coordination environment of the metal and this leads to the substantial variations in the d band centers, as well as the alloying effect. By doping the Pt nanowires with Rh, Zeng [104] et al. obtained Rh-doped Pt nanowires (NWs) with enhanced mass activity (7.8fold) and specific activity (5.4-fold) with excellent long-term stability. The compressive strain and ligand effect in the Rh-doped Pt NWs caused the downshift of the d-band center, leading to the optimized adsorption energy of the hydroxyl, thus the boosted electrocatalytic performance.

Regulation of the bandgap As it commonly documented that the energy band structure, carrier concentration, and mobility are the internal determinants of the intrinsic electrical conductivity of the electrocatalysts [105]. For the extrinsic materials, the defect concentration (dopant, etc.) at room temperature is greater than the thermally generated intrinsic carrier concentrations, leading to the dependence of conductivity on the carrier concentration and mobility (Fig. 6c). As it concluded, the conductivity can be calculated following the equation:  = A exp(−Eg /2kT ) ( is the conductivity, Eg is the bandgap energy). With reduced bandgap after the introduction of defects, higher carrier concentration and electrical conductivity can be anticipated, which can lead to enhanced electrocatalytic performances. By incorporating the oxygen into the ultrathin MoS2 nanosheets, an obviously reduced bandgap of 1.3 eV could be observed due to the significantly enhanced hybridization of the Mo d-orbital and S p-orbital, as reported by Xie [106] et al. The introduction of oxygen atoms could result in higher carrier concentration and electrical conductivity, leading to enhanced electrocatalytic HER properties. As it commonly is known that the nanostructured MoS2 with a large bandgap suffers from sluggish kinetics in the HER process [106,107]. By carefully designed engineering of its energy level via

direct transitional metal doping, Chen [107] et al. found that the Zndoped MoS2 delivered superior catalytic activity due to the energy level matching and rich active sites by thermodynamic and kinetic acceleration.

Modulation of catalysis-evolved electron density As mentioned above, the adsorption/desorption of the reaction intermediates, which is directly correlated with the reaction energy barrier, has been considered as a more intrinsic parameter for evaluating the electrocatalytic activity of the electrocatalysts. Interaction between valence band of the active center (generally d band for transition metal) and the normally s orbit of the adsorbates determined the energy needed for the adsorption or desorption of the intermediates, which can be favorably tuned by modulation of the electron density in the relative bands [105]. Defects created via various methods can greatly perturb the normal configuration of the electrons, leading to preferential electron distribution, thus optimized adsorption energy (Fig. 6d). With optimized adsorption energy between the adsorbates and catalytic surfaces, the intrinsic activity of the active centers can be potentially boosted, leading to the enhancement of the electrocatalytic performance of the electrocatalysts. Recently, Baek [108] et al. prepared atomically dispersed Cu on ultrathin nitrogen-doped carbon (Cu-N-C) with ultrahigh loading of 20.9 wt% and evaluated its ORR properties. DFT calculations indicated that the Cu active sites exhibit improved O O bond stretching and favorable adsorption energies of O2 and OOH, which contributed to its enhanced ORR performances. By introducing sulfur vacancies into the MoS2 nanoflowers, Sun [109] et al. prepared defect-rich MoS2 and investigated its NRR performance. The enhanced NRR performance can be greatly attributed to the vacancy induced optimization of the electron density, which lowered the energy barrier for the potential determining step. Keeping these in mind, we evaluate the roles of defects during the elementary steps of the heterogeneous catalysis process (Fig. 7a). After the initial diffusion through a boundary layer sur-

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Fig. 7. (a) Illustration of the elementary reaction steps on the surface. (b) Potential energy diagram for the approach of reactant toward a surface. (c–d) Reaction path of the defect-featured electrocatalysts before and after the introduction of defects for the electrocatalytic process.

rounding the catalyst particle, the adsorption of the reactants onto the surface occurs. As stated by Sabatier that a good heterogeneous catalyst is a material that exhibits an intermediate strength of interaction with the reactants, products, and intermediates of the catalytic process [110]. With the introduction of defects, the strength of the adsorption of the reactant can be enhanced, changing from physisorption to chemisorption (Fig. 7b), which could facilitate the kinetics of adsorption, leading to the boosted electrocatalytic performance [105,111–113]. Further, in the following surface reaction process, the defect-induced electron redistribution could also lower the energy barrier for the rate-determining step (RDS), which could accelerate the reaction kinetics (Fig. 7c), favoring the electrocatalytic process [114–117]. Moreover, the defect-induced electron behavior can change the reaction pathway by optimizing the reaction barrier towards different intermediates (Fig. 7d), giving us the opportunity for the rational design of electrocatalysts with desired selectivity and activity [118–121]. Advanced characterization techniques The positive effects of various defects in the electrocatalytic process have been widely reported as mentioned above, in which various advanced characterization techniques have been applied to reveal the defects. It is therefore necessary to give a brief overview of the commonly utilized advanced characterization methods with the basic physical principles underlying them and how the analysis results link with the actual imperfections within the electrocatalysts, including but not limited to XAS (X-ray Absorption Spectra), HAADF-STEM (High-Angle Annular Dark-Field Scanning Transmission Electron Microscopy), XPS (X-ray Photoelectron Spectroscopy), SRPES (Synchrotron-Radiation Photoemission Spectroscopy), EPR (Electron Paramagnetic Resonance), DRIFT (Diffuse

Reflectance Infrared Fourier Transform), PAS (Positron Annihilation Spectrum) and so on. Microscopy techniques High-angle annular dark-field scanning transmission Electron microscopy (HAADF-STEM) Transmission electron microscopy (TEM) is a microscopy technique that employs a high-energy electron beam to transmit through a specimen and generates various information of the specimen, including but not limited to material size, shape, crystallinity, composition and elemental mapping. Unlike the conventional TEM (Fig. 8a), Scanning TEM (STEM) deploys a fine focus beam, which is formed by a probe forming lens before the beam reaches the specimen, to address each pixel in series as the probe is scanned across the sample (Fig. 8b). Among various STEM-based techniques, high-angle annular dark-field STEM (HAADF-STEM) is of particular interest to the nanomaterial society. High-angle scattered electron are few in number and most of them result from Rutherford scattering, which makes them insensitive to structure and orientation but strongly dependent on the atomic number of the specimen [122]. The intensity of the signal collected by the detector varies as the Z␣ , where ˛ is a constant between 1.5 and 2.0 and is often quoted around 1.7 for most experimental setups. This signal leads to the so-called Z-contrast imaging which carries abundant information about the specimen, especially the local chemical environment. For its convenience to directly observe imperfections in the nanomaterials, HAADF-STEM has been more and more popular in the defect investigations, especially for the identification of vacancies and heteroatoms. As discussed above, the signal intensity of the HAADF-STEM image depends strongly on the atomic number in the nanomaterials, which makes it visualized to distinguish different

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Fig. 8. (a) The electron beam in the conventional transmission electron microscope (TEM). (b) Schematic illustration of the STEM imaging mode as a function of the angles of the electrons within the specimen. (c) HAADF-STEM images of single-atom Pt1 /FeOx catalyst. Pt single atoms (white circles) are seen to be uniformly dispersed on the FeOx support and occupy exactly the positions of the Fe atoms. Reproduced with permission [123]. Copyright 2011, Springer Nature. (d) HADDF-STEM image of A-Ni@DG and the zoomed-in image of the defective area (vacancy) and defective area (with atomic Ni trapped). Reproduced with permission [124]. Copyright 2018, Elsevier. (e) HAADF–STEM image of Co–S MoS2 , showing two bright contrast sites in the MoS2 monolayer (arrows) with the HAADF image simulation and corresponding atomic model. Reproduced with permission [125]. Copyright 2018, Springer Nature.

components of the target materials. Zhang [123] et al. prepared single Pt atom dispersed Pt/FeOx structure and the HAADF-STEM gave a direct observation of the heteroatoms on the FeOx substrate with good dispersion for their distinct different atomic numbers and further investigation of the HAADF image revealed the substitution of Fe atoms by Pt single atoms (Fig. 8c). By intentionally introducing defects into graphene nanosheets, Yao [124] et al. successfully trapped abundant single atomic Ni species into the vacancies, which can be clearly observed in the HAADF-STEM images (Fig. 8d). Tsang [125] et al. prepared single Co-doped MoS2 and examined its morphology and composition with HAADF-STEM (Fig. 8e). As can be obviously detected, sites scattered across the basal plane that was of higher contrast than the surrounding Mo and S sites originated from the constituent atoms in the scanned atomic column, namely doped Co atoms. Spectroscopy techniques X-ray absorption spectra (XAS) X-ray absorption spectroscopy, which measures the energydependent fine structure of the X-ray absorption coefficient ((E)) near the absorption edge of a particular element, is a well-established analytical technique used extensively for the characterization of materials [93,126–128]. (E) is a smooth function of the photo energy, which follows approximately as (E) ∼dZ4 /mE3 , (d is the target density, Z is the atomic number and m is the atomic

mass) over large energy regions. In the case that the photon energy exceeds the binding energy of a core electron, a new absorption channel is available, which leads to a sharp increase in the absorption coefficient. For energies higher than the energy gap between the unoccupied bound state and the core level, the photoelectron is promoted to the continuum state, as a result of which, a wave that propagates outwards is created and scattered at neighboring atoms (Fig. 9a-b). The outgoing and scattered waves interfere in a manner that depends on the geometry of the absorber environment and on the photoelectron wavelength. Thus, two main regions are commonly distinguished, namely the X-ray absorption near edge structure (XANES) and the extended X-ray absorption fine structure (EXAFS), as schemed in Fig. 9c. As discussed above, the position and shape of the XANES features at the absorption edge are dependent upon the local chemical environment and the effective charge on the absorbing atom. Theoretical calculations of the fine structure in this region are complex and only limited accuracy can be achieved. Therefore, analysis of the XANES spectra typically compares the measured one to those of known standards and qualifies the ratios by which these standards are present in the sample using linear combination fitting. As in the case of EXAFS, the photoelectron is promoted to the continuum state when the photo energies are higher than ∼30 eV above the edge, which means that the EXAFS is independent of chemical bonding and depends heavily on the atomic arrangement around the absorber [131,132]. It contains information about the

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Fig. 9. (a) Schematic illustration of the absorption process. (b) Schematic illustration of the absorbing atom and its first nearing neighbors. (c) Absorption coefficient (E) versus the photon energy above the edge, which is divided into XANES and EXAFS. (d) XANES spectra of the defective TiO2 nanosheets, the red area highlights the near-edge absorption energy (left). XANES spectra of Ti L3 -edge (right). Reproduced with permission [129]. Copyright 2018, John Wiley & Sons, Inc. (e) Co K-edge extended XAFS oscillation function k3 ␹(k) and the corresponding Fourier transforms FT(k3 ␹(k)). Reproduced with permission [130]. Copyright 2017, Springer Nature.

coordination number, interatomic distances, structure and thermal disorder around a particular atomic species [133,134]. Moreover, the EXAFS does not require the long-range order and is applied to the structural analysis of a wide range of ordered and disordered materials. Different from the XANES, theoretical calculations of the fine structure in the EXAFS region are now available with sufficient accuracy, which constitutes two important parts of the experimental procedure with the measurement of suitable standards. The EXAFS can provide significant structure information about the 5 ∼ 6 Å space around an absorbing atom. Therefore, for the ultrahigh sensitivity of the X-ray absorption towards the local chemical environment, the XANES and EXAFS have been intensively used in the qualitative and quantitative description of defects in the nanomaterials [135–138]. Recently, Li [139] et al. prepared defective TiO2 supported Au single-atom catalyst and investigated the effect of O vacancy on the catalysis performance. To demonstrate the existence of the defect, namely O vacancy in the anatase TiO2 nanosheets, XANES spectrum was collected and analyzed (Fig. 9d). The similarity of the relative intensities of the peaks and their line shapes indicated the anatase phase of the perfect and defective TiO2 . As proved elsewhere, the intensity of the pre-peak B increased as the central Ti atom sites become more non-centrosymmetric compared with the perfect TiO2 . As can be observed in Fig. 9d, the growing non-centrosymmetry of the defective TiO2 indicated the presence of point defect (O vacancy) near the Ti atom. The spectra lines of peak t2g (L3 ) for the perfect and defective TiO2 are almost the same, indicating the same oxidation state of Ti in both the samples, while the difference of the XANES peaks at eg (L3 ), t2g (L2 ), and eg (L2 ) is the reflection of the defective state (i.e. oxygen vacancy) in the defective TiO2 compared with the perfect TiO2 . Xie [130] et al. synthesized oxygen-vacancy rich and oxygen-vacancy-poor Co3 O4 samples and carefully investigated the EXAFS spectrum to reveal the different defect concentrations in the nanomaterials. As can be seen, the post-edge oscillations amplitude of the Co K-edge for the Vo -rich Co3 O4 single-unit-layers are obviously different from the Vo -poor Co3 O4 single-unit-layers

and bulk counterpart, further confirmed by the Fourier transformed k3 ␹(k) functions, qualitatively indicating their distinct local atomic arrangement (Fig. 9e). Derived from the EXAFS data, the coordination numbers were calculated. Compared with the bulk sample, the Co-O, Co-Co1 , Co-O1 , Co-Co2, and Co-O2 coordination number reduced with a smaller extent for the Vo -poor Co3 O4 single-unitlayers, implying the presence of many dangling bonds as well as an obvious distortion on their surface. In addition, the Co-O, Co-O1 , CoO2 coordination numbers of the Vo -rich Co3 O4 single-unit-layers further decreased compared with that of Vo -poor Co3 O4 singleunit-layers with the almost same Co-Co1 and Co-Co2 coordination numbers, indicating the higher concentration of oxygen vacancies.

X-ray photoelectron spectroscopy (XPS) As commonly stated, the catalysis process mostly happens on the very surface of the catalyst, which makes it significantly important to investigate the surface properties of the designed electrocatalysts. X-ray photoelectron spectroscopy (XPS), namely electron spectroscopy for chemical analysis (ESCA), is a wellestablished surface-sensitive method for the analysis of solid materials, which is capable of providing atomic and molecular information regarding the surface of the nanomaterials in the range of ∼100 Å [140]. The physical basis of XPS is the photoelectric effect, namely the ejection of electrons from the surface. In an XPS test, the kinetic energy of the emitted electron is the quantity measured, which is of discrete nature and is a function of the electron binding energy, making it element and environment-specific. As a nondestructive technique, the XPS results offer us information relating to the energy distribution of any electron emissions within the predefined energy range, the special distribution and depth distribution of specific electron emissions [141]. Moreover, the specimen to be analyzed can be in the form of solid or liquid, which makes it even more popular in catalysis society. Nowadays, different from the conventional XPS method in X-ray sources, a newly developed method called synchrotron-radiation photoemission spectroscopy (SRPES) is drawing more and more

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attention for its unique applications in the catalysis study. Unlike the commonly used X-ray source, the synchrotron-radiation-based source has a number of unique properties, including the high brightness, the wide energy spectrum (0.1 ∼ 10 keV), the much higher photon flux per area, the highly collimated beam, and the allowance of the polarization of the beam [140,141]. By tuning the photon energy, the kinetic energy and the information depth of the photoelectrons can be varied, which makes it possible to investigate the vary surface of the catalyst, providing much accurate electron distribution information of the units involved in the catalysis process. For example, the kinetic energy of the photoelectron of interest can be adjusted to about 50 eV (the energy for which the attenuation length is about 1 ∼ 2 monolayers) by selecting the appropriate photon energy, which gives us the specific bonding state of the specific units. Due to the change of the electronic structure of the nanomaterials after the introduction of defects, the energy needed for an electron to eject from the surface can be affected, leading to the difference of the XPS spectra [144]. By annealing BiOBr nanoplates under vacuum, Xie [142] et al. prepared BiOBr samples with oxygen vacancies, and the XPS was chosen to identify the existence of oxygen vacancies. As can be seen, the broadened O 1s spectrum of the BiOBr-OV can be ascribed to the adsorbed oxygen species at the vacancy sites (Fig. 10a). By reducing MoO3 nanosheets under the H2 atmosphere for different durations, Wen [143] et al. obtained MoO3-x with increasing oxygen vacancy concentrations, and XPS spectrum was used to validate the conclusion. As can be observed, with the increasing reducing time, the intensity originated from Mo5+ was enhanced and finally Mo4+ can be detected, which indicated the increasing concentration of oxygen vacancies (Fig. 10b-e). The ultra-sensitivity of the SRPES towards the very surface of the nanomaterials has made it an effective way to study the chemical environment of the defected nanomaterials. Xie [97] et al. prepared ultrathin WO3 atomic layers with different oxygen vacancy concentrations via a simple solvothermal method (Fig. 10f) and the SRPES spectra confirmed that the oxygen vacancies not only upshifted the valence band edges but also caused a new peak at ca. 0.63 eV, which provided more solid evidence for the formation of intermediate bands in the oxygen-deficient WO3 atomic layers (Fig. 10g). Due to the suitable band edge positions and the intermediate bands associated with ultrathin thickness, the Vo -rich and Vo -poor WO3 atomic layers exhibited IR-driven CO2 reduction to CO and O2 . Electron paramagnetic resonance (EPR) Based on the Zeeman effect, the energy differences investigated in the EPR, also known as electron spin resonance (ESR), are predominately due to the interaction of an unpaired electron in the nanomaterials with the magnetic field produced by the equipment [145]. The energies of the two spin states of an unpaired electron diverge when the magnetic field is scanned. There will be the absorption of energy by the spins when the magnetic at which the energy difference between the two-electron spin states is equal to h for the spectrometer [146]. Thus, for the systems with unpaired electrons, such as vacancy induced dangling bonds, charge redistribution resulted from doping and so on, the EPR can play an important role in identifying the interesting properties they bring. With the missing atom from the crystal lattices, the dangling bond comes into shape, which may introduce the single-pair electrons that can be detected by the EPR. By introducing hydrogen atoms into the oxygen vacancies of the TiO2 nanocrystals, Cheng [147] et al. prepared OV-TiO2 and OVH -TiO2 , which displayed remarkably different features of oxygen vacancy-related ESR signals, demonstrating the existence of atomic hydrogen in the OVH -TiO2 . Compared to the broad signal at g = 1.958 in OVTiO2 assigned to Ti3+ ions in oxygen-deficient TiO2 , the signal at

11

g = 2.002 is typically considered to result from an electron trapped in an oxygen vacancy (Fig. 11a-b). Zhang [148] et al. prepared BiOCl single-crystalline nanosheets with {001} facets exposed with different oxygen vacancy concentrations which showed obviously distinct ESR intensities (Fig. 11c-d). In addition to the detection of anion vacancy, the EPR spectra can also be utilized for the identification of cation vacancies. As illustrated below, Xie [59] et al. adopted the EPR spectra for the investigation of Zn vacancies of the ZnIn2 S4 atomic layer, which indicated the much higher Zn vacancies of the products obtained at 200 ◦ than that at 180 ◦ (Fig. 11e-f).

