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Mechanism of surface effect and selective catalytic performance of MnO2 nanorod: DFT+U study
Applied Surface Science 420 (2017) 205–213
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Mechanism of surface effect and selective catalytic performance of MnO2 nanorod: DFT+U study Zheng Chen a , Guifa Li a,b,∗ , Haizhong Zheng a , Xiaoyong Shu a , Jianping Zou a , Ping Peng b a b
Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Jiangxi, 330063, China School of Material Science and Engineering, Hunan University, Hunan 410082, China
a r t i c l e
i n f o
Article history: Received 13 February 2017 Received in revised form 15 May 2017 Accepted 16 May 2017 Available online 17 May 2017 Keywords: MnO2 nanorod Surface nanometer Selective catalysis Microcosmic mechanism First-principles
a b s t r a c t The mechanism of surface effect and selective catalytic performance of MnO2 nanorods remain mysterious at present. Using first-principles pseudo-potential plane wave method, the surface energy, cohesive energy, geometrical and electronic structure for MnO2 in the evolution of crystal → bulk surface → nanorod morphologies have been systematically calculated and analyzed. The results show that the surface energy is increased along with the decrease of geometry configuration in crystal → bulk surface → nanorod as a whole. However in three nanorod morphologies, the surface energy is increased along with the additional geometry configuration, wherein the largest nano(III) has the largest surface energy and lowest cohesive energy. These characters are originated from their changes in geometry structure and lost in Mulliken charges of atoms along surface planes. Electronic structure shows that the selective catalytic activity of MnO2 nanorods is originated from their unique states of valence electrons, which occupying the highest occupied molecular orbital (HOMO) only appears on (110) Miller surface layers and the lowest unoccupied molecular orbital (LUMO) only on (100) Miller surface layers, respectively. Such attracting phenomenon has significantly difference with that of MnO2 bulk surface. Thereinto, a transitional model of [(100 × 110)] microfacet model is found to exhibit much more approaching surface performance to MnO2 nanometer structure somewhere. Thus, our findings open an avenue for detailed and comprehensive studies on the growth and catalysis of MnO2 nanomaterials. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Environmental pollution resulting from economic development has attracted the attention of many researchers due to its effect on the quality of life [1]. Reducing the toxicity of pollutants is a major problem facing the world. One of the potential materials typically suggested as an absorbent for pollutants is manganese dioxide (MnO2 ), which theoretically has high capacitance, low cost, environmental compatibility, and naturally abundant. These properties are used when MnO2 is employed in electrochemical capacitors and Li-ion battery cathodes [2]. Furthermore, as a good selective catalytic reduction (SCR) agent, it can be used in many oxygen reduction reaction (ORR) processes, such as the oxidation of ammonia with NO [3], SOx [4], and methane in the presence of visible
∗ Corresponding author at: Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Jiangxi, 330063, China. E-mail addresses: lgf [email protected], [email protected] (G. Li). http://dx.doi.org/10.1016/j.apsusc.2017.05.141 0169-4332/© 2017 Elsevier B.V. All rights reserved.