Diffuse reflectance infrared fourier transform (DRIFT) Owing to its wide applicability to both the condensed phase and gaseous state, the infrared spectroscopy is a useful tool for molecular structural studies, identification and quantitative analysis of nanomaterials. The diffusion-reflection measurement is often used for measuring infrared absorption spectra from powder or powdered samples and utilized as a tool for the in-situ measurements for the absorption, desorption and catalysis mechanism. The diffuse-reflection spectrum of a specimen contains similar information as the infrared absorption spectra but with much stronger signal intensity due to the multiple absorption behavior of the infrared light inside the sample, which is beneficial for the identification of the intermediates during the catalysis process and the existence of the defects. Zeng [149] et al. prepared Co4 N by skillfully incorporating N atoms into Co nanosheets, which is verified by the DRIFT (Fig. 12a). As depicted in Fig. 12b the strong peak at 3140 cm−1 was assigned to N H stretching vibration, which arose from the incorporated N atoms in the Co nanosheets. Also, Zeng [150] et al. has utilized DRIFT to prove the single Pt atoms on the MoS2 ultrathin nanosheets, which possessed a predominant peak at 2069 cm−1 , corresponding to CO adsorbed on Pt ı+ sites.

Positron annihilation Spectrum (PAS) Because of the ability of the positron to annihilate from a variety of specific states in solids, such as highly localized states in lattice defects, PAS can offer us unique information regarding various properties of nanomaterials [152]. In the annihilation process, the electron and the positron are transformed into -photons and the lifetime of the photons depends on the electron density sensed by the particle [153]. In the presence of lattice defects such as vacancies, vacancy clusters or dislocations, the positron can subsequently be trapped in a bound state in such defect, leading to the change in the positron annihilation spectrums [153]. Thus, the PAS can provide direct information about the type, and relative concentration of defects, which makes it an outstanding technique in the investigation of the defects in nanomaterials. By analyzing the positron lifetime and intensity, information about the defect type and relative concentration could be gained using the PAS [142,151,154]. Xie [151] et al. prepared BiOCl nanosheets and nanoplates and PAS was chosen to identify the type and concentration of different kinds of defects (Fig. 12c). They argued that the shortest lifetime (␶1 , around 250 ps) in the experimental positron lifetime spectra could be attributed to positron annihilation as trapped at the single isolated bismuth vacancies, whereas another (␶1 , around 325 ps)) could be assigned to Bi3+ -oxygen vacancy associates, V  Bi VO•• V  Bi (Fig. 12d-e). And the relative intensity of the positron lifetime offered extra information about the distribution of these defects, from which the conclusion that triple Bi3+ -oxygen vacancy associates are predominant in ultrathin BiOCl nanosheets and isolated bismuth vacancies are predominant in BiOCl nanoplates can be drawn.

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Fig. 10. (a) Atomic-resolution HAADF–STEM image of BiOBr-OV and the corresponding O 1s spectra. Reproduced with permission [142]. Copyright 2018, American Chemical Society. (b–e) High-resolution XPS spectra of the Mo 3d of MoO3 NSs, MoO3− x NSs-1, MoO3− x NSs-2, and MoO3− x NSs-3, which were obtained with different reducing time. Reproduced with permission [143]. Copyright 2017, Elsevier. (f) TEM images of the Vo -rich WO3 atomic layers. (g) SRPES valence-band spectra and the corresponding enlarged spectra between -1.0 and 2.0 eV. Reproduced with permission [97]. Copyright 2018, Elsevier.

Modern computational techniques The need for the development of clean methods for both energy production and chemical synthesis has generated renewed interest in electrocatalysis. Despite the experimental advances in the application of spectroscopic and microscopic methods, development in theoretical methods [155–158](density functional theory (DFT)-based calculations, molecular dynamics (MD) simulations, and Monte Carlo simulations, etc.) provide new insights in understanding the electron behaviors induced by the imperfections. Moreover, the advent of high-throughput catalyst screening based on the recently thriving machine learning has also benefited the rational catalyst design [159,160]. Here, we give a brief g introduction of the most often used computational methods, followed by a few examples, and highlight the ongoing developments in the defect-featured electrocatalysts. Density functional theory As it commonly documented that the density functional theory is built on the basis of two fundamental mathematical theorems proved by Kohn and Hohenberg and the derivation of a set of equa-

tions by Kohn and Sham in the mid-1960s [161–166]. The first one is that the ground-state energy from Schrödinger’s equation is a unique functional of the electron density, which states that there exists a one-to-one mapping between the ground-state wave function and the ground-state electron density [157,161]. The second one is that the electron density that minimizes the energy of the overall functional is the true electron density corresponding to the full solution of the Schrödinger equation [161,163]. Thus, the energy functional can be expressed as follows: i }]

E[{

= E known [{

i }] + EXC [{ i }]

And the E known [{ E known [{ e2 + 2

 

i }] =

h2



m

i }]

is:

 ∗ 2 ∇ i

3 id r +

V (r)n(r)d3 r

i

n(r)n(r  )

  d3 rd3 r  + Eion  r r 

in which the terms on the right are the electron kinetic energy, the Coulomb interactions between the electrons and the nuclei, the Coulomb interactions between pairs of electrons and the

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Fig. 11. (a) The color change of anatase TiO2 powder from white to blue by introducing hydrogen-free oxygen vacancies, and from white to red by introducing atomic hydrogen-mediated oxygen vacancies. (b) ESR spectra of OVH -TiO2 and OV-TiO2 measured at 130 K under dark. Reproduced with permission [147]. Copyright 2018, John Wiley & Sons, Inc. (c–d) Schematic illustration of the crystal orientation of the BiOCl single-crystalline nanosheet with {001} facets exposed and the corresponding EPR spectra of the as-prepared BiOCl. Reproduced with permission [148]. Copyright 2014, Royal Society of Chemistry. (e–f) HAADF-STEM images of the VZn -rich one-unit-cell ZnIn2 S4 (ZIS) layers obtained at 200 ◦ C and the EPR spectra of VZn -rich and VZn -poor one-unit-cell ZIS layers. Reproduced with permission [59]. Copyright 2017, American Chemical Society.

Fig. 12. (a) Magnified HAADF-STEM image of Co4 N nanosheets. (b) In situ DRIFT spectra of Co and Co4 N nanosheets after the treatment with H2 at 30 ◦ C for 30 min. Reproduced with permission [149]. Copyright 2017, Springer Nature. (c) Positron lifetime spectrum of ultrathin BiOCl nanosheets and BiOCl nanoplates, respectively. (d–e) Schematic representations of trapped positrons of V  Bi defect and V  Bi VO•• V  Bi vacancy associates, respectively. Reproduced with permission [151]. Copyright 2013, American Chemical Society.

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Coulomb interaction between pairs of nuclei. And the EXC [{ i }] is the exchange-correlation functional, which includes all the quantum mechanical effects that are not included in the “known” terms. By choosing appropriate exchange-correlation functionals, many important properties including defect energies, diffusion barrier, phonon modes, elastic tensor, adsorption energy, reaction path and so on, can be determined through the DFT-based total energy calculations with acceptable accuracy [165,167]. As for the defectfeatured electrocatalysts, the modified electron density after the introduction of multiple dimensional imperfections into the crystal lattice holds the magic code behind the enhanced electrocatalytic performance, which can be conceptually decrypted by the DFTbased calculations. The surface-based heterogeneous catalytic process mainly involves three elementary steps: adsorption of the target reactant, catalytic process and the desorption of the final products [110,168]. Thus the adsorption energy of the target reactant and the energy barrier of the rate-determining step (RDS) are the main descriptors for the rational design of electrocatalysts with highly desired performances [11,99,158,169–172]. Recently, the catalytic performance of a series of single metal atoms supported on graphitic carbon nitride (g-C3 N4 ) for NRR was investigated by comparing their N2 adsorption energy and the energy barrier of the RDS [173]. As concluded by the authors that the single tungsten (W) atom anchored on g-C3 N4 (W@g-C3 N4 ) shows the highest electrocatalytic activity toward NRR with a limiting potential of −0.35 V via associative enzymatic pathway. The out-performed activity of the W@g-C3 N4 can be ascribed to the significant positive charge and large spin moment on the W atom, excellent electrical conductivity, and moderate adsorption strength with NRR intermediates, as indicated by the DFT-based calculations (Fig. 13a-b). Furthermore, Jiang [174] et al. have proved that the double atomic catalyst (DAC) is more superior in the NRR electrocatalytic process than the single atomic catalyst (SAC) due to the optimized adsorption energy and lower energy barrier for the RDS.

boosted ORR performance of the J-PtNWs to the increased number of the undercoordinated surface atoms, stressed surface due to the unique structure and the higher number of the ORR-favorable rhombic structures on the surface. In addition to the application of MD in the ORR process, MD-based simulations can also be applicable to the ECR process. Zeng [176] et al. conducted molecular dynamics simulation and experimental investigations and demonstrated the surface-strain dependence of catalytic activity in the electrochemical reduction of CO2 (Fig. 13d). Monte carlo method A Monte Carlo simulation generates configurations of a system by making random changes to the positions of the atoms present, together with their orientations and conformations where appropriate. The sampling method (importance sampling) used in the simulation is able to generate states of low energy, as this enables properties to be calculated properly [177]. The potential energy of each configuration of the system, as well as the values of other properties, can be calculated from the positions of the atoms. Unlike the molecular dynamics simulation, there is no momentum contribution in the Monte Carlo simulations, which samples from a 3N-dimensional space of the positions of the particles. All the deviations from ideal gas behavior of the system can be ascribed to the presence of interactions between the system, as can be calculated using the potential energy function [177]. This energy function is dependent only on the positions of the atoms rather than their momenta, giving the Monte Carlo simulation the ability to calculate the excess contributions that give rise to the deviations from ideal gas behavior, thus the energy of the whole system. However, the Monte Carlo method is often used in the kinetic (defect diffusion, etc.) investigations of the defects [178–188], and rare research has been reported in the area electrocatalysis, which makes it an attractive subject for the theorists. Other analysis techniques

Molecular dynamics Molecular dynamics is a computational simulation technique that the time evolution of a set of interacting atoms is followed by integrating their equations of motions based on the classical Newton’s law: F i = mi ai Where mi is the atom mass, ai is the acceleration and Fi is the force acting upon it. By giving the system an initial set of positions and velocities, the subsequent time evolution is theoretically determined. According to some statistical distribution functions, a trajectory in a 6N-dimensional phase space (3N positions and 3N momenta) can be accurately calculated, which means the energy that can be derived from the nuclear coordinates is readily known. Furthermore, compared with the aforementioned DFT-based calculations, the MD-based calculations can solve problems on a much bigger scale (<1000 atoms for DFT vs. > 100,000 atoms for MD) [165]. Thus, by applying appropriate force-field, the MD-based simulations can give us a more informational description of the defect-featured electrocatalysts on a much larger scale. Duan [175] et al. prepared ultrafine jagged Pt nanowires (J-PtNWs) that exhibited excellent electrocatalytic ORR performance with a high electrochemically active surface area (ECSA) of 118 m2 /g, a specific activity of 11.5 mA cm−2 (@0.9 V vs. RHE) and a mass activity of 13.6 A mg-1 via an electrochemical dealloying method. Reactive molecular dynamics (RMD) studies using the reactive force field (ReaxFF) were conducted to gain further insight into the enhanced ORR performance (Fig. 13c). Combined by the theoretical and experimental investigations, the authors ascribe the

In addition to the aforementioned advanced characterization methods, the commonly used UV–vis spectra [189,190] (nanomaterials with various defects often have different optical properties and distinct bandgaps, which can be deduced from the UV–vis spectra), Raman [69,191–193] (especially useful for the defect level identification of the carbon-based nanomaterials for the distinguished ID /IG ratios), photoluminescence spectra [194,195] (defects in the nanomaterials often act as carrier recombination sites during the transport process of the carriers, as a result of which, different PL spectra should be observed), XRD (doping with heteroatoms can induce lattice mismatch into the nanocrystals, leading to the small shift of the XRD peaks) could also be powerful tools for studying defects in the electrocatalysts. Defect engineering in electrocatalytic ORR, ECR, and NRR For addressing some of the most pressing social and environmental challenges, varying from the reduction of fossil fuel emission to the production of renewable energies, electrocatalysis plays an impressively important role [23,196,197]. The role necessitates the design and synthesis of highly active, stable, yet low-cost electrocatalysts. Due to the arresting change in the electronic structure after the introduction of defects into nanomaterials, catalyst would have distinct catalytic performances, which have been observed in the oxygen reduction reaction (ORR) [85,104,198–202], electrochemical CO2 reduction (ECR) [9,117,203–209], nitrogen reduction reaction (NRR) [209–213] and other electrocatalysis processes [105,106,214–219].

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Fig. 13. (a) The computed adsorption energies (Eads , eV) of N2 molecule and (b) the Gibbs free energy changes (G) of the potential determining step of NRR on various single metal atoms supported on the g-C3 N4 surface. Reproduced with permission [173]. Copyright 2018, John Wiley & Sons, Inc. (c) Structural analysis of the J-PtNWs obtained from ReaxFF reactive molecular dynamics. Reproduced with permission [175]. Copyright 2016, American Association for the Advancement of Science. (d) Surface strain fields of a Pd octahedron and icosahedron. The color indicates strain labeled in the color map. Reproduced with permission [176]. Copyright 2017, John Wiley & Sons, Inc.

Defects in oxygen reduction reaction (ORR) Among various kinds of energy conversion technologies, fuel cells, in which the chemical energy of the hydrogen can be converted to electricity via the electrochemical reactions, are one of the most promising devices for their high Faradaic efficiency and zero pollutant emission [85,220–225]. However, the sluggish reaction kinetics of the ORR at the cathodic side, which results in the high overpotential, have severely limited the overall efficiency of the fuel cells [198,226–232]. To mitigate the overpotential issues, the search for effective strategies to obtain low-cost electrocatalysts with high activity and durability has been critical. As it has been proved that the ORR process involves multiple reaction steps and can proceed by either a one-step four-electron (4e− ) path, which converts O2 into H2 O (O2 + 4H+ + 4e− = 2H2 O) or a two-step two-electron (2e− ) path with the production of H2 O2 [11]. Apparently, a higher selectivity toward the 4e− pathway is more efficient to catalyze the ORR and the selectivity of the ORR process towards the production of H2 O2 or H2 O is determined by the propensity of the electrocatalysts to break the O O bond, which is directly influenced by the electron distribution of the surface atoms [233]. Here, the defect engineering offers a simple but effective way for the optimization of the electron distribution around the defect sites to regulate the binding energy of the adsorbed intermediates and thus the performance of the ORR catalyst [199,234–237]. In addition to the direct participation in the electrocatalysis process, the defects that exist in the nanostructures can also serve as trapping sites to capture and stabilize the heteroatoms to form defect-based motifs with diverse coordination environments for stable and durable ORR performances [84,134,238]. In the following, we give a brief overview of the recent investigations on the role that the defect plays in the ORR process based on the categories of defects. Inspired by the fact that the electronic properties of the carbon can be modulated through traditional heteroatom doping to enhance the ORR activity [104,223,229,230,232,240, 240,241,242,243,244,245,246,247,248,249,250,251,252,253,254], Yao [233] et al. induced structural defects on 3D hierarchical porous carbon aerogels to generate architectures with atomic S C defects and N S C defects as active sites and investigated its ORR

performance in both acidic and alkaline solutions. As shown by Fig. 14a, the designed electrocatalyst exhibited enhanced ORR properties than the commercial Pt/C in both acidic and alkaline electrolyte. Density functional theory (DFT) based results indicated that the introduction of heteroatoms (S and N atoms) caused the redistribution of the electrons, which transferred 0.04 |e| through S-doping in the graphene, 0.09 |e| in the pentagon defect in S G and more electrons in the N-S-d-G (Fig. 14b). Together with the narrowed bandgap (Fig. 14c), the N-S-d-G showed the lowest G and the best ORR performance due to the appearance of the new active sites reconstructed from the edged thiophene S, graphitic N, and pentagon defects. Despite the experimental-derived design of defect-rich electrocatalysts, on the grounding of in-depth understanding of the defect-optimized electron contribution effect, the theoreticalguided rational manufacturing of efficient electrocatalysts has proven itself a potential alternative for the preparation of electrocatalysts for specific reactions [255–257]. Recently, by closely coupled computational design and experimental development, Guo [239] et al. have achieved the highly effective bifunctionality in the phosphorus and nitrogen co-doped graphene framework (PNGF). Given the fact the P atom has a relatively large atom radius and high electron-donating property, on the basis of the structure search, several specific configurations with P doping in the graphene were selected for further analysis. As it is clear that the density of states (DOS) at the valence band maximum (VBM) and the conduction band minimum (CBM) are associated with electron-donating and accepting mechanisms, which can be utilized for the selection of potential active sites. The P sites in the Z-PN-5-OX2 showed the highest DOS just below the Fermi level, making it the most active to donate electrons to O2 (Fig. 14d). Based on the theoretical results, experiments were carried out for the synthesis of the target electrocatalysts with the similar atom arrangement, which showed both ORR and OER activities reaching the theoretical limits of metalfree catalysts, superior to their noble metal counterparts in both (bi)functionality and durability (Fig. 14e). With the ability to maximize utilization of the active metal sites, the single-atom catalysts (SAC) are capable of exhibiting ultra-high activity and even selectivity toward diverse reactions, comparable to their homogeneous counterparts [261,262]. Con-

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Fig. 14. (a) LSV curves of NSCA-700, NSCA-700-1000, NSCA-700-1100, and 20 % Pt/C at 1600 rpm in 0.5 M H2 SO4 solution, 0.1 M HClO4 solution and 0.1 M KOH solution. (b) The optimized structures of S-G, S-d-G, and N-S-d-G model 1 (Gray, white, yellow, and claret balls represent C, H, S, and N atoms, respectively; the blue atoms represent S- or N-adjacent C atoms.), and the corresponding electron density. The blue and red regions indicate electron-donating and electron-withdrawing areas, respectively. (c) The calculated density of states (DOS) of N-S-d-G, S-d-G, and S-G (the Fermi level is set at zero energy). Reproduced with permission [233]. Copyright 2018, Elsevier. (d) The PDOS for the Z-PN-5-OX2 structure. (e) ORR and OER activities of PNGF DAP, PNGF ADP, PNGF ADP (op), and PNGF (op), measured by rotating disk electrode at 1600 RPM. Reproduced with permission [239]. Copyright 2017, Royal Society of Chemistry.

struction of the single-atom dispersed nanostructures allows bridging the gap between homogeneous and heterogeneous catalysts, providing a good platform for the understanding of the relationship between the structure and activity on the atomic scale [81,84,128,134,138,236,238,258,260,261,263–273]. Li [258] et al. have prepared isolated single Fe atoms anchored on N-doped porous carbon via the high-temperature pyrolysis of the Fe precursor capped in the ZIF-8, which exhibited excellent ORR performance in terms of high half-wave potential (0.900 V) and high kinetic current density (37.83 mA cm−2 at 0.85 V) (Fig. 15a). The outperformed ORR properties compared with its nanoparticle-anchored counterpart originated from the easier electron transfer from Fe single atoms to the adsorbed *OH species (Fig. 15b), leading to smaller energy barrier for the rate-determining step. Further, by controlling the coordination environment of the central metal atoms, the ORR performance of the SACs can be readily boosted. Here, by coordinating the central Fe single atom with Cl in the N-doped carbon, Li [259] et al. have achieved a great improvement of the ORR performance with a half-wave potential of 0.921 V and a kinetic current density of 41.11 mA cm−2 at 0.85 V (Fig. 15c). DFT calculations demonstrated that the electronic structure can be adjusted by contemporaneously modulating the near-range interaction with

chlorine and long-range interaction with sulfur of the Fe active sites, leading to favorable O2 binding energy and enhanced ORR behaviors (Fig. 15d). In addition, Guo [260] et al. have designed a sulfur-doped Fe/N/C catalyst by a template-sacrificing method, in which the incorporated sulfur sites gave a thiophene-like structure (C-S-C) (Fig. 15e). The sulfur-doped sample showed better ORR performance with more positive half-wave potential and larger kinetic current density, which is believed to result from the electron localization around the Fe center originating from the long-range interaction with the sulfur atom (Fig. 15f). The improved interaction with the oxygenated species owing to the larger availability of electron states around the Fermi level facilitated the 4e- ORR process, leading to the excellent ORR property. In addition to the dopant, another kind of point defect, namely the vacancy, also has an appreciable influence on the electron distribution by removing one or more atoms from the perfect lattice [30,237,274,275]. By simply freeze-drying and carbonization procedures with the assistance of NaCl template, Wen [30] et al. synthesized ␤-FeOOH/PNGNs, in which the FeOOH was homogeneously anchored on the surface of porous graphene nanosheets. The ␤-FeOOH/PNGNs catalyst exhibited remarkable ORR performances in all-pH medium with superior E1/2 (half-wave potential)

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Fig. 15. (a) HAADF-STEM images and enlarged images of the Fe-ISAs/CN and the ORR polarization curves. (b) ORR polarization curves of the as-prepared and acid-leached Fe-ISAs and NPs/CN and the free-energy paths of ORR on Fe-ISAs/CN and Fe-NPs/CN. Reproduced with permission [258]. Copyright 2017, John Wiley & Sons, Inc. (c) AC HAADF-STEM image of FeCl1N4/CNS and the ORR polarization curves in O2 -saturated 0.1 M KOH. (d) Free energy pathways of ORR processes on FeCl1N4/CNS under standard conditions (pH = 1, T =298 K and P = 1 bar) and the correlation of the ORR overpotentials with the O2 binding energies for the Fe catalysts. Reproduced with permission [259]. Copyright 2018, Royal Society of Chemistry. (e) The TEM image of Fe/SNC and the HAADF STEM images of Fe/SNC. (f) ORR polarization curves of the as-synthesized catalysts in O2 -saturated 0.5 M H2 SO4 solution at a scan rate of 10 mVs−1 and the free energy diagram for the Fe/NC and Fe/SNC systems during the ORR under acidic conditions at an equilibrium potential of U0 = 1.23 V. Reproduced with permission [260]. Copyright 2017, John Wiley & Sons, Inc.

compared with the state-of-art Pt/C catalyst in 0.1 M KOH and comparable performances in the neutral and acid media (Fig. 16a). Based on the XPS and EXAFS results, which indicated the decreased Fe-Fe intensity of ␤-FeOOH/PNGNs, the authors argued the existence of the Fe vacancies was the origin of the ORR performance enhancement (Fig. 16b). Further DFT-based calculations, which indicated the formation of the six coordinated Fe octahedron sites (FeO6 ) with an optimized energy barrier, validated the argument (Fig. 16c). Via a general solution-based method, Han [237] et al. synthesized a class of multicomponent anisotropic structure (AS) with a high density of vacancies, as indicated by the HAADF-STEM images (Fig. 16d). Accompanied by the formation of vacancies, the surface charge density at the site of Co was changed. Also, the introduction of Co atoms into the PdCu-AS can also increase the electronic polarization of the PdCu-AS, which helped to overcome the reaction barrier of the limiting steps of ORR, resulting in better ORR performance (Fig. 16e). Despite the doping-induced vacancy effect, the involvement of vacancies into the nanostructure can also induce the strain effect due to the mismatch of the lattice, which can facilitate the optimization of the d-band center positions and adsorption energy of the intermediate [10,26,99,276,277]. As can be observed in Fig. 16f, the adsorption energy for O2 increased with the change of lattice, which again demonstrated that the vacancy structure can enhance the performance of ORR. Except for the point defect induced electron redistribution, the amorphous phase with abundant dangling bonds that can act as active centers has also been proved as an effective way for the optimization of ORR electrocatalysts. Recently, Viswanathan [92] et al.

proposed that Ni3 S2 underwent a self-limiting oxidative surface reconstruction process under ORR conditions, forming an amorphous surface film with a thickness of ∼1.6 nm that coated on the Ni3 S2 crystallites (Fig. 17a). As proved by the EDS, the Ni:S ration varied along the direction that perpendicular to the amorphous shell surface with a Ni:S = 1:1 at the very surface of the amorphous layer (Fig. 17a). Further ORR activity test indicated that the amorphous layer with nominal NiS stoichiometry exhibited high activity, similar to that of Ni3 S2 upon surface transformation (Fig. 17b). To identify the relation between the ORR activity and the structure, DFT-based calculations revealed the scaling between adsorption free energies of OH* and OOH* on the various stable Ni-S phases, indicating that the activity was governed by the free energy of one reaction intermediate, namely OH* (Fig. 17c). Based on these results, the authors argued that a structure-energy descriptor relation was responsible for the ORR activity, which suggested that having 3 sulfur atoms bonded to the active site led to high ORR activity in this situation (Fig. 17d). As it has been well documented that the interface between the heterogeneous components promises strong interfacial chemical and electronic interactions by electron transfer, which can potentially benefit the ORR process [235,279]. Qiao [278] et al. reported the atomic and electronic coupling of Pt nanoparticles with pyramidal nanofacets enclosed single-crystal (SC) CoO nanorods on the on a conductive carbon fiber paper (CFP) substrate. The HRTEM images clearly indicated the perfect interface between the Pt NPs and the {111} nanofacets of SC CoO NR, which showed the epitaxial growth of Pt NPs on SC CoO NRs (Fig. 17e). Derived from the unique interface, DFT-based calculation confirmed the apparent electron

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Fig. 16. (a) Electrochemical ORR measurements. LSV curves obtained in 0.1 M KOH, 0.1 M PBS, and 0.5 M H2 SO4 . (b) The Fe K edge Fourier transforms (FT) curves of EXAFS of the samples. (c) The free energy variations for Fe-perf, Fe-vac, Fe-perf-G, and Fe-vac-G during the oxygen reduction processes. Reproduced with permission [30]. Copyright 2018, John Wiley & Sons, Inc. (d) HRTEM images projected along the zone axes of [110] axis and the corresponding bright-field image of a dendritic structure. (e) The ORR polarization curves were recorded in O2 -saturated 0.1 m HClO4 and the inset was the corresponding mass activity Tafel plots for the three catalysts, respectively. (f) The deformation charge density of the vacancy in v-PdCuCo-AS and O2 adsorbed v-PdCuCo-AS, and the adsorption energies of O2 on strained v-PdCuCo-AS species. (blue area: charge accumulation and red area: charge depletion). Reproduced with permission [237]. Copyright 2018, John Wiley & Sons, Inc.

transfer from CoO to Pt with a charge accumulation of 0.18e. Further analysis of the DOS indicated the downshift of the d-band center of Pt from -2.36 to -2.46 eV, which led to the optimal adsorption of intermediates, making it preferable for catalytic ORR reaction (Fig. 17f), as proved by the ORR test (Fig. 17g). To better understand the defect-featured catalyst for ORR, we have summarized representative electrocatalysts and their main catalytic performance in Table 1.

Defects in electrochemical CO2 reduction (ECR) As the most significant greenhouse gas, the rapid increase of the carbon dioxide (CO2 ) concentration in the atmosphere has given rise to numerous environmental issues, including but not limited to global warming, ocean acidification, rising sea levels [91,117,281]. Converting CO2 into fuels and chemicals via the electrochemical reactions is a promising strategy to mitigate energy shortage and to lower the global carbon footprint [9,282]. Unlike the aforementioned ORR process, the ECR catalytic process is quite complex and is related with multiple electron transfer for both C1 and C2+ (multi-carbon products) products (Table 2) [94,283,284]. Generally,

the cathodic reaction in the electrochemical CO2 reduction can be expressed as: xCO2 + nH + + ne− → Product + yH2 O Compared with the C1 products, the generation of C2+ products with higher energy density and more economical values has received intensive attention [94,285]. However, the complicated catalysis process, distinct catalysis pathways under different conditions, poor selectivity, lack of long-term stability, all of which are related with the electron distributions in the catalysts, have restricted practical use and technological commercialization [13,286,287]. As for the various heterogeneous processes, the relative metal oxygen and hydrogen affinities have played important roles in controlling the electrocatalysts’ activity and selectivity by influencing the binding strength of specific reaction intermediates on their surface [27,257], which is also applicable to the ECR process. Based on the relative strength of the O and H affinity, the metal electrodes are classified into four groups with different main products [170], giving us a clue for the reduction of CO2 into multicarbon chemicals by changing the electron distribution of the active sites. The defect engineering in tuning the electronic structures of

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Fig. 17. (a) TEM images of the post-electrolysis Ni3 S2 nanoparticles and the Elemental composition determined from energy-dispersive X-ray spectroscopy (EDS) at a series of spots along a line from the crystallite edge to the bulk. (b) Tafel plots of Ni3 S2 (black), as-prepared NiS (blue), and annealed NiS (red). (c) ORR activity volcano of nickel sulfide phases showing the expected limiting potential (bold line) and the limiting potential (dashed line) obtained from a thermodynamic analysis, as a function of the DFT-calculated adsorption free energy of the intermediate OH*. (d) Structure-activity contour plot of the expected limiting potential, UEL , based on the structure descriptor, GOH = 0.29(0.08CN(Ni) + CN(S) ), from the coordination numbers of the nearest neighbor nickel and sulfur atoms. Reproduced with permission [92]. Copyright 2017, Elsevier. (e) The HRTEM image of the atomically perfect interface for Pt NP/SC CoO NRs. (f) The projected density of states (PDOS) onto Pt d orbitals of Pt (111) (top) and Pt (111)/CoO and the d-band center of a surface Pt atom on the (111) surface of Pt and Pt/CoO. (g) Linear sweep voltammograms of Pt NPs/SC CoO NRs hybrid catalysts. Reproduced with permission [278]. Copyright 2017, John Wiley & Sons, Inc.

the catalysts has been pushed forward, in which nanomaterials with different kinds of defects have been utilized in the electrochemical CO2 reduction [27,286,288,289]. As the most commonly used method for the optimization of electrocatalysts, doping-induced alteration of the electronic structures and the optimized adsorption energy of the intermediates has been proven as the origin of the enhancement of the ECR catalysts [117,204,205,208,209,290–293]. By doping the electron-deficient boron atoms into the copper lattice, electrons will transfer from Cu atoms to the boron atoms, leading to the positively charged Cu sites, as argued by Sargent [118] et al., which will finally influence the adsorption energy of the CO on the Cu surface (Fig. 18a). The CO CO dimerization energy that governed the rate-limiting step for the C2+ products could be a function of the average CO adsorption energy, which was in a volcano shape, making it possible for the ECR selectivity to be adjusted by changing the oxidation state of the copper (Fig. 18b). Furthermore, under the circumstance that the optimal CO adsorption energy was achieved, a larger difference in the adsorption energies of these two CO molecules would further enhance the CO dimerization (Fig. 18c). Inspired by the electron distribution enhanced CO dimerization, the boron doped copper with different dopant concentrations was prepared and

the XANES results revealed the relationship between the oxidation degree and the binding energy with higher oxidation state showing larger energy shift (Fig. 18d). The results indicated that with the optimal oxidation state, the Cu(B)-2 sample exhibited much higher Faradaic efficiency and C2 partial current density (Fig. 18e), validating the electron distribution induced optimization of the ECR electrocatalysts. Moreover, by doping two kinds of elements into the electrocatalysts, good C2 product selectivity can be further achieved. By doping B and N atoms into the nanodiamond (BND), Quan [209] et al. reported good ethanol selectivity with a high Faradaic efficiency of 93.2 % at 1.0 V vs. RHE (Fig. 18f). Both experimental and theoretical results indicated that the enhanced CO2 reduction activity and high selectivity originated from the synergetic effects of boron and nitrogen co-doping. As depicted in Fig. 18g, the doped B enhanced the CO2 capture by forming B–O bond and H preferential adsorption at doped N, which facilitated the *H transfer in the elementary reactions involving hydrogenation, contributed to the superior catalytic performance. Except for doping with B or N, elementary doping with F [290], O [291], Ni [292], etc. can also help to improve the ECR properties. Similarly to the application in ORR, SACs have also been applied in the ECR by rational regulation of the electron distribution

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Table 1 Summary of the performance of representative defect-featured nanocatalysts for ORR. Catalyst

Half-wave potential

Mass Activity (mA/mgcat. )

Kinetic Current Density (Jk , mA cm−2 )

Ref.

N-modified S-defect carbon Pt/d-Ti0.9 Mo0.1 Oy BClCNTs FeBNC Fe − N-Doped Carbon N-HCNS Fe0.3 Co0.7 /NC Fe–N–C/VA-CNT Mn/C-NO Pd-B Fe/N/C-SCN Co3 (PO4 )2 C-N/rGOA Rh-doped Pt NWs/C Ga − PtNi/C Co-N-mC Fe/N/C Fe-N-C-950 FeSAs/PTF-600 Ru/nitrogen doped GO Pt1 @Fe-N-C Fe-ISA/SNC Co-NC-1100 ISA Fe/NC FeNP -N-C Co SAs/N-C SA-Fe-HPC Cu-N-C ␤-FeOOH/PNGNs v-PdCuCo-AS

0.760 V in 0.1 M HClO4 ;0.850 V in 0.1 M KOH 0.840 V in 0.5 M H2 SO4 0.840 V in 0.1 M KOH 0.838 V in 0.1 M KOH 0.830 V in 0.1 M KOH 0.872 V in 0.1 M KOH 0.880 V in 0.1 M KOH 0.790 V in 0.5 M H2 SO4 0.860 V in 0.1 M KOH 0.860 V in 0.1 M KOH 0.8360 V in 0.1 M H2 SO4 0.837 V in 1 M KOH – – 0.851 V in 0.1 M KOH 0.930 V in 0.1 M KOH 0.920 V in 0.1 M HClO4 0.870 V in 0.1 M KOH 0.750 V in 0.1 M HClO4 0.800 V in 0.5 M H2 SO4 0.896 V in 0.1 M KOH 0.800 V in 0.5 M H2 SO4 0.900 V in 0.1 M KOH 0.889 V in 0.1 M KOH 0.881 V in 0.1 M KOH 0.890 V in 0.1 M KOH 0.869 V in 0.1 M KOH 0.883 V in 0.1 M KOH 0.915 V in 0.1 M HClO4

– 5.03 @ 0.90 V vs. RHE – – – – – 7.0 @ 0.80 V vs. RHE – 2380 @ 0.85 V vs. RHE 23 @0.80 V vs. RHE – 1410 @ 0.90 V vs. RHE 1240 @ 0.9 V vs. RHE – – – – ∼540 @ 0.7 V vs. RHE – – – – – – – 36.9 @ 0.85 V vs. RHE – 180 @ 0.9 V vs. RHE

– –

[233] [241] [222] [227] [223] [199] [220] [245] [229] [225] [230] [250] [104] [253] [200] [263] [264] [238] [236] [265] [267] [268] [258] [84] [280] [270] [108] [30] [237]

Table 2 Several representative half-reactions and reduction potential of CO2 -reduction reactions in aqueous solutions. E0 / V vs. RHE

Products

Reaction

O2 (Oxygen) H2 (Hydrogen) C1 products (formic acid, carbon monoxide, formaldehyde, graphite, methanol, methane)

2H2 O → O2 + 4H + 4e 2H+ + 2e− = H2(g) CO2 + 2H+ + 2e− → HCOOH(aq) CO2 + 2H+ + 2e− → CO(g) + H2 O CO2(g) + 4H+ + 2e− → HCHO(aq) + H2 O(aq) CO2 + 4H+ + 4e− = C(s) + 2H2 O CO2 + 6H+ + 6e− = CH3 OH(aq) + H2 O CO2 + 8H+ + 8e− = CH4(g) + 2H2 O 2CO2 + 2H+ + 2e− = C2 H2 O4(s) 2CO2 + 8H+ + 8e− = CH3 COOH(aq) + 2H2 O 2CO2 + 10H+ + 10e− = CH3 CHO(aq) + 3H2 O 2CO2 + 12H+ + 12e− = C2 H5 OH(aq) + 3H2 O 2CO2 + 12H+ + 12e− = C2 H4 + 4H2 O 2CO2 + 14H+ + 14e− = C2 H6(g) + 4H2 O 3CO2 + 16H+ + 16e− = C2 H5 CHO(aq) + 5H2 O 3CO2 + 18H+ + 18e− = C3 H7 OH(aq) + 5H2 O

C2 products (Oxalic acid, acetic acid, acetaldehyde, ethanol, ethylene, ethane) C3 products (Propionaldehyde and propanol)