light or water [2]. It can also be put to use in the degradation of organic pollutants such as methylene blue [5], benzyl alcohol [6], and polyamide [7]. Such excellent catalytic activity arises from the unique redox couple Mn3+ /Mn4+ presenting on the surface and its tunnel-like structure. In addition, MnO2 ’s special configuration allows another important use in the form of reduction for heavy metals in environmental pollutants, such as Zn, Cu, Ni, Co [8], and Hg [9]. However, its poor conductivity and the constrained activity of the atoms along the surface have a detrimental effect on both the utilization of the catalyst and the rate of reaction. Thus, many methods have been used to improve the structure, stability, and surface reaction rate. For example, carbon nanotubes (CNTs) [10] and graphene [11] are used to form composites with MnO2 . Doping Ag [12], Au [13], Co [14], and Al [15] into MnO2 is an excellent method of enhancing its chemical properties. And nanotechnology is another effective and straightforward technique to promote the catalytic activity of a material by improving its surface structure, surface active sites, and surface area, all of which have a direct bearing on the crystal surface, crystal planes, and stereochemistry of the catalytic process. Due to the greatly enhanced surface
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reaction rates of nanostructured MnO2 compared with the bulk material surface, the former has attracted the attention of many researchers. In addition to enhancements in the catalytic properties, the absorption capability of nanomaterials is 5–10 times that of micron size counterparts [16]. Huang et al. [17] found that the efficiency of removal of NO was affected by the content of magnesium in doped nanostructured manganese oxides, wherein the best results were obtained for samples doped with 10 wt% magnesium, while the worst was for 5 wt%. Also, different nanoscale configurations of MnO2 were synthesized by a hydrothermal method to exhibit different catalytic effects. All of such variations originate from the surface mechanisms, especially due to nanoscale effects. These surface mechanisms are not yet fully understood, and theoretical research is vital in elucidating the effect of microstructure on the mechanisms. For example, Crespo et al. [2] discovered the special absorbing style of oxygen on ␣-MnO2 through O2 molecule instead of single O atom, which was the primary condition for lithium-air batteries. Zhang et al. [18] studied the adsorption ability and adsorption structures of mercury species (Hg, HgCl and HgCl2 ) on MnO2 , and described their desorption mechanisms. Loganathan et al. [19] studied the influence of pH on the absorption ability of Ni on ␦-MnO2 . It is well known that the surface structure and surface area are the main elements influencing the adsorption ability of MnO2 material. However, questions remain as to what the internal differences are between bulk and nanostructured MnO2 , the relationship between these different forms, and the optimal configuration for nanorod MnO2 . Why MnO2 have selective catalytic activity for Li2 O2 by (100) Miller surface layer [2] and As5+ by (110) Miller surface layer [20], respectively. Such questions are difficult to answer in experiment. In this paper, we attempt to illuminate the evolution mechanisms of the electronic structure for bulk and
nanorod MnO2 , and reveal the selective catalysis mechanism of MnO2 . This can be considered a first and vital step in understanding the unusual properties of nanoscale MnO2 . 2. Simulation models and method In order to illuminate the surface effect of the nanoscale material, several models of MnO2 in topological configurations of crystal, bulk surface and nanorod were constructed and studied systematically by their stiochiometric proportion. In the above constructs, the surface area increases from zero to a very high value. Based on the research of Tompsett et al. [21], only two prominent and stable Miller index planes, such as (100) and (110), were considered. In this study, the nanorod models were constructed following the results of experiments by Zhang et al. [22] and Wulff [21]. A transitional [(100 × 110)] microfacet structure only composed by (100) and (110) Miller indices was built to link the bulk surface and nanorod morphologies. All of the simulation models are shown in Fig. 1. For (100) and (110) bulk surfaces there exist twice units of (100) and (110) Miller index slabs in Fig. 1(b) and (c), respectively. About nanorod models, all of them were also combined by (110) and (100) Miller index slabs in Fig. 1(e–f). The smallest nanorod, consisting of four units of the (100) and (110) Miller index slabs, is labeled as nano(I) in Fig. 1(e). The second MnO2 nanorod (nano(II)) contains eight units of the (100) and four units of the (110) Miller index slabs (Fig. 1(f)). The largest MnO2 nanorod in this paper is labeled as nano(III), and is modeled with eight units of each (100) and (110) Miller index slabs to construct a Mn112 O224 supercell (Fig. 1(g)). This last model is the largest possible based on the capability of our computer cluster. All of these primitive cells of the nanorods can be repeated to make larger supercells by using their
Fig. 1. Simulated several morphologies of MnO2 models, wherein (a) MnO2 crystal (Mn8 O16 ) (b) (100) bulk surface (Mn32 O64 ) (c) (110) bulk surface (Mn16 O32 ) (d) [(100 × 110)] microfacet (Mn32 O64 ) (e) nano(I) (Mn28 O56 ) (f) nano(II) (Mn68 O136 ) (g) nano(III) (Mn112 O224 ).