+

−

1.23 0.00 −0.12 −0.10 −0.07 0.21 0.03 0.17 −0.47 0.11 0.06 0.09 0.08 0.14 0.09 0.10

[80,116,169,203,206,288,294]. Recently, Zhang [288] et al. reported atomically dispersed nickel on nitrogenated graphene as an efficient and durable electrocatalyst for CO2 reduction. With the evidence from XAS, XPS, and EPR, the monovalent Ni(I) atomic center with a 3d9 , S = 1/2 electronic configuration was identified, which was considered as the catalytically active sites (Fig. 19a). The atomically dispersed Ni catalyst exhibited high intrinsic CO2 activity (350 A gcatalyst −1 ) and a TOF of 14,800 h−1 at 0.61 V for CO conversion with 97 % Faradaic efficiency and long-term stability (Fig. 19b). The operando X-ray absorption spectroscopy and photoelectron spectroscopy revealed that the Ni (I) active sites showed higher oxidation state in CO2 -saturated electrolyte due to the delocalization of the unpaired electron in the 3dx2 −y2 orbital and spontaneous charge transfer from Ni(I) to the carbon 2p orbital in CO2 to form a CO2 ı- species (Fig. 19c). And the change of the

23.6 18.3 5.68 – – – 4.13 – 5.58 – – – – 14.8 – – – – – 37.83 18.43 -∼22 3.72 11.84 ∼48 0.252

valence band edge dramatically reduced the Ni 3d DOS, again proving the charge transfer from the 3dx2 −y2 orbital of Ni (I) to the C 2 u orbital that was also consistent with the DFT-based calculations (Fig. 19d). Except for participating in the ECR process directly, the single atoms on the substrate can also help with the optimization of the nanostructures, which enables the activation of the CO2 , leading to enhanced ECR performance [169,294,295]. Guided by the DFT calculations, Zheng [206] et al. found that introduction of copper atoms into the CeO2 substrate can stably enrich up to three oxygen vacancies around each Cu site, which can activate the CO2 into CO2 ·- by optimizing the adsorption energy of the CO2 molecules on the surface (Fig. 19e). Inspired by the theoretical results, single copper dispersed CeO2 was successfully prepared and tested for the ECR (Fig. 19f). The authors attributed the excellent CH4 selectivity to both the atomic dispersion of the Cu site and the surrounding multiple oxygen vacancies, as well as the cooperative effective from the CeO2 framework (Fig. 19g). As expected, the other important point defect (vacancy) can also find its place in boosting the ECR performance by modulating the electronic structures of the electrocatalysts [297,298]. Focusing on the post-C–C coupling process, Sargent [256] et al. argued that generation of the Cu vacancies on the Cu2 S core can potentially shift the balance of the product in favor of ethanol by suppressing ethylene production through increasing the energy barrier toward the ethylene path (Fig. 20a). Based on the theoretical results, Cu2 S–Cu core-shell nanostructure with Cu vacancies on the very surface was successfully obtained with both Cu◦ and Cu+ features. With the shifted reaction pathway by altering the adsorption energy of the pivotal intermediate, the Cu2 S–Cu-V exhibited excellent ECR performance in both the traditional H-cell and the flow-cell, with a C2+ alcohol production rate of 126 ± 5 mA cm−2 with a selectivity of 32 ± 1 % Faradaic efficiency (Fig. 20b-c). Furthermore, the interface can also prove its ability in enhancing the ECR performance of the electrocatalysts by its potential in

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Fig. 18. (a) The CO adsorption energy (Ead ) is monotonically increased as the partial positive oxidation state of copper is increased. (b) The CO = CO dimerization energy as a function of the average adsorption energy of two adsorbed CO molecules. (c) When the ‘optimal’ average adsorption energy of CO is ∼0.8–1.0 eV, a larger difference in the Ead values of two CO molecules further enhances CO = CO dimerization. (d) The average oxidation state of copper in Cu(B) with different contents of boron obtained from copper K-edge XANES, suggesting that the oxidation states of copper in Cu(B) samples are tunable. (e), Faradaic efficiency of C2 and C1 at different copper oxidation states on Cu(B) at the potential of –1.1 V versus RHE; Conversion efficiency of reacted CO2 to C2 and C1 products at different potentials on Cu(B)-2 and the partial current density of C2 at different potentials on Cu(B)-2, Cu(C) and Cu(H). Reproduced with permission [118]. Copyright 2018, Springer Nature. (f) Production rates of the CH3 CH2 OH, CH3 OH, and HCOO− and corresponding Faradaic efficiencies on BND3. (g) Free energy diagrams for CO2 reduction on (111) facet of BND. Reproduced with permission [209]. Copyright 2017, John Wiley & Sons, Inc.

changing the electron distributions at the interface between the substrate the metal [170,299,300]. Herein, Cu/CeO2-x heterodimers (HDs) were synthesized, which exhibited the state-of-art selectivity toward ECR (up to ∼80 %) against the competitive hydrogen evolution reaction (HER) and high faradaic efficiency for methane (up to ∼54 %) at −1.2 VRHE (Fig. 20d), outperforming the interfacefree counterparts, as reported by Buonsanti [296] et al. As revealed by the DFT-based calculations, the Cu d-band shifted to lower energy away from the Fermi level and significant relocation of the Cu-electrons into the Ce atoms can be observed, which stabilized ECR intermediates, leading to enhanced electrocatalytic performance (Fig. 20e). Bulk defects, such as grain boundaries (GBs), can create strained regions in polycrystalline materials via stabilizing dislocations, which offers us a potential method for the preparation of high-energy surfaces for various electrocatalysis [114,302–305]. Recently, combined with bulk electrochemical measurements and scanning electrochemical cell microscopy with the sub-micrometer resolution, Kanan [207] et al. gave us the direct observation that GBs in the Au electrode achieved enhanced performance for the electrochemical conversion from CO2 to CO than the grain surfaces (Fig. 21a). As depicted in Fig. 21b, an increase of jCO with the total GB density of the electrode was observed, providing an alternative

method for the exploration of electrocatalysts. More detailed investigation around the GBs indicated that the two grains displayed similar currents, but the GB region showed a peak that was 2–2.5 times as large as the neighboring grains, indicating the stronger activity of the GBs than the grain surfaces (Fig. 21c). Also, the activity of the grains boundaries was heavily dependent on the geometry for the fact that the geometry determined the energy and local structure of the GBs, which is vital for the electrocatalytic processes (Fig. 21d). Benefited from large numbers of low-coordinated atoms and the abundance of defects, which can act as catalytic centers, the amorphous electrocatalysts are expected for enhancing the electrochemical performance of the electrocatalysts. By using a tender reduction agent, Jiang [301] et al. proposed a facile and effective protocol for amorphizing Cu nanoparticles, which exhibited superior electrochemical performances (Fig. 21e). The amorphous Cu nanoparticles achieved long-term stability (∼ 12 h), high catalytic activity and selectivity, namely, total Faradic efficiency of 50 % at 1.4 V vs. Ag/AgCl with formic acid and ethanol account for 37 % and 22 % (Fig. 21f). A summary of the representative defect-featured electrocatalysts and their main product, along with their Faradaic efficiency are listed in Table 3.

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Fig. 19. (a) Ni K-edge XANES spectra of A-Ni-NG, A-Ni-NSG, and NiPc, where peak A represents 1s → 3d transition, peak B represents 1s → 4pz transition, C and D represent 1s → 4px,y transitions, and multiple scattering processes, respectively. And the EPR spectra of A-Ni-NG measured at room temperature (RT) and 77 K. (b) CO Faradaic efficiency at various applied potentials. (c) Normalized operando Ni K-edge XANES spectra for A-Ni-NG at various biases (applied voltage versus RHE) in 0.5 M KHCO3 aqueous solution at room temperature in 1 atm of Ar or CO2 . (d) Valence band spectra of A-Ni-NG before (black line) and after (red line) CO2 gas exposure, and after desorption of CO2 by thermal treatment at 500 ◦ C for 20 min in a vacuum (dark blue line). Reproduced with permission [288]. Copyright 2018, Springer Nature. (e) Theoretical calculations of the most stable structures of Cu-doped CeO2 (110) and their effects on CO2 activation. (f) XANES spectra at the Cu K-edge and the corresponding K3 -weighted Fourier Transform (FT) spectra in R-space. (g) Faradaic efficiencies (bars, left y-axis) and deep reduction products current density (jdrp , red curves, right y-axis) of the Cu-CeO2 -4 %. Reproduced with permission [206]. Copyright 2018, American Chemical Society.

Defects in N2 reduction reaction (NRR) Nitrogen is of importance to all of life and a number of industrial processes [315]. As it is a fact that the interchange of the nitrogen oxidation states (ammonia, nitric acid and so on) is mostly powered by fossil fuels [14,16,316–319]. Apart from the natural biological synthetic process, the ammonia is mainly produced via the Haber-Bosch process with Fe-based catalyst under harsh conditions (150−350 atm and 400–600 ◦ ) [16,320–323]. As a result, approximately 1.5–2 % of the globally annual energy is consumed along with the production of 400 Mt of CO2 [315,324–326]. As an alternative approach, the electrocatalytic N2 reduction reaction under ambient conditions has been proposed as a promising method for achieving clean and sustainable NH3 production with lower energy consumption and is becoming one of the most attractive topics in the field of chemistry [315,327,328]. By designing highly efficient electrocatalysts, including but not limited to heterogeneous, homogeneous, photo- and electrochemical catalysts, we can minimize the use of fossil fuels, which is harmful to the environment [7,15,329–340]. Currently, the reaction route of the NRR can be divided into dissociative and associative mechanisms [326,341]. In the dissociative

pathway, the N N triple bond is broken before the addition of H to the N atoms, while in the associative route, the N N bond is cleaved simultaneously with the release of the first NH3 atom [16,326]. Based on the different hydrogenation sequences, the associative route can be further divided into the distal route and alternating route [324,326,328]. However, due to the high bond energy (∼941 kJ mol−1 ) of the dinitrogen molecule, N2 is thermodynamically stable, which makes it a great challenge to activate the N2 for the following electrocatalytic reactions [328,342], as can be proved from the high reduction potential for the activation of dinitrogen molecules (Table 4). Despite the inertness of the N2 molecules, the competing HER process during the electrocatalysis process is another issue for obtaining the target product (NH3 ) due to the relatively smaller energy needed for the activation of the H2 O in the electrolyte [340,343]. Thus, the main focus in this field can be classified as the search for new electrocatalysts with high intrinsic activity, long-term stability and desirable selectivity [344–352] and the optimization of the electrocatalysts by suppressing the HER process via modulation of the electrolyte, test device and so on [353,354]. Defect engineering, which possesses the ability to influence the electronic structure of the electrocatalysts, is emerging as an effective way for the development of electrocatalysts in the

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Fig. 20. (a) Atomic models, reaction Gibbs free energy diagram from the adsorbed C2 H3 O intermediate to ethylene (black lines) and ethanol (red lines) for Cu with Cu vacancy and subsurface S and the same reaction free energy diagrams after applying a –0.5 V bias potential. (b–c) Faradaic efficiencies of alcohols (ethanol and propanol) and ethylene on different catalysts at the potential of –0.95 V versus RHE and Faradaic efficiencies of C2+ alcohols (ethanol and propanol) on CSVE nanocatalyst in the current density range 200–600 mA cm−2 . Reproduced with permission [256]. Copyright 2018, Springer Nature. (d) Faradaic efficiencies and CO2 RR partial current-densities for 15 ␮g of Cu/CeO2-x HDs, Cu − CeO2-x mix, Cu NCs, and CeO2-x NCs loaded on a glassy carbon surface of 1 cm2 , measured at −1.2 VRHE . (e) Partial density of states of the d-orbital of an interfacial Cu atom with and without CeO2-x cluster being present and the difference of charge distribution (The red isosurface (+0.0015) is enrichment region and the green isosurface (−0.0015) signifies depletion.) Reproduced with permission [296]. Copyright 2019, American Chemical Society.

nitrogen fixation reactions. In this section, we will focus on the recent advancement toward NRR electrocatalysts via defect engineering. Heteroatom doping has been proven as an effective way of enhancing the NRR performance of various electrocatalysts by optimizing the electron distribution of the nanomaterials [357,358]. By intentionally introducing the electron-deficient boron atoms into the graphene, Zheng [355] et al. prepared two-dimensional Bdoped graphene nanosheets and investigated its NRR performance. As proved by DFT-based calculations, the introduction of boron atoms into graphene caused the redistribution of the electron densities of both HOMO and LUMO, leading to the positively charged boron atoms (+0.59e), which is beneficial for the adsorption of N2 to boost the NRR performance (Fig. 22a). To further identify the active centers for the NRR process, XPS-based results were collected and analyzed, indicating that different carbon structures coexisted in the B-doped graphene and showed different catalytic activities towards NRR (Fig. 22b). Temperature-programmed desorption (TPD) results demonstrated the chemisorption of N2 , which is quite different from the physisorption in the pure graphene, indicating the effectiveness of the boron-doping strategy (Fig. 22c). Among different boron-doped carbon structures, the BC3 structure showed the lowest energy barrier (0.43 eV) for the electro-reduction of

nitrogen to ammonia, during which the formation of NH2 * is the limiting step. At a relatively low doping concentration (6.2 %), the Bdoped graphene achieved a high NH3 production of 9.8 ␮g h−1 cm-2 and a faradaic efficiency of 10.8 % (-0.5 V vs. RHE) in aqueous solutions at ambient conditions (Fig. 22d). The graphene-like BC3 structures played a key role in the enhanced NRR performance, which benefited both the N2 fixation and ammonia production. To further develop the cost-effective method for the electrochemical reduction of nitrogen into ammonia, Zhao [356] et al. recently reported the N-doped porous carbon (NPC) as the efficient electrocatalyst under the ambient conditions, where the N content and species were tuned to enhance the N2 chemical adsorption and N≡N cleavage The as-prepared NPC delivered a high production rate of 1.40 mmol·g−1 h−1 at −0.9 V vs. RHE (Fig. 22e). In order to identify the active centers of the NRR electrocatalysts, XPS characterization was carried out, which demonstrated different contents of pyridinic, pyrrolic, and graphitic N (Fig. 22f). DFT calculations indicated that the pyridinic and pyrrolic N were active sites for ammonia synthesis with much lower energy barrier for the activation of dinitrogen molecules, whose contents played an important role for the promotion of NH3 on NPC (Fig. 22g). As validated by the theoretical calculation results, the single atoms in the electrocatalysts can help to improve the NRR per-

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Fig. 21. (a) Two approaches detailed here for electrochemical characterization of defect effects on CO2 reduction. (b) jCO and jH2 versus total GB density for the five Au samples. (c) Scanning electrochemical cell microscopy of CO2 and H2 O reduction on Au on the 17◦ GB. (d) Scanning electrochemical cell microscopy of CO2 and H2 O reduction on Au on the other types of GB. Reproduced with permission [207]. Copyright 2017, American Association for the Advancement of Science. (e) TEM image and SAED patterns of a-Cu. (f)) FE of liquid products at each given potential for 2 h and the partial current densities for the production of liquid products. Reproduced with permission [301]. Copyright 2018, John Wiley & Sons, Inc.

formances through an enzymatic mechanism with a quite low overpotential [173,174,351,360–364]. Recently, by simply pyrolyzing Ru-containing ZIF-8, Zeng [113] et al. successfully obtained Ru single-atom dispersed N-doped carbon, and HAADF-STEM and EXAFS were used to identify the single-atom nature of the Ru on the N-doped carbon (Fig. 23a). The Ru SAs/N-C catalyst exhibited amazing NRR performances with a Faradaic efficiency of 29.6 % for -1 NH3 and 120.9 gNH3 mg-1 at -0.2 V vs. RHE, which was much cat h higher than the previously reported (Fig. 23b-c). The stronger binding strength between the N2 and Ru single atoms, combined with the much smaller energy barrier for the rate-limiting step revealed by the DFT-based calculations were believed to be the main reasons for the excellent NRR performances of the Ru SAs/N-C catalyst. By rationally designing Fe − N/C − carbon nanotube, Zheng [359] et al. synthesized highly-efficient NRR electrocatalysts with an NH3 production rate of 34.83 ␮g h−1 mg−1 cat. , a Faradaic efficiency of 9.28 % at −0.2 V vs. RHE, good selectivity, and long-term stability in 0.1 M KOH aqueous media under mild conditions, which outperformed the CNTs and NC-CNTs (Fig. 23d-e). More detailed investigations on the atomic-level revealed that the unique Fe-N3 center can significantly decrease the N2 adsorption energy (-0.75 eV) compared with the traditional Fe-N4 structure, leading to the high activ-

ity of the electrocatalysts (Fig. 23f). Moreover, the N* adsorption energy was about -5.47 eV in the Fe-N3 system, which was stronger than the competing counterparts (-4.34 eV for OH− , -0.53 eV for H+ and -0.21 eV for H2 O), resulting in the high selectivity of the Fe − N/C − CNTs. Vacancy, the simplest and conventional defect, can also help with the improvement of the NRR performance of the electrocatalysts [210,367]. By tuning the electronic structure of the polymeric carbon nitride (PCN) via the introduction of nitrogen vacancies, Yu [365] et al. realized effective NRR performance on the metalfree catalyst (Fig. 24a). Theoretical calculations indicated that the nitrogen molecules can adsorb on PCN in the form of a dinuclear end-on coordinated mode after the introduction of nitrogen vacancies. As a result, the N N triple bond can be significantly weakened with an increase of bond length (1.26 Å vs 1.095 Å in free N2 ) (Fig. 24b). Furthermore, the electrons on adjacent carbon atoms were transferred to the adsorbed N2 , which resembled that generally takes place in transition metals with the availability of d-orbital electrons for being donated into the ␲ N − N antibonding, leading to the activation of the N N triple bond. As proved by the NRR tests, the PCN-NV4 delivered a high NH3 production

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Table 3 Summary of the performance of representative defect-featured nanocatalysts for ECR. Catalyst

Electrolyte

Applied Potential

Products with FE

Ref.

Ni-doped carbon Fe-N-C S-modified Cu Cu/CeO2-x Cu-doped CeO2 CN-H-CNT Fe-/NG-750 SD-Cu NPs a-Cu CuCl-derived catalyst D-C-1100 SA-Sn␦+ -NG BND Plasma-activated Ag Co-N2 5 %Ni-SnS2 H-InOx NRs Ni–NG nanosheet C-Zn1 Ni4 ZIF-8 Cu foam@BiNW Sn(S) AN-Cu CuSn3 V-Cu2 S Cu(B) Cu-on-Cu3 N A-Ni-NSG CuOx –Vo

0.5 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 0.05 M KHCO3 0.1 M KHCO3 0.25 M KHCO3 0.1 M NaHCO3 0.1 M KHCO3 0.5 M KHCO3 0.1 M KHCO3 0.5 M NaHCO3 0.5 M KHCO3 1 M KHCO3 0.5 M NaHCO3 0.1 M KHCO3 0.1 M KHCO3 0.1 M KHCO3 1 M KOH 0.1 M KHCO3 0.1 M KHCO3 0.5 M KHCO3 0.1 M KHCO3

−0.67 V vs RHE −0.65 V vs. RHE −0.80 V vs. RHE −1.20 V vs. RHE −1.80 V vs. RHE −0.50 V vs. RHE −0.60 V vs. RHE −0.993 V vs. RHE −1.40 V vs. Ag/AgCl −2.00 V vs. Ag/AgCl −0.70 V vs. RHE −1.60 V vs. SCE −1.00 V vs. RHE −0.60 V vs. RHE −0.68 V vs. RHE −0.90 V vs. RHE −0.70 V vs. RHE −0.80 V vs. RHE −0.63 V vs. RHE −0.69 V vs. RHE −0.75 V vs. RHE −1.08 V vs. RHE −0.50 V vs. RHE −0.92 V vs. RHE −1.00 V vs. RHE −0.95 V vs. RHE −0.50 V vs. RHE −1.40 V vs. RHE

CO (93 %) CO (93 %) HCOOH (80 %) CH4 (54%) CH4 (58%) CO (88 %) CO (80 %) C2 H4 (52.43% ± 2.72%) HCOOH (37 %), EtOH (22 %) C2 H4 (56%) CO (80.4 %) HCOOH (74.3 %) EtOH (93.2 %) CO (>90 %) CO (95 %) HCOOH (93 %) HCOOH (91.7 %) CO (95 %) CO (97.8 %) HCOOH (95 %) HCOOH (93 %) C2 H4 (38.1%) HCOOH (95 %) C2+ alcohol (32 ± 1%) C2+ (79 ± 2%) C2+ (64 ± 2%) CO (97 %) C2 H4 (63%)

[117] [203] [205] [296] [206] [208] [116] [306] [301] [307] [308] [309] [209] [310] [295] [292] [298] [169] [293] [311] [312] [313] [255] [256] [118] [314] [288] [297]

Table 4 Several representative half-reactions and reduction potential of nitrogen reduction reactions in aqueous solutions. Reaction

E0 / V vs. RHE

2H2 O → O2 + 4H+ + 4e− 2H+ + 2e− = H2(g) N2 + e− = N2 − N2 + H+ + e− = N2 H N2 + 2H+ + 2e− = N2 H2 N2 + 4H+ + 2e− = N2 H4 N2 + 6H+ + 6e− = 2NH3

1.23 0 −3.74 −0.63 −0.48 −0.30 0.07

the development of the advanced characterization methods and DFT-based calculations, researchers can obtain sufficient and accurate information about the imperfections, which may help us with understanding the role of the imperfections played during the catalysis process. By combining experimental and theoretical results, the rational design of electrocatalyst with optimal performances has become possible and is now thriving. Although much work has been done on the defects of the catalysts, many challenges remain. 1 Controllable production of the defects.

8.09 ␮g h−1

mg−1

rate of cat and a high Faradic efficiency of 11.59 % (Fig. 24c). Despite the doping method in tuning the electronic structures of the electrocatalysts, amorphization offers another route to improve the performance of catalysts during the nitrogen reduction reaction [368,369]. Via a facile co-reduction method, Jiang [369] et al. prepared the CeOx -induced amorphization of Au nanoparticles anchored on reduced graphite oxide (a-Au/CeOx –RGO) (Fig. 24d). With a low loading content of Au (1.31 wt%), the a-Au/CeOx –RGO delivered a Faradic efficiency as high as 10.10 % and an ammonia yield rate as high as 8.3 ␮g h−1 mg−1 cat at -0.2 V vs. RHE, which was much enhanced performance than that of the crystalline counterpart (c-Au/RGO) (Fig. 24e). To better understand the recent advances in the electrocatalytic reduction of N2 , the representative defect-featured electrocatalysts for NRR with their yield rate and Faradaic efficiency are provided in Table 5. Summary and outlook In summary, the imperfections of the nanomaterials have played a vital role in various electrocatalysis processes, including but not limited to HER, OER, ORR, ECR, NRR, etc. via influencing the electronic structure of the electrocatalysts. The imperfections can serve as active sites or electronic-structure modifier, which enhance the catalytic performance in a direct or indirect way. Due to

Although many methods have been developed for the introduction of various defects, controllable preparation of defects has remained a great challenge. At present, defects are either in-situ generated during the reaction process in a random way or created via chemical or physical methods after the synthesis of electrocatalysts. The fact that the defects in the electrocatalyst are randomly arranged has made it difficult to understand the role more systematically. The relationship between the defects and activity of the catalysts remains, to some extent, in the black box due to the lack of a controlled method for the preparation of defects. It is therefore necessary to develop effective methods to controllably induce various defects into nanomaterials. Benefited from the rapid development of advanced high-resolution imaging technology, using energetic particle etching at atomic sites assisted by atomic-resolution microscopy may be a promising approach to generate defects with desired species, density, and chemical environment accurately in the near future. Further, atomic layer deposition with carefully adjusted parameters has also proved itself an alternative approach for the controlled preparation of defects. 2 Advanced in-situ characterization methods The morphology or composition of the electrocatalysts may change during the catalysis process under applied potentials,

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Fig. 22. (a) Schematic of the atomic orbital of BC3 for binding N2 and the LUMO (blue) and HOMO (red) of undoped G (G) and BG. (b) Percentages of different B types in the three BG samples (c) N2 TPD curve of BG-1, BOG, BG-2, and G. (d) The NH3 production rates and Faradaic efficiencies of BG-1, BOG, BG-2, and G at different potentials. Reproduced with permission [355]. Copyright 2018, Elsevier. (e) Ammonia production rates of NPC-750, NPC-850, and NPC-950 at -0.7 and -0.9 V. (f) Contents of pyridinic, pyrrolic, and graphitic N in NPCs. (g) Free energy diagram for ammonia synthesis on NPC with pyridinic (Left) and pyrrolic N (Right).Reproduced with permission [356]. Copyright 2018, American Chemical Society.

Table 5 Summary of the performance of representative defect-featured nanocatalysts for NRR. Catalyst

Electrolyte

Yield Rate (␮g h−1 mgcat. )

Faradaic Efficiency

Potential (V vs. RHE)

Ref

Ru SAs/N-C a-Au/CeOx –RGO PCN BG-1 Bi NS Fe − N/C − CNTs TiO2 /Ti3 C2 Tx DR-MoS2 Pd0.2 Cu0.8 /rGO TA-reduced Au/TiO2 SA-Mo/NPC Ru@ZrO2 /NC FeSA –N–C CoS2 /NS-G BCN AuSAs-NDPCs

0.05 M H2 SO4 0.1 M HCl 0.1 M HCl 0.05 M H2 SO4 0.1 M Na2 SO4 0.1 M KOH 0.1 M HCl 0.1 M Na2 SO4 0.1 M KOH 0.1 M HCl 0.1 M KOH 0.1 M HCl 0.1 M KOH 0.05 M H2 SO4 0.1 M HCl 0.1 M HCl

120.9 8.3 8.09 ∼54.88 ∼13.23 34.83 32.17 29.28 2.80 21.4 34.0 ± 3.6 3665 7.48 – 7.75 3.87

29.60 % 10.10 % 11.59 % 10.80 % 10.46 ± 1.45 % 9.28 % 16.07 % 8.34 % – 8.11 % 14.6 ± 1.6 % ∼15 % 56.55 % 25.9 % 13.79 % 12.3 %

−0.20 −0.20 −0.20 −0.50 −0.80 −0.20 −0.45 −0.40 −0.20 −0.20 −0.30 −0.21 0.00 −0.05 −0.30 −0.20

[113] [366] [365] [355] [346] [359] [370] [109] [371] [372] [364] [351] [373] [374] [375] [376]

which makes it desirable to investigate in-situ the change of the active sites in order to truly understand the catalysis process. The operando characterization techniques that allow in-situ monitor-

ing the perturbation of the catalyst surfaces and the local atomic structures are capable of offering information about the active sites and promoting the fundamental understanding of the reac-

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Fig. 23. (a) Scheme of the synthetic procedure for Ru SAs/N-C. (b–c) FE and yield rate of NH3 production at different applied potentials on Ru SAs/N-C and Ru NPs/N-C. Reproduced with permission [113]. Copyright 2018, John Wiley & Sons, Inc. (d) The yield rate of NH3 (blue) and the Faradaic efficiency (red) at each given potential of Fe − N/C − CNTs catalyst in 0.1 M KOH electrolyte. (e) The yield rate of NH3 at −0.2 V vs RHE at room temperature and ambient pressure with different catalysts. (f) The different free-energy diagrams of the N2 adsorption on the Fe − N4 and Fe − N3 structures and the free-energy diagram for the NRR on Fe − N3 /C − CNTs at zero and the applied potential through the distal pathway. Reproduced with permission [359]. Copyright 2019, American Chemical Society.

tion systems. However, due to the high expense and complication of the in-situ characterization methods, limited results have been reported. Thus, developing low-cost and simple methods for the insitu investigation of the catalysis process is desired. Furthermore, the present in-situ characterization methods are mostly based on the principles of spectroscopy (in-situ IR, Raman, localized surface plasmon resonance, XPS, XAS, etc.), which makes the exploration of other methods intriguing (such as in-situ microscopy, scanning electrochemical microscopy, etc.). 3 Expansion of the application fields of the defects. The imperfections in the catalysts have proved its importance in various catalysis processes, especially in the field of electrocatalysis and photocatalysis, which is exciting and meaningful. Due to the fact that electronic structures of the nanomaterials can be significantly influenced by the imperfections, the expansion of the application fields of defects can be readily made. By tuning the density and type of defects in the materials, performance enhancement can be rationally achieved in the lithium-ion batteries, photovoltaic devices, metal-air batteries, solid-state batteries, organic electrochemical synthesis, and other energy conversion and storage fields.

The potentials of the defects are far from completely exploited, which makes the studies of the defects in the emerging research areas infusive and expecting. 4 Investigations of the non-zero dimensional defects. For its simplicity and relative easiness of preparation, point defects (mainly vacancy, substitutions) become the focus of the research, while rare reports about the role of the non-zero dimensional defects in the electrocatalysis process are mentioned. The unique composition and arrangement of the special point defect, namely Schottky and Frenkel defects, have proved their significance in the physical society, yet almost few literature have been reported in the electrocatalysis. Other non-zero dimensional defects, line defects (edge dislocations, screw dislocations), stacking faults, twin boundary and so on, have their unique influence on the electronic structures of the catalysts, which make it possible to change the performance of the catalysts. To conclude, we believe that the in-depth understanding of the defect-derived electron-distribution optimization in the electrocatalytic processes will promote the whole field and push them into the industrial deployment.

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Fig. 24. (a) TEM image of the PCN-V4. (b) The charge density difference of the N2 -adsorbed PCN with nitrogen-vacancy (the yellow and blue isosurfaces represent charge accumulation and depletion in the space, respectively) and the free energy diagram for NRR on NVs engineered PCN at equilibrium potential. (c) The yield of NH3 (purple-red) and Faradaic efficiency (navy-blue) at each given potential of the PCN-V4. Reproduced with permission [365]. Copyright 2018, John Wiley & Sons, Inc. (d) Representative STEM image of the a-Au/CeOx –RGO. (e) The yield of NH3 (red) and Faradaic efficiency (blue) at each given potential and the yield of NH3 with different catalysts at -0.2 V versus RHE at room temperature and atmospheric pressure. Reproduced with permission [366]. Copyright 2017, John Wiley & Sons, Inc.

Declaration of Competing Interest The authors declare no competing financial interests. Acknowledgments This work was supported by the NSFC under Grant No.21603208, the Shenzhen Science and Technology Project under Grant Nos. JCYJ20170412105400428 and JCYJ20180507182246321, the Shenzhen Peacock Technological Innovation Project under Grant No. KQJSCX20170727101208249, Fundamental Research Funds for the Central Universities, and the Open Project Program of the State Key Laboratory of Silicon Materials, Zhejiang University. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Y. Liu, C. Xiao, P. Huang, M. Cheng, Y. Xie, Chemistry 4 (2018) 1263–1283. P. Zhang, T. Wang, J. Gong, Chem. 4 (2018) 223–245. B. You, Y. Sun, Acc. Chem. Res. 51 (2018) 1571–1580. J. Jia, C. Qian, Y. Dong, Y.F. Li, H. Wang, M. Ghoussoub, et al., Chem. Soc. Rev. 46 (2017) 4631–4644. J. Yu, Q. Wang, D. O’Hare, L. Sun, Chem. Soc. Rev. 46 (2017) 5950–5974. C. Tang, S.-Z. Qiao, Chem. Soc. Rev. (2019), 10.1039.C9CS00280D. J.G. Chen, R.M. Crooks, L.C. Seefeldt, K.L. Bren, R.M. Bullock, M.Y. Darensbourg, et al., Science 360 (2018) eaar6611. O.S. Bushuyev, P. De Luna, C.T. Dinh, L. Tao, G. Saur, J. van de Lagemaat, et al., Joule 2 (2018) 825–832. A. Chen, B.-L. Lin, Joule 2 (2018) 594–606.

[10] Z. Xia, S. Guo, Chem. Soc. Rev. (2019), 10.1039.C8CS00846A. [11] X. Yan, Y. Jia, X. Yao, Chem. Soc. Rev. 47 (2018) 7628–7658. [12] H. Jin, C. Guo, X. Liu, J. Liu, A. Vasileff, Y. Jiao, et al., Chem. Rev. 118 (2018) 6337–6408. [13] Z. Sun, T. Ma, H. Tao, Q. Fan, B. Han, Chem. 3 (2017) 560–587. [14] J.G. Chen, R.M. Crooks, L.C. Seefeldt, K.L. Bren, R.M. Bullock, M.Y. Darensbourg, et al., Science 360 (2018) eaar6611. ˜ [15] J. Deng, J.A. Iniguez, C. Liu, Joule 2 (2018) 846–856. [16] X. Cui, C. Tang, Q. Zhang,Adv. Energy Mater. (2018), 1800369. [17] J. Wang, Z. Wei, S. Mao, H. Li, Y. Wang, Energy Environ. Sci. 11 (2018) 800–806. [18] Y.-Y. Ma, Z.-L. Lang, L.-K. Yan, Y.-H. Wang, H.-Q. Tan, K. Feng, et al., Energy Environ. Sci. 11 (2018) 2114–2123. [19] Z. Luo, Y. Ouyang, H. Zhang, M. Xiao, J. Ge, Z. Jiang, W. Xing, et al., Nat. Commun. 9 (2018). [20] G. Ou, Y. Xu, B. Wen, R. Lin, B. Ge, Y. Tang, et al., Nat. Commun. 9 (2018). [21] W. Xiao, W. Lei, M. Gong, H.L. Xin, D. Wang, ACS Catal. 8 (2018) 3237–3256. [22] I. Roger, M.A. Shipman, M.D. Symes, Nat. Rev. Chem. 1 (2017) 0003. [23] G. Li, G.R. Blake, T.T.M. Palstra, Chem. Soc. Rev. 46 (2017) 1693–1706. [24] J. Nowotny, M.A. Alim, T. Bak, M.A. Idris, M. Ionescu, K. Prince, et al., Chem. Soc. Rev. 44 (2015) 8424–8442. [25] C. Zhang, J. Lin, Chem. Soc. Rev. 41 (2012) 7938. [26] Q. Feng, S. Zhao, D. He, S. Tian, L. Gu, X. Wen, et al., J. Am. Chem. Soc. 140 (2018) 2773–2776. [27] M. Luo, S. Guo, Nat. Rev. Mater. 2 (2017) 17059. [28] C. Guo, Y. Zheng, J. Ran, F. Xie, M. Jaroniec, S.-Z. Qiao, Angew. Chem. Int. Ed. 56 (2017) 8539–8543. [29] X. Long, H. Lin, D. Zhou, Y. An, S. Yang, ACS Energy Lett. 3 (2018) 290–296. [30] Y. Li, J. Huang, X. Hu, L. Bi, P. Cai, J. Jia, et al.,Adv. Funct. Mater. (2018), 1803330. [31] H. Fang, J. Yang, M. Wen, Q. Wu,Adv. Mater. 30 (2018), 1705698. [32] M. Setvín, M. Wagner, M. Schmid, G.S. Parkinson, U. Diebold, Chem. Soc. Rev. 46 (2017) 1772–1784.

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G Model NANTOD-100833; No. of Pages 32

ARTICLE IN PRESS S. Jiao, X. Fu, L. Zhang et al. / Nano Today xxx (xxxx) xxx

[33] R. Asahi, T. Morikawa, H. Irie, T. Ohwaki, Chem. Rev. 114 (2014) 9824–9852. [34] D. Deng, K.S. Novoselov, Q. Fu, N. Zheng, Z. Tian, X. Bao, Nat. Nanotechnol. 11 (2016) 218–230. [35] J. Deng, H. Li, J. Xiao, Y. Tu, D. Deng, H. Yang, X. Bao, et al., Energy Environ. Sci. 8 (2015) 1594–1601. [36] X.-L. Wei, Y.-P. Chen, W.-L. Liu, J.-X. Zhong, Phys. Lett. A 376 (2012) 559–562. [37] M.-W. Ahn, K.-S. Park, J.-H. Heo, J.-G. Park, D.-W. Kim, K.J. Choi, et al.,Appl. Phys. Lett. 93 (2008), 263103. [38] Y. Shi, G. Chen, F. Liu, X. Yue, Z. Chen, ACS Energy Lett. 3 (2018) 1683–1692. [39] A. Ulvestad, A. Singer, J.N. Clark, H.M. Cho, J.W. Kim, R. Harder, et al., Science 348 (2015) 1344–1347. [40] J. Kim, S.-H. Lee, J.H. Lee, K.-H. Hong, J. Phys. Chem. Lett. 5 (2014) 1312–1317. [41] W.S. Yang, B.-W. Park, E.H. Jung, N.J. Jeon, Y.C. Kim, D.U. Lee, et al., Science 356 (2017) 1376–1379. [42] Z. Liu, J. Hu, H. Jiao, L. Li, G. Zheng, Y. Chen, et al.,Adv. Mater. 29 (2017), 1606774. [43] J.M. Ball, A. Petrozza, Nat. Energy 1 (2016) 16149. [44] N. Aristidou, C. Eames, I. Sanchez-Molina, X. Bu, J. Kosco, M.S. Islam, et al., Nat. Commun. 8 (2017) 15218. [45] Z. Hu, Z. Wu, C. Han, J. He, Z. Ni, W. Chen, Chem. Soc. Rev. 47 (2018) 3100–3128. [46] S. Yang, J. Kang, Q. Yue, J.M.D. Coey, C. Jiang,Adv. Mater. Interfaces 3 (2016), 1500707. [47] J. Yuan, L. Hu, Z. Xu, Y. Zhang, H. Li, X. Cao, et al., J. Phys. Chem. C 123 (2019) 14766–14773. [48] L. Hu, Q. Liao, Z. Xu, J. Yuan, Y. Ke, Y. Zhang, et al., ACS Photonics 6 (2019) 886–894. [49] B. Gahtori, S. Bathula, K. Tyagi, M. Jayasimhadri, A.K. Srivastava, S. Singh, et al., Nano Energy 13 (2015) 36–46. [50] Q. Zhang, Q. Song, X. Wang, J. Sun, Q. Zhu, K. Dahal, et al., Energy Environ. Sci. 11 (2018) 933–940. [51] H. Tamaki, H.K. Sato, T. Kanno, Adv. Mater. 28 (2016) 10182–10187. [52] L. Hu, M.N. Amini, Y. Wu, Z. Jin, J. Yuan, R. Lin, et al.,Adv. Opt. Mater. 6 (2018), 1800440. [53] R. Lin, L. Hu, J. Wang, W. Zhang, S. Ruan, Y.-J. Zeng, Opt. Lett. 43 (2018) 2244–2247. [54] C. Tan, H. Zhang, Chem. Soc. Rev. 44 (2015) 2713–2731. [55] X. Zhang, Z. Lai, Q. Ma, H. Zhang, Chem. Soc. Rev. (2018). [56] X. Wang, G. Sun, P. Routh, D.-H. Kim, W. Huang, P. Chen, Chem. Soc. Rev. 43 (2014) 7067–7098. [57] X.-K. Kong, C.-L. Chen, Q.-W. Chen, Chem. Soc. Rev. 43 (2014) 2841–2857. [58] Q. He, Y. Wan, H. Jiang, Z. Pan, C. Wu, M. Wang, et al., ACS Energy Lett. 3 (2018) 1373–1380. [59] X. Jiao, Z. Chen, X. Li, Y. Sun, S. Gao, W. Yan, et al., J. Am. Chem. Soc. 139 (2017) 7586–7594. [60] S. Gao, B. Gu, X. Jiao, Y. Sun, X. Zu, F. Yang, et al., J. Am. Chem. Soc. 139 (2017) 3438–3445. [61] F. Lei, Y. Sun, K. Liu, S. Gao, L. Liang, B. Pan, et al., J. Am. Chem. Soc. 136 (2014) 6826–6829. [62] J. Xie, H. Zhang, S. Li, R. Wang, X. Sun, M. Zhou, et al., Adv. Mater. 25 (2013) 5807–5813. [63] J. Xie, J. Zhang, S. Li, F. Grote, X. Zhang, H. Zhang, R. Wang, et al., J. Am. Chem. Soc. 135 (2013) 17881–17888. [64] Z. Li, C. Xiao, H. Zhu, Y. Xie, J. Am. Chem. Soc. 138 (2016) 14810–14819. [65] K. Liu, W. Zhang, F. Lei, L. Liang, B. Gu, Y. Sun, et al., Nano Energy 30 (2016) 810–817. [66] H. Li, C. Tsai, A.L. Koh, L. Cai, A.W. Contryman, A.H. Fragapane, et al., Nat. Mater. 15 (2016) 48–53. [67] S. Dou, L. Tao, J. Huo, S. Wang, L. Dai, Energy Environ. Sci. 9 (2016) 1320–1326. [68] Z. Xiao, Y. Wang, Y.-C. Huang, Z. Wei, C.-L. Dong, J. Ma, et al., Wang, Energy Environ. Sci. 10 (2017) 2563–2569. [69] Z. Liu, Z. Zhao, Y. Wang, S. Dou, D. Yan, D. Liu, Z. Xia, S. Wang, Adv. Mater. 29 (2017). [70] Y. Wang, Y. Zhang, Z. Liu, C. Xie, S. Feng, D. Liu, M. Shao, S. Wang, Angew. Chem. Int. Ed. 56 (2017) 5867–5871. [71] L. Tao, X. Duan, C. Wang, X. Duan, S. Wang, Chem. Commun. 51 (2015) 7470–7473. [72] L. Xu, Q. Jiang, Z. Xiao, X. Li, J. Huo, S. Wang, L. Dai, Angew. Chem. Int. Ed. 55 (2016) 5277–5281. [73] R. Liu, Y. Wang, D. Liu, Y. Zou, S. Wang, Adv. Mater. 29 (2017) 1–7. [74] J.P. Thiruraman, K. Fujisawa, G. Danda, P.M. Das, T. Zhang, A. Bolotsky, et al., Nano Lett. 18 (2018) 1651–1659. [75] G. Nanda, S. Goswami, K. Watanabe, T. Taniguchi, P.F.A. Alkemade, Nano Lett. 15 (2015) 4006–4012. [76] S. Bertolazzi, S. Bonacchi, G. Nan, A. Pershin, D. Beljonne, P. Samorì,Adv. Mater. 29 (2017), 1606760. [77] D.S. Fox, Y. Zhou, P. Maguire, A. O’Neill, C. Ó’Coileáin, R. Gatensby, et al., Nano Lett. 15 (2015) 5307–5313. [78] C. Zhao, X. Dai, T. Yao, W. Chen, X. Wang, J. Wang, et al., J. Am. Chem. Soc. 139 (2017) 8078–8081. [79] Z. Li, D. Wang, Y. Wu, Y. Li, Sci. Rev. (2018). [80] X. Wang, Z. Chen, X. Zhao, T. Yao, W. Chen, R. You, et al., Angew. Chem. 130 (2018) 1962–1966. [81] P. Yin, T. Yao, Y. Wu, L. Zheng, Y. Lin, W. Liu, et al., Angew. Chem. Int. Ed. 55 (2016) 10800–10805.

29

[82] X. Dai, Z. Chen, T. Yao, L. Zheng, Y. Lin, W. Liu, et al., Chem. Commun. (Camb.) 53 (2017) 11568–11571. [83] X. Wang, W. Chen, L. Zhang, T. Yao, W. Liu, Y. Lin, et al., J. Am. Chem. Soc. 139 (2017) 9419–9422. [84] L. Jiao, G. Wan, R. Zhang, H. Zhou, S.-H. Yu, H.-L. Jiang, Angew. Chem. Int. Ed. 57 (2018) 8525–8529. [85] G.-L. Chai, K. Qiu, M. Qiao, M.-M. Titirici, C. Shang, Z. Guo, Energy Environ. Sci. 10 (2017) 1186–1195. [86] Q. He, H. Xie, Z. ur Rehman, C. Wang, P. Wan, H. Jiang, W. Chu, L. Song, ACS Energy Lett. 3 (2018) 861–868. [87] M. Naguib, V.N. Mochalin, M.W. Barsoum, Y. Gogotsi, Adv. Mater. 26 (2014) 992–1005. [88] W. Yang, X. Zhang, Y. Xie, Nano Today 11 (2016) 793–816. [89] F. Bonaccorso, L. Colombo, G. Yu, M. Stoller, V. Tozzini, a C. Ferrari, et al., Science 347 (2015), 1246501–1246501. [90] X. Zhao, F. Li, R. Wang, J.-M. Seo, H.-J. Choi, S.-M. Jung, et al.,Adv. Funct. Mater. 27 (2017), 1605717. [91] C. Zhang, S. Yang, J. Wu, M. Liu, S. Yazdi, M. Ren, et al.,Adv. Energy Mater. (2018), 1703487. [92] B. Yan, D. Krishnamurthy, C.H. Hendon, S. Deshpande, Y. Surendranath, V. Viswanathan, Joule 1 (2017) 600–612. [93] J. Zhang, J. Liu, L. Xi, Y. Yu, N. Chen, S. Sun, et al., J. Am. Chem. Soc. 140 (2018) 3876–3879. [94] Z. Sun, T. Ma, H. Tao, Q. Fan, B. Han, Chem 3 (2017) 560–587. [95] Q. Wang, Y. Lei, D. Wang, Y. Li, Energy Environ. Sci. (2019), 10.1039.C8EE03781G. [96] J. Deng, H. Li, S. Wang, D. Ding, M. Chen, C. Liu, et al., Nat. Commun. 8 (2017) 14430. [97] L. Liang, X. Li, Y. Sun, Y. Tan, X. Jiao, H. Ju, Z. Qi, J. Zhu, Y. Xie, Joule (2018). [98] Z. Xiao, Y. Wang, Y.-C. Huang, Z. Wei, C.-L. Dong, J. Ma, et al., Energy Environ. Sci. 10 (2017) 2563–2569. [99] M. Luo, S. Guo, Nat. Rev. Mater. 2 (2017) 17059. [100] H.B. Yang, J. Miao, S.-F. Hung, J. Chen, H.B. Tao, X. Wang, et al.,Sci. Adv. 2 (2016), e1501122–e1501122. [101] J.K. Norskov, F. Abild-Pedersen, F. Studt, T. Bligaard, Proc. Natl. Acad. Sci. 108 (2011) 937–943. [102] Gomez, Carolina. Fundamental Studies of d-Band Transition Metal Alloys. Diss., 2013. [103] J.K. Nørskov, Prog. Surf. Sci. 38 (2) (1991) 103–144. [104] H. Huang, K. Li, Z. Chen, L. Luo, Y. Gu, D. Zhang, et al., J. Am. Chem. Soc. 139 (2017) 8152–8159. [105] P. Chen, Y. Tong, C. Wu, Y. Xie, Acc. Chem. Res. 51 (2018) 2857–2866. [106] J. Xie, J. Zhang, S. Li, F. Grote, X. Zhang, H. Zhang, et al., J. Am. Chem. Soc. 135 (2013) 17881–17888. [107] Y. Shi, Y. Zhou, D.-R. Yang, W.-X. Xu, C. Wang, F.-B. Wang, et al., J. Am. Chem. Soc. 139 (2017) 15479–15485. [108] F. Li, G.-F. Han, H.-J. Noh, S.-J. Kim, Y. Lu, H.Y. Jeong, et al., Energy Environ. Sci. 11 (2018) 2263–2269. [109] X. Li, T. Li, Y. Ma, Q. Wei, W. Qiu, H. Guo, et al.,Adv. Energy Mater. 8 (2018), 1801357. [110] J.K. Nørskov, F. Studt, F. Abild-Pedersen, T. Bligaard, (n.d.) 208. [111] Y. Liu, S. Zhou, J. Li, Y. Wang, G. Jiang, Z. Zhao, et al., Appl. Catal. B Environ. 168–169 (2015) 125–131. [112] Z. Geng, X. Kong, W. Chen, H. Su, Y. Liu, F. Cai, et al., Angew. Chem. Int. Ed. 57 (2018) 6054–6059. [113] Z. Geng, Y. Liu, X. Kong, P. Li, K. Li, Z. Liu, et al.,Adv. Mater (2018), 1803498. [114] Y. Chen, C.W. Li, M.W. Kanan, J. Am. Chem. Soc. 134 (2012) 19969–19972. [115] J. Greeley, T.F. Jaramillo, J. Bonde, I. Chorkendorff, J.K. Nørskov, Nat. Mater. 5 (2006) 909–913. [116] C. Zhang, S. Yang, J. Wu, M. Liu, S. Yazdi, M. Ren, et al.,Adv. Energy Mater. 8 (2018), 1703487. [117] X.-M. Hu, H.H. Hval, E.T. Bjerglund, K.J. Dalgaard, M.R. Madsen, M.-M. Pohl, et al., ACS Catal. 8 (2018) 6255–6264. [118] Y. Zhou, F. Che, M. Liu, C. Zou, Z. Liang, P. De Luna, et al., Nat. Chem. 10 (2018) 974–980. [119] Y. Lum, J.W. Ager, Nat. Catal. 2 (2019) 86–93. [120] X. Zheng, Y. Ji, J. Tang, J. Wang, B. Liu, H.-G. Steinrück, et al., Nat. Catal. 2 (2019) 55–61. [121] H. Li, L. Wang, Y. Dai, Z. Pu, Z. Lao, Y. Chen, et al., Nat. Nanotechnol. 13 (2018) 411–417. [122] R. Brydson (Ed.), Aberration-Corrected Analytical Transmission Electron Microscopy: Brydson/Aberration-Corrected Analytical Transmission Electron Microscopy, John Wiley & Sons, Ltd, Chichester, UK, 2011. [123] B. Qiao, A. Wang, X. Yang, L.F. Allard, Z. Jiang, Y. Cui, J. Liu, J. Li, T. Zhang, Nat. Chem. 3 (2011) 634–641. [124] L. Zhang, Y. Jia, G. Gao, X. Yan, N. Chen, J. Chen, et al., Chem. 4 (2018) 285–297. [125] G. Liu, A.W. Robertson, M.M.-J. Li, W.C.H. Kuo, M.T. Darby, M.H. Muhieddine, et al., Nat. Chem. 9 (2017) 810–816. [126] W. Chen, J. Pei, C.-T. He, J. Wan, H. Ren, Y. Wang, et al.,Adv. Mater 30 (2018), 1800396. [127] C. Zhu, Q. Shi, S. Feng, D. Du, Y. Lin, ACS Energy Lett. 3 (2018) 1713–1721. [128] Q. Cheng, L. Yang, L. Zou, Z. Zou, C. Chen, Z. Hu, H. Yang, ACS Catal. 7 (2017) 6864–6871. [129] J. Wan, W. Chen, C. Jia, L. Zheng, J. Dong, X. Zheng, et al.,Adv. Mater. 30 (2018), 1705369.

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ARTICLE IN PRESS S. Jiao, X. Fu, L. Zhang et al. / Nano Today xxx (xxxx) xxx

[130] S. Gao, Z. Sun, W. Liu, X. Jiao, X. Zu, Q. Hu, et al., Nat. Commun. 8 (2017) 14503. [131] J.A. van Bokhoven, C. Lamberti, in: X-Ray Absorption and X-Ray Emission Spectroscopy: Theory and Applications, John Wiley & Sons, Inc, Chichester, West Sussex, 2015. [132] G. Bunker, Introduction to XAFS: A Practical Guide to X-ray Absorption Fine Structure Spectroscopy, Cambridge University Press, 2010. [133] X.-P. Yin, H.-J. Wang, S.-F. Tang, X.-L. Lu, M. Shu, R. Si, et al., Angew. Chem. Int. Ed. 57 (2018) 9382–9386. [134] Q. Li, W. Chen, H. Xiao, Y. Gong, Z. Li, L. Zheng, et al.,Adv. Mater 30 (2018), 1800588. [135] P. Chen, T. Zhou, L. Xing, K. Xu, Y. Tong, H. Xie, et al., Angew. Chem. Int. Ed. 56 (2017) 610–614. [136] H. Wang, N. Yang, W. Li, W. Ding, K. Chen, J. Li, et al., ACS Energy Lett. 3 (2018) 1345–1352. [137] T. Chao, X. Luo, W. Chen, B. Jiang, J. Ge, Y. Lin, et al., Angew. Chem. Int. Ed. 56 (2017) 16047–16051. [138] C. Wang, H. Zhang, J. Wang, Z. Zhao, J. Wang, Y. Zhang, et al., Chem. Mater. 29 (2017) 9915–9922. [139] J. Wan, W. Chen, C. Jia, L. Zheng, J. Dong, X. Zheng, et al.,Adv. Mater 30 (2018), 1705369. [140] J.M. Wagner (Ed.), X-Ray Photoelectron Spectroscopy, Nova Science Publishers, Inc, New York, 2011. [141] Paulvander Heide, X-ray Photoelectron Spectroscopy: an Introduction to Principles and Practices, Wiley, 2011. [142] H. Wang, D. Yong, S. Chen, S. Jiang, X. Zhang, W. Shao, et al., J. Am. Chem. Soc. 140 (2018) 1760–1766. [143] S. Zhang, G. Wang, J. Jin, L. Zhang, Z. Wen, J. Yang, Nano Energy 36 (2017) 186–196. [144] S. Huang, Y. Long, H. Yi, Z. Yang, L. Pang, Z. Jin, et al., Appl. Surf. Sci. 491 (2019) 328–334. [145] E. Talsi, K. Bryliakov, Applications of EPR and NMR Spectroscopy in Homogeneous Catalysis, Evgenii Talsi, Konstantin Bryliakov, CRC Press, 2017. [146] M. Drescher, G. Jeschke, E. Bordignon (Eds.), EPR Spectroscopy: Applications in Chemistry and Biology, Springer, Heidelberg, 2012. [147] Y. Yang, L.-C. Yin, Y. Gong, P. Niu, J.-Q. Wang, L. Gu, et al.,Adv. Mater. 30 (2018), 1704479. [148] H. Li, J. Shi, K. Zhao, L. Zhang, Nanoscale 6 (2014) 14168–14173. [149] L. Wang, W. Zhang, X. Zheng, Y. Chen, W. Wu, J. Qiu, et al., Nat. Energy 2 (2017) 869–876. [150] H. Li, L. Wang, Y. Dai, Z. Pu, Z. Lao, Y. Chen, et al., Nat. Nanotechnol. 13 (2018) 411–417. [151] M. Guan, C. Xiao, J. Zhang, S. Fan, R. An, Q. Cheng, et al., J. Am. Chem. Soc. 135 (2013) 10411–10417. [152] P. Asoka-Kumar, M. Alatalo, V.J. Ghosh, A.C. Kruseman, B. Nielsen, K.G. Lynn, Phys. Rev. Lett. 77 (1996) 2097–2100. [153] R.W. Siegel, Ann. Rev. Mater. Sci. 10 (1980) 393–425. [154] C. Xiao, X. Qin, J. Zhang, R. An, J. Xu, K. Li, et al., J. Am. Chem. Soc. 134 (2012) 18460–18466. [155] M.J. Eslamibidgoli, J. Huang, T. Kadyk, A. Malek, M. Eikerling, Nano Energy 29 (2016) 334–361. [156] A. Bruix, J.T. Margraf, M. Andersen, K. Reuter, Nat. Catal. 2 (2019) 659–670. [157] J.K. Nørskov, T. Bligaard, J. Rossmeisl, C.H. Christensen, Nat. Chem. 1 (2009) 37–46. [158] K. Honkala, A. Hellman, I.N. Remediakis, A. Logadottir, A. Carlsson, S. Dahl, C.H. Christensen, J.K. Nørskov 307 (2005) 5. [159] J.R. Kitchin, Nat. Catal. 1 (2018) 230–232. [160] P. Schlexer Lamoureux, K.T. Winther, J.A. Garrido Torres, V. Streibel, M. Zhao, M. Bajdich, et al., ChemCatChem 11 (2019) 3581–3601. [161] David Sholl, Janice A. Steckel, Density Functional Theory: a Practical Introduction, John Wiley & Sons, 2011. [162] P. Hohenberg, W. Kohn, Phys. Rev. 136 (1964) B864–B871. [163] W. Kohn, L.J. Sham, Phys. Rev. 140 (1965) A1133–A1138. [164] P. Geerlings, F.D. Proft, W. Langenaeker, (n.d.) 82. [165] A. Jain, Y. Shin, K.A. Persson, Nat. Rev. Mater. 1 (2016) 15004. [166] M.G. Medvedev, I.S. Bushmarinov, J. Sun, J.P. Perdew, K.A. Lyssenko, Science 355 (2017) 49–52. [167] N. Mardirossian, M. Head-Gordon, Mol. Phys. 115 (2017) 2315–2372. [168] Rutger A. Van Santen, Modern Heterogeneous Catalysis: an Introduction, John Wiley & Sons, 2017. [169] K. Jiang, S. Siahrostami, T. Zheng, Y. Hu, S. Hwang, E. Stavitski, et al., Energy Environ. Sci. 11 (2018) 893–903. [170] A. Vasileff, C. Xu, Y. Jiao, Y. Zheng, S.-Z. Qiao, Chem. 4 (2018) 1809–1831. [171] J.G. Chen, R.M. Crooks, L.C. Seefeldt, K.L. Bren, R.M. Bullock, M.Y. Darensbourg, et al., Science 360 (2018) eaar6611. [172] Z.W. Seh, J. Kibsgaard, C.F. Dickens, I. Chorkendorff, J.K. Nørskov, T.F. Jaramillo, Science 355 (2017) eaad4998. [173] Z. Chen, J. Zhao, C.R. Cabrera, Z. Chen,Small Methods (2018), 1800368. [174] Z.W. Chen, J.-M. Yan, Q. Jiang,Small Methods (2018), 1800291. [175] M. Li, Z. Zhao, T. Cheng, A. Fortunelli, C.-Y. Chen, R. Yu, et al., Science 354 (2016) 1414–1419. [176] H. Huang, H. Jia, Z. Liu, P. Gao, J. Zhao, Z. Luo, J. Yang, J. Zeng, Angew. Chem. Int. Ed. 56 (2017) 3594–3598. [177] Andrew R. Leach, Molecular Modelling: Principles and Applications, Pearson education, 2001.

[178] G.-L. Chai, Z. Hou, D.-J. Shu, T. Ikeda, K. Terakura, J. Am. Chem. Soc. 136 (2014) 13629–13640. [179] H.-J. Chun, V. Apaja, A. Clayborne, K. Honkala, J. Greeley, ACS Catal. 7 (2017) 3869–3882. [180] C. Cui, L. Gan, M. Heggen, S. Rudi, P. Strasser, Nat. Mater. 12 (2013) 765–771. [181] I. Martin-Bragado, R. Borges, J.P. Balbuena, M. Jaraiz, Prog. Mater. Sci. 92 (2018) 1–32. [182] B. Weiler, A. Gagliardi, P. Lugli, J. Phys. Chem. C 120 (2016) 10062–10077. [183] M. Zhong, R. Wang, K. Kawamoto, B.D. Olsen, J.A. Johnson, Science 353 (2016) 1264–1268. ˇ enas, ˙ [184] M. Sim J. Banys, E.E. Tornau, J. Mater. Chem. C Mater. Opt. Electron. Devices 6 (2018) 1487–1494. [185] H. Xu, R.E. Stoller, L.K. Béland, Y.N. Osetsky, Comput. Mater. Sci. 100 (2015) 135–143. [186] J. Greeley, N.M. Markovic, Energy Environ. Sci. 5 (2012) 9246. [187] N. Artrith, A.M. Kolpak, Nano Lett. 14 (2014) 2670–2676. [188] V. Viswanathan, H.A. Hansen, J. Rossmeisl, J.K. Nørskov, ACS Catal. 2 (2012) 1654–1660. [189] X.Y. Kong, Y.Y. Choo, S.-P. Chai, A.K. Soh, A.R. Mohamed, Chem. Commun. (Camb.) 52 (2016) 14242–14245. [190] J. Fang, H. Fan, Y. Ma, Z. Wang, Q. Chang, Appl. Surf. Sci. 332 (2015) 47–54. [191] J. Liu, A. Shen, X. Wei, K. Zhou, W. Chen, F. Chen, et al., ACS Appl. Mater. Interfaces 7 (2015) 20507–20512. [192] D. Yan, S. Dou, L. Tao, Z. Liu, Z. Liu, J. Huo, S. Wang, J. Mater. Chem. A Mater. Energy Sustain. 4 (2016) 13726–13730. [193] V.S. Kale, M. Hwang, H. Chang, J. Kang, S.I. Chae, Y. Jeon, et al.,Adv. Funct. Mater. 28 (2018), 1803786. [194] H. Nan, Z. Wang, W. Wang, Z. Liang, Y. Lu, Q. Chen, et al., ACS Nano 8 (2014) 5738–5745. [195] P.K. Chow, R.B. Jacobs-Gedrim, J. Gao, T.-M. Lu, B. Yu, H. Terrones, N. Koratkar, ACS Nano 9 (2015) 1520–1527. [196] B. Anasori, M.R. Lukatskaya, Y. Gogotsi, Nat. Rev. Mater. 2 (2017) 16098. [197] F. Bonaccorso, L. Colombo, G. Yu, M. Stoller, V. Tozzini, A.C. Ferrari, R.S. Ruoff, V. Pellegrini, Science 347 (2015), 1246501–1246501. [198] Z. Huang, H. Pan, W. Yang, H. Zhou, N. Gao, C. Fu, et al., ACS Nano 12 (2018) 208–216. [199] H. Tan, J. Tang, J. Henzie, Y. Li, X. Xu, T. Chen, et al., ACS Nano 12 (2018) 5674–5683. [200] F. Hu, H. Yang, C. Wang, Y. Zhang, H. Lu, Q. Wang,Small 13 (2017), 1602507. [201] Q. Wang, Y. Lei, Y. Zhu, H. Wang, J. Feng, G. Ma, et al., ACS Appl. Mater. Interfaces 10 (2018) 29448–29456. [202] L. Zhao, Q. Wang, X. Zhang, C. Deng, Z. Li, Y. Lei, M. Zhu, ACS Appl. Mater. Interfaces 10 (2018) 35888–35895. [203] F. Pan, H. Zhang, K. Liu, D. Cullen, K. More, M. Wang, et al., ACS Catal. 8 (2018) 3116–3122. [204] W. Ju, A. Bagger, G.-P. Hao, A.S. Varela, I. Sinev, V. Bon, B. Roldan Cuenya, S. Kaskel, J. Rossmeisl, P. Strasser, Nat. Commun. 8 (2017). [205] T. Shinagawa, G.O. Larrazábal, A.J. Martín, F. Krumeich, J. Pérez-Ramírez, ACS Catal. 8 (2018) 837–844. [206] Y. Wang, Z. Chen, P. Han, Y. Du, Z. Gu, X. Xu, G. Zheng, ACS Catal. (2018) 7113–7119. [207] R.G. Mariano, K. McKelvey, H.S. White, M.W. Kanan, Science 358 (2017) 1187–1192. [208] X. Cui, Z. Pan, L. Zhang, H. Peng, G. Zheng,Adv. Energy Mater. 7 (2017), 1701456. [209] Y. Liu, Y. Zhang, K. Cheng, X. Quan, X. Fan, Y. Su, et al., Angew. Chem. Int. Ed. 56 (2017) 15607–15611. [210] J. Zhao, Z. Chen, J. Am. Chem. Soc. 139 (2017) 12480–12487. [211] M. Zhao, L. Xu, M. Vara, A.O. Elnabawy, K.D. Gilroy, Z.D. Hood, et al., ACS Catal. 8 (2018) 6948–6960. [212] C. Choi, S. Back, N.-Y. Kim, J. Lim, Y.-H. Kim, Y. Jung, ACS Catal. 8 (2018) 7517–7525. [213] Q. Qin, T. Heil, M. Antonietti, M. Oschatz,Small Methods (2018), 1800202. [214] R.H. Crabtree, Chem. Rev. 117 (2017) 9228–9246. [215] Z. Guo, B. Liu, Q. Zhang, W. Deng, Y. Wang, Y. Yang, Chem. Soc. Rev. 43 (2014) 3480. [216] T. Li, F. Liu, Y. Tang, L. Li, S. Miao, Y. Su, et al., Angew. Chem. Int. Ed. 57 (2018) 7795–7799. [217] J. Di, C. Yan, A.D. Handoko, Z.W. Seh, H. Li, Z. Liu, Mater. Today (2018). [218] Z.-F. Huang, J. Song, Y. Du, S. Xi, S. Dou, J.M.V. Nsanzimana, et al., Nat. Energy 4 (2019) 329–338. [219] N.-T. Suen, S.-F. Hung, Q. Quan, N. Zhang, Y.-J. Xu, H.M. Chen, Chem. Soc. Rev. 46 (2017) 337–365. [220] B.Y. Guan, Y. Lu, Y. Wang, M. Wu, X.W.D. Lou,Adv. Funct. Mater. 28 (2018), 1706738. [221] S. Gupta, S. Zhao, X.X. Wang, S. Hwang, S. Karakalos, S.V. Devaguptapu, et al., ACS Catal. 7 (2017) 8386–8393. [222] Z. Kou, B. Guo, D. He, J. Zhang, S. Mu, ACS Energy Lett. 3 (2018) 184–190. [223] G.A. Ferrero, K. Preuss, A. Marinovic, A.B. Jorge, N. Mansor, D.J.L. Brett, et al., ACS Nano 10 (2016) 5922–5932. [224] C. Tang, B. Wang, H.-F. Wang, Q. Zhang,Adv. Mater. 29 (2017), 1703185. [225] T.T. Vo Doan, J. Wang, K.C. Poon, D.C.L. Tan, B. Khezri, R.D. Webster, H. Su, H. Sato, Angew. Chem. Int. Ed. 55 (2016) 6842–6847. [226] S. Kabir, A. Serov, K. Artyushkova, P. Atanassov, ACS Catal. 7 (2017) 6609–6618.

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[227] K. Yuan, S. Sfaelou, M. Qiu, D. Lützenkirchen-Hecht, X. Zhuang, Y. Chen, et al., ACS Energy Lett. 3 (2018) 252–260. [228] J. Wei, Y. Hu, Y. Liang, B. Kong, J. Zhang, J. Song, et al., Adv. Funct. Mater. 25 (2015) 5768–5777. [229] Y. Yang, K. Mao, S. Gao, H. Huang, G. Xia, Z. Lin, et al.,Adv. Mater. 30 (2018), 1801732. [230] Y.-C. Wang, Y.-J. Lai, L. Song, Z.-Y. Zhou, J.-G. Liu, Q. Wang, et al., Angew. Chem. Int. Ed. 54 (2015) 9907–9910. [231] B. Lu, T.J. Smart, D. Qin, J.E. Lu, N. Wang, L. Chen, et al., Chem. Mater. 29 (2017) 5617–5628. [232] H. Wu, H. Li, X. Zhao, Q. Liu, J. Wang, J. Xiao, et al., Energy Environ. Sci. 9 (2016) 3736–3745. [233] D. Li, Y. Jia, G. Chang, J. Chen, H. Liu, J. Wang, et al., Chemistry 4 (2018) 2345–2356. [234] V. Perazzolo, R. Brandiele, C. Durante, M. Zerbetto, V. Causin, G.A. Rizzi, et al., ACS Catal. 8 (2018) 1122–1137. [235] Y. Chen, Y. Choi, S. Yoo, Y. Ding, R. Yan, K. Pei, et al., Joule 2 (2018) 938–949. [236] C. Zhang, J. Sha, H. Fei, M. Liu, S. Yazdi, J. Zhang, et al., ACS Nano 11 (2017) 6930–6941. [237] Y. Zuo, D. Rao, S. Li, T. Li, G. Zhu, S. Chen, et al.,Adv. Mater. 30 (2018), 1704171. [238] J.-D. Yi, R. Xu, Q. Wu, T. Zhang, K.-T. Zang, J. Luo, et al., ACS Energy Lett. 3 (2018) 883–889. [239] G.-L. Chai, K. Qiu, M. Qiao, M.-M. Titirici, C. Shang, Z. Guo, Energy Environ. Sci. 10 (2017) 1186–1195. [240] K. Qiu, G. Chai, C. Jiang, M. Ling, J. Tang, Z. Guo, ACS Catal. 6 (2016) 3558–3568. [241] M.-C. Tsai, T.-T. Nguyen, N.G. Akalework, C.-J. Pan, J. Rick, Y.-F. Liao, et al., ACS Catal. 6 (2016) 6551–6559. [242] N. Wang, B. Lu, L. Li, W. Niu, Z. Tang, X. Kang, S. Chen, ACS Catal. 8 (2018) 6827–6836. [243] Y. Ye, H. Li, F. Cai, C. Yan, R. Si, S. Miao, Y. Li, G. Wang, X. Bao, ACS Catal. 7 (2017) 7638–7646. [244] Z. Zhao, Z. Xia, ACS Catal. 6 (2016) 1553–1558. [245] S. Yasuda, A. Furuya, Y. Uchibori, J. Kim, K. Murakoshi, Adv. Funct. Mater. 26 (2016) 738–744. [246] L. Ye, G. Chai, Z. Wen,Adv. Funct. Mater. 27 (2017), 1606190. [247] H. Yu, L. Shang, T. Bian, R. Shi, G.I.N. Waterhouse, Y. Zhao, et al., Adv. Mater. 28 (2016) 5080–5086. [248] Z. Zhao, M. Li, L. Zhang, L. Dai, Z. Xia, Adv. Mater. 27 (2015) 6834–6840. [249] J. Zhang, L. Qu, G. Shi, J. Liu, J. Chen, L. Dai, Angew. Chem. Int. Ed. 55 (2016) 2230–2234. [250] T. Zhou, Y. Du, S. Yin, X. Tian, H. Yang, X. Wang, et al., Energy Environ. Sci. 9 (2016) 2563–2570. ´ J. Lee, Z. Wang, S. Liu, et al., J. Am. Chem. Soc. 139 [251] J. Zhang, Y. Song, M. Kopec, (2017) 12931–12934. [252] Q. Jia, Z. Zhao, L. Cao, J. Li, S. Ghoshal, V. Davies, et al., Nano Lett. 18 (2018) 798–804. [253] J. Lim, H. Shin, M. Kim, H. Lee, K.-S. Lee, Y. Kwon, et al., Nano Lett. 18 (2018) 2450–2458. [254] A. Zitolo, N. Ranjbar-Sahraie, T. Mineva, J. Li, Q. Jia, S. Stamatin, et al., Nat. Commun. 8 (2017). [255] X. Zheng, Y. Ji, J. Tang, J. Wang, B. Liu, H.-G. Steinrück, et al., Nat. Catal. 2 (2019) 55–61. [256] T.-T. Zhuang, Z.-Q. Liang, A. Seifitokaldani, Y. Li, P. De Luna, T. Burdyny, et al., Nat. Catal. 1 (2018) 421–428. [257] J.K. Norskov, F. Abild-Pedersen, F. Studt, T. Bligaard, Proc. Natl. Acad. Sci. 108 (2011) 937–943. [258] Y. Chen, S. Ji, Y. Wang, J. Dong, W. Chen, Z. Li, et al., Angew. Chem. Int. Ed. 56 (2017) 6937–6941. [259] Y. Han, Y. Wang, R. Xu, W. Chen, L. Zheng, A. Han, et al., Energy Environ. Sci. (2018). [260] H. Shen, E. Gracia-Espino, J. Ma, K. Zang, J. Luo, L. Wang, et al., Angew. Chem. Int. Ed. 56 (2017) 13800–13804. [261] A. Wang, J. Li, T. Zhang, Nat. Rev. Chem. (2018). [262] Y. Chen, S. Ji, C. Chen, Q. Peng, D. Wang, Y. Li, Joule 2 (2018) 1242–1264. [263] H. Ren, Y. Wang, Y. Yang, X. Tang, Y. Peng, H. Peng, et al., ACS Catal. 7 (2017) 6485–6492. [264] M. Xiao, J. Zhu, L. Ma, Z. Jin, J. Ge, X. Deng, et al., ACS Catal. 8 (2018) 2824–2832. [265] X. Zeng, J. Shui, X. Liu, Q. Liu, Y. Li, J. Shang, et al.,Adv. Energy Mater. 8 (2018), 1701345. [266] P. Song, M. Luo, X. Liu, W. Xing, W. Xu, Z. Jiang, et al.,Adv. Funct. Mater. 27 (2017), 1700802. [267] A. Han, W. Chen, S. Zhang, M. Zhang, Y. Han, J. Zhang, et al.,Adv. Mater. 30 (2018), 1706508. [268] X.X. Wang, D.A. Cullen, Y.-T. Pan, S. Hwang, M. Wang, Z. Feng, et al.,Adv. Mater. 30 (2018), 1706758. [269] Q. Zhang, X.-X. Qin, F.-P. Duan-Mu, H.-M. Ji, Z.-R. Shen, X.-P. Han, et al., Angew. Chem. Int. Ed. 57 (2018) 9351–9356. [270] Z. Zhang, J. Sun, F. Wang, L. Dai, Angew. Chem. Int. Ed. 57 (2018) 9038–9043. [271] Y. Han, Y.-G. Wang, W. Chen, R. Xu, L. Zheng, J. Zhang, et al., J. Am. Chem. Soc. 139 (2017) 17269–17272. [272] H. Zhang, S. Hwang, M. Wang, Z. Feng, S. Karakalos, L. Luo, et al., J. Am. Chem. Soc. 139 (2017) 14143–14149.

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[273] B. Wang, X. Wang, J. Zou, Y. Yan, S. Xie, G. Hu, et al., Nano Lett. 17 (2017) 2003–2009. [274] Q. Lai, L. Zheng, Y. Liang, J. He, J. Zhao, J. Chen, ACS Catal. 7 (2017) 1655–1663. [275] C. Tang, H.-F. Wang, X. Chen, B.-Q. Li, T.-Z. Hou, B. Zhang, et al., Adv. Mater. 28 (2016) 6845–6851. [276] L. Bu, N. Zhang, S. Guo, X. Zhang, J. Li, J. Yao, et al., Science 354 (2016) 1410–1414. [277] A. Khorshidi, J. Violet, J. Hashemi, A.A. Peterson, Nat. Catal. 1 (2018) 263–268. [278] C. Meng, T. Ling, T.-Y. Ma, H. Wang, Z. Hu, Y. Zhou, et al.,Adv. Mater 29 (2017), 1604607. [279] C. Meng, T. Ling, T.-Y. Ma, H. Wang, Z. Hu, Y. Zhou, et al.,Adv. Mater. 29 (2017), 1604607. [280] P. Yin, T. Yao, Y. Wu, L. Zheng, Y. Lin, W. Liu, et al., Angew. Chem. Int. Ed. 55 (2016) 10800–10805. [281] X. Duan, J. Xu, Z. Wei, J. Ma, S. Guo, S. Wang, et al.,Adv. Mater. 29 (2017), 1701784. [282] D. Voiry, H.S. Shin, K.P. Loh, M. Chhowalla, Nat. Rev. Chem. 2 (2018) 0105. [283] J. Yang, Y. Guo, W. Lu, R. Jiang, J. Wang,Adv. Mater. 30 (2018), 1802227. [284] S. Nitopi, E. Bertheussen, S.B. Scott, X. Liu, A.K. Engstfeld, S. Horch, et al., Chem. Rev. (2019), acs.chemrev.8b00705. [285] S. Sorcar, Y. Hwang, C.A. Grimes, S.-I. In, Mater. Today 20 (2017) 507–515. [286] J. Liu, C. Guo, A. Vasileff, S. Qiao,Small Methods 1 (2017), 1600006. [287] Y. Zheng, A. Vasileff, X. Zhou, Y. Jiao, M. Jaroniec, S.-Z. Qiao, J. Am. Chem. Soc. 141 (2019) 7646–7659. [288] H.B. Yang, S.-F. Hung, S. Liu, K. Yuan, S. Miao, L. Zhang, et al., Nat. Energy 3 (2018) 140–147. [289] E.L. Clark, C. Hahn, T.F. Jaramillo, A.T. Bell, J. Am. Chem. Soc. 139 (2017) 15848–15857. [290] J. Xie, X. Zhao, M. Wu, Q. Li, Y. Wang, J. Yao, Angew. Chem. Int. Ed. (2018). [291] J. Fu, B. Zhu, C. Jiang, B. Cheng, W. You, J. Yu,Small 13 (2017), 1603938. [292] A. Zhang, R. He, H. Li, Y. Chen, T. Kong, K. Li, et al., Angew. Chem. Int. Ed. (2018). [293] C. Yan, H. Li, Y. Ye, H. Wu, F. Cai, R. Si, et al., Energy Environ. Sci. 11 (2018) 1204–1210. [294] C. Gao, S. Chen, Y. Wang, J. Wang, X. Zheng, J. Zhu, L. Song, W. Zhang, Y. Xiong,Adv. Mater. 30 (2018), 1704624. [295] X. Wang, Z. Chen, X. Zhao, T. Yao, W. Chen, R. You, et al., Angew. Chem. Int. Ed. 57 (2018) 1944–1948. [296] S.B. Varandili, J. Huang, E. Oveisi, G.L. De Gregorio, M. Mensi, M. Strach, et al., ACS Catal. (2019) 5035–5046. [297] Z. Gu, N. Yang, P. Han, M. Kuang, B. Mei, Z. Jiang, et al.,Small Methods (2018), 1800449. [298] J. Zhang, R. Yin, Q. Shao, T. Zhu, X. Huang, Angew. Chem. Int. Ed. 58 (2019) 5609–5613. [299] Y. Wang, P. Han, X. Lv, L. Zhang, G. Zheng, Joule 2 (2018) 2551–2582. [300] Y.-X. Liu, H.-H. Wang, T.-J. Zhao, B. Zhang, H. Su, Z.-H. Xue, et al., J. Am. Chem. Soc. 141 (2019) 38–41. [301] Y.-X. Duan, F.-L. Meng, K.-H. Liu, S.-S. Yi, S.-J. Li, J.-M. Yan, Q. Jiang,Adv. Mater. 30 (2018), 1706194. [302] X. Feng, K. Jiang, S. Fan, M.W. Kanan, J. Am. Chem. Soc. 137 (2015) 4606–4609. [303] X. Feng, K. Jiang, S. Fan, M.W. Kanan, ACS Cent. Sci. 2 (2016) 169–174. [304] A. Verdaguer-Casadevall, C.W. Li, T.P. Johansson, S.B. Scott, J.T. McKeown, M. Kumar, et al., J. Am. Chem. Soc. 137 (2015) 9808–9811. [305] C.W. Li, J. Ciston, M.W. Kanan, Nature 508 (2014) 504–507. [306] C. Choi, T. Cheng, M. Flores Espinosa, H. Fei, X. Duan, W.A. Goddard, Y. Huang,Adv. Mater. (2018), 1805405. [307] M.G. Kibria, C.-T. Dinh, A. Seifitokaldani, P. De Luna, T. Burdyny, R. Quintero-Bermudez, et al.,Adv. Mater. 30 (2018), 1804867. [308] W. Wang, L. Shang, G. Chang, C. Yan, R. Shi, Y. Zhao, et al.,Adv. Mater. 31 (2019), 1808276. [309] X. Zu, X. Li, W. Liu, Y. Sun, J. Xu, T. Yao, et al.,Adv. Mater. 31 (2019), 1808135. [310] H. Mistry, Y.-W. Choi, A. Bagger, F. Scholten, C.S. Bonifacio, I. Sinev, et al., Angew. Chem. Int. Ed. 56 (2017) 11394–11398. [311] X. Zhang, X. Sun, S.-X. Guo, A.M. Bond, J. Zhang, Energy Environ. Sci. 12 (2019) 1334–1340. [312] X. Zheng, P. De Luna, F.P. García de Arquer, B. Zhang, N. Becknell, M.B. Ross, et al., Joule 1 (2017) 794–805. [313] S.Y. Lee, H. Jung, N.-K. Kim, H.-S. Oh, B.K. Min, Y.J. Hwang, J. Am. Chem. Soc. (2018). [314] Z.-Q. Liang, T.-T. Zhuang, A. Seifitokaldani, J. Li, C.-W. Huang, C.-S. Tan, et al., Nat. Commun. 9 (2018) 3828. [315] F. Jiao, B. Xu,Adv. Mater. (2018), 1805173. [316] S.L. Foster, S.I.P. Bakovic, R.D. Duda, S. Maheshwari, R.D. Milton, S.D. Minteer, et al., Nat. Catal. 1 (2018) 490–500. [317] L. Hu, A. Khaniya, J. Wang, G. Chen, W.E. Kaden, X. Feng, ACS Catal. (n.d.) 27. [318] Y. Liu, Y. Su, X. Quan, X. Fan, S. Chen, H. Yu, et al., ACS Catal. 8 (2018) 1186–1191. [319] Y. Zhang, W. Qiu, Y. Ma, Y. Luo, Z. Tian, G. Cui, et al., ACS Catal. (2018) 8540–8544. [320] D. Bao, Q. Zhang, F.-L. Meng, H.-X. Zhong, M.-M. Shi, Y. Zhang, et al.,Adv. Mater. 29 (2017), 1604799. [321] L. Zhang, X. Ji, X. Ren, Y. Ma, X. Shi, Z. Tian, et al.,Adv. Mater 30 (2018), 1800191. [322] Y. Zhao, Y. Zhao, G.I.N. Waterhouse, L. Zheng, X. Cao, F. Teng, et al.,Adv. Mater. 29 (2017), 1703828.

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[323] C. Li, T. Wang, Z.-J. Zhao, W. Yang, J.-F. Li, A. Li, et al., Angew. Chem. Int. Ed. 57 (2018) 5278–5282. [324] Q. Wang, Y. Lei, D. Wang, Y. Li, Energy Environ. Sci. (2019), 10.1039.C8EE03781G. [325] L. Wang, M. Xia, H. Wang, K. Huang, C. Qian, C.T. Maravelias, G.A. Ozin, Joule 2 (2018) 1055–1074. [326] S.L. Foster, S.I.P. Bakovic, R.D. Duda, S. Maheshwari, R.D. Milton, S.D. Minteer, et al., Nat. Catal. 1 (2018) 490–500. [327] H. Liu, L. Wei, F. Liu, Z. Pei, J. Shi, Z. Wang, et al., ACS Catal. (2019) 5245–5267. [328] X. Cui, C. Tang, Q. Zhang,Adv. Energy Mater. 8 (2018), 1800369. [329] Y. Gong, J. Wu, M. Kitano, J. Wang, T.-N. Ye, J. Li, et al., Nat. Catal. 1 (2018) 178–185. [330] C. Guo, J. Ran, A. Vasileff, S.-Z. Qiao, Energy Environ. Sci. 11 (2018) 45–56. [331] L. Wang, M. Xia, H. Wang, K. Huang, C. Qian, C.T. Maravelias, et al., Joule 2 (2018) 1055–1074. [332] W. Qiu, X.-Y. Xie, J. Qiu, W.-H. Fang, R. Liang, X. Ren, et al., Nat. Commun. 9 (2018). [333] D. Zhu, L. Zhang, R.E. Ruther, R.J. Hamers, Nat. Mater. 12 (2013) 836–841. [334] Y. Zhao, Y. Zhao, G.I.N. Waterhouse, L. Zheng, X. Cao, F. Teng, et al.,Adv. Mater. 29 (2017), 1703828. [335] Y. Zhao, Y. Zhao, R. Shi, B. Wang, G.I.N. Waterhouse, L. Wu, et al.,Adv. Mater. 31 (2019), 1806482. [336] C. Li, T. Wang, Z.-J. Zhao, W. Yang, J.-F. Li, A. Li, et al., Angew. Chem. Int. Ed. 57 (2018) 5278–5282. [337] J. Peng, X. Chen, W.-J. Ong, X. Zhao, N. Li, Chemistry 5 (2019) 18–50. [338] J. Zheng, Y. Lyu, M. Qiao, R. Wang, Y. Zhou, H. Li, et al., Chemistry 5 (2019) 617–633. [339] H. Jia, A. Du, H. Zhang, J. Yang, R. Jiang, J. Wang, C. Zhang, J. Am. Chem. Soc. 141 (2019) 5083–5086. [340] K. Ithisuphalap, H. Zhang, L. Guo, Q. Yang, H. Yang, G. Wu,Small Methods (2018), 1800352. [341] Y. Wan, J. Xu, R. Lv, Mater. Today (2019), S136970211930001X. [342] Y. Tanabe, Y. Nishibayashi, Coord. Chem. Rev. 257 (2013) 2551–2564. [343] B.H.R. Suryanto, H.-L. Du, D. Wang, J. Chen, A.N. Simonov, D.R. MacFarlane, Nat. Catal. 2 (2019) 290–296. [344] J. Chen, H. Wang, Z. Wang, S. Mao, J. Yu, Y. Wang, et al., ACS Catal. (2019) 5302–5307. [345] L. Hu, A. Khaniya, J. Wang, G. Chen, W.E. Kaden, X. Feng, ACS Catal. 8 (2018) 9312–9319. [346] L. Li, C. Tang, B. Xia, H. Jin, Y. Zheng, S.-Z. Qiao, ACS Catal. 9 (2019) 2902–2908. [347] Y. Fang, Z. Liu, J. Han, Z. Jin, Y. Han, F. Wang, et al.,Adv. Energy Mater. 9 (2019), 1803406. [348] H. Cheng, L.-X. Ding, G.-F. Chen, L. Zhang, J. Xue, H. Wang,Adv. Mater. 30 (2018), 1803694. [349] S. Zhao, X. Lu, L. Wang, J. Gale, R. Amal,Adv. Mater. 31 (2019), 1805367. [350] L. Han, X. Liu, J. Chen, R. Lin, H. Liu, F. Lu, et al., Angew. Chem. (2018), ange.201811728. [351] H. Tao, C. Choi, L.-X. Ding, Z. Jiang, Z. Han, M. Jia, et al., Chemistry 5 (2019) 204–214. [352] X. Yu, P. Han, Z. Wei, L. Huang, Z. Gu, S. Peng, et al., Joule 2 (2018) 1610–1622. [353] Y. Liu, M. Han, Q. Xiong, S. Zhang, C. Zhao, W. Gong, et al.,Adv. Energy Mater. 9 (2019), 1803935. [354] Y.-C. Hao, Y. Guo, L.-W. Chen, M. Shu, X.-Y. Wang, T.-A. Bu, et al., Nat. Catal. (2019). [355] X. Yu, P. Han, Z. Wei, L. Huang, Z. Gu, S. Peng, J. Ma, G. Zheng, Joule (2018). [356] Y. Liu, Y. Su, X. Quan, X. Fan, S. Chen, H. Yu, et al., ACS Catal. 8 (2018) 1186–1191. [357] C. Zhao, S. Zhang, M. Han, X. Zhang, Y. Liu, W. Li, et al., ACS Energy Lett. 4 (2019) 377–383. [358] S. Mukherjee, D.A. Cullen, S. Karakalos, K. Liu, H. Zhang, S. Zhao, et al., Nano Energy 48 (2018) 217–226. [359] Y. Wang, X. Cui, J. Zhao, G. Jia, L. Gu, Q. Zhang, et al., ACS Catal. 9 (2019) 336–344. [360] W. Zhao, L. Zhang, Q. Luo, Z. Hu, W. Zhang, S. Smith, J. Yang, ACS Catal. 9 (2019) 3419–3425. [361] L.M. Azofra, N. Morlanés, A. Poater, M.K. Samantaray, B. Vidjayacoumar, et al., Angew. Chem. 130 (2018) 16038–16042. [362] C. Liu, Q. Li, C. Wu, J. Zhang, Y. Jin, D.R. MacFarlane, C. Sun, J. Am. Chem. Soc. 141 (2019) 2884–2888. [363] Q. Qin, T. Heil, M. Antonietti, M. Oschatz,Small Methods 2 (2018), 1800202. [364] L. Han, X. Liu, J. Chen, R. Lin, H. Liu, F. Lü, et al., Angew. Chem. Int. Ed. 58 (2019) 2321–2325. [365] C. Lv, Y. Qian, C. Yan, Y. Ding, Y. Liu, G. Chen, G. Yu, Angew. Chem. Int. Ed. (2018). [366] S.-J. Li, D. Bao, M.-M. Shi, B.-R. Wulan, J.-M. Yan, Q. Jiang,Adv. Mater. 29 (2017), 1700001. [367] H. Hirakawa, M. Hashimoto, Y. Shiraishi, T. Hirai, J. Am. Chem. Soc. 139 (2017) 10929–10936.

[368] M.-M. Shi, Adv. Energy Mater. (2018) 6. [369] S.-J. Li, D. Bao, M.-M. Shi, B.-R. Wulan, J.-M. Yan, Q. Jiang,Adv. Mater. 29 (2017), 1700001. [370] Y. Fang, Z. Liu, J. Han, Z. Jin, Y. Han, F. Wang, et al.,Adv. Energy Mater. 9 (2019), 1803406. [371] M.-M. Shi, D. Bao, S.-J. Li, B.-R. Wulan, J.-M. Yan, Q. Jiang,Adv. Energy Mater. 8 (2018), 1800124. [372] M.-M. Shi, D. Bao, B.-R. Wulan, Y.-H. Li, Y.-F. Zhang, J.-M. Yan, Q. Jiang,Adv. Mater. 29 (2017), 1606550. [373] M. Wang, S. Liu, T. Qian, J. Liu, J. Zhou, H. Ji, J. Xiong, J. Zhong, C. Yan, Nat. Commun. 10 (2019) 341. [374] P. Chen, N. Zhang, S. Wang, T. Zhou, Y. Tong, C. Ao, et al., Proc. Natl. Acad. Sci. 116 (2019) 6635–6640. [375] C. Chen, D. Yan, Y. Wang, Y. Zhou, Y. Zou, Y. Li, S. Wang,Small 15 (2019), 1805029. [376] Q. Qin, T. Heil, M. Antonietti, M. Oschatz,Small Methods 2 (2018), 1800202. Shilong Jiao received his Ph.D. degree in material science from Shandong University. He is currently a Postdoc fellow in Shenzhen University. His current research interests includes the preparation and characterization of nanocatalyts with various defects and the relationship between the structural imperfections and the electrocatalytic performance of renewable energy-related reactions.

fx3Xianwei Fu received her B.S. degree from Shanxi Datong University, Datong, China, in 2013, and Ph.D. degree from Shandong University, Jinan, China, in 2018. Now, she is a full postdoc fellow in Hunan University. Her research interests are focused on fabrication and characterization of the low-dimensional materials based optoelectronic devices. Li Zhang received her bachelor’s degree in materials science and engineering from Yancheng Institute of Technology in 2018. She is now pursuing her master’s degree in Hunan University under the supervision of Prof. Hongwen Huang. Her research interests include energy materials and electrocatalysis.

Yu-Jia Zeng is a professor at Shenzhen University. He received his bachelor’s degree in materials science and engineering from Zhejiang University and his Ph.D. degree in materials physics and chemistry from Zhejiang University. After graduation, he worked at the Department of Physics of KU Leuven as a postdoctoral fellow and a research associate. His research interests include lowdimensional materials, in particular semiconductors, for optoelectronics and spintronics.

Hongwen Huang is now a full professor at Hunan University. He received his bachelor’s degree in materials science and engineering from South China University of Technology in 2009 and his Ph.D. degree in materials science and engineering from Zhejiang University in 2015. From 2012–2014, he studied in Georgia Institute of Technology under the supervision of Prof. Younan Xia. After graduation, he worked at the University of Science and Technology of China as a postdoctoral fellow from 2015 to 2017 and joined Hunan University in 2017. His research interests include the controlled growth of nanocrystals and their applications in energy-related electrocatalysis.

Please cite this article as: S. Jiao, X. Fu, L. Zhang et al., Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections, Nano Today, https://doi.org/10.1016/j.nantod.2019.100833