Z. Chen et al. / Applied Surface Science 420 (2017) 205–213 Table 1 Predicted PBE + U, experimental and theoretical lattice parameters for ␣-MnO2. ␣-MnO2
a(Å)
b(Å)
c(Å)
This work Ref. [21] Exp. [26]
9.922 9.907 9.750
9.922 9.907 9.750
2.904 2.927 2.861
the coefficient of 2 is not necessary. The cohesive energy is representative of the work required for a crystal to be decomposed into atoms, which in turn denotes the stability of the respective simulation model. Herein, the Ecohesive of several MnO2 models have been calculated from the following equation: Ecohesive =
periodic boundary conditions and transitional symmetry. The aim of such constructions is to quantify the surface effect of different Miller indices over several models. All of the bulk surface models were calculated based on slabs with a minimum thickness of 14 Å, and a vacuum thickness greater than 12 Å was used throughout to separate the slabs from their periodic image for bulk surface and nanorod models. All of the above simulation models were relaxed via the following process: a first-principles pseudopotential plane-wave method, based on density functional theory, was implemented in the CASTEP code [23]. The electronic structure was calculated using the Generalized Gradient Approximation (GGA) of Perdew, Burke, and Ernzerh with Hubbard U corrections (PBE + U) [24]. The PBE + U exchange–correlation functional has been demonstrated to give a good description of defect properties in MnO2 [21]. All calculations were performed in a ferromagnetic spin polarized configuration, while effects of more complex magnetic orders were left for future work due to their low energy scale. All subsequent calculations were performed based on the equilibrium lattice constants obtained without cell relaxation using a cutoff of 450 eV. This included the recalculation of the energy for the bulk unit cell so that all comparative energies were obtained. A minimum of 2*1*1 k-points was used in the Brillouin zone of the conventional cell and scaled appropriately for supercells. All atomic positions in these primitive cells were relaxed according to the total energy and force using the BFGS scheme [25], based on the cell optimization criteria (RMS force of 0.03 eV/Å, a stress of 0.01 GPa, and displacement of 0.002 Å). The convergence criteria of self-consistent field (SCF) and energy tolerances were set at 1.0 × 10−4 and 5.0 × 10−4 eV/atom, respectively. The value of the U parameter for our PBE + U calculations was determined by ab initio calculations. Previous work conducted by some of the present authors [2] demonstrated that a good description of structural stability, band gaps and magnetic interactions can be obtained when PBE + U was applied in the fully-localized limit, which is therefore used in this work as well. We employ U = 1.6 eV. The ␣-MnO2 crystal occurs in the tetragonal space group I4/m with lattice parameters a = b = 9.775 Å and c = 2.904 Å. In Table 1, we show the calculated lattice parameters for ␣-MnO2 from PBE + U. These results agree with the experimental parameters within 1.8%, but the common tendency for PBE + U to overestimate the unit cell volume is evident. 3. Results and discussion 3.1. Structural stability In Table 2, we show the surface energy Esurface and cohesive energy Ecohesive for ␣-MnO2 obtained via PBE + U calculations. The surface energy is calculated by taking the difference between the energy of a constructed slab and the same number of ␣-MnO2 formula units in the bulk: Esurface =
Etotal − nEb 2·S
(1)
where Etotal is the energy of a surface or nanorod model containing n formula crystal units, and Eb is the total energy of the ␣-MnO2 crystal. S is the area of the bulk surface, and the coefficient of 2 indicates that each bulk surface model has two surfaces. For nanorods,
207
1 Mn O − mEgas ) (E Mnl Om − lEgas l + m total
(2)
wherein l and m represent the number of Mn and O atoms in Mnl Om denotes the total the respective morphologies of MnO2 . Etotal Mn and E O are the energies of energy of the Mnl Om models. Egas gas the gaseous Mn and O atoms, respectively. Before optimizing the gaseous atoms, we constructed a 10 × 10 × 10 (Å3 ) vacuum box and put a single atom, such as Mn or O in the center of the box to be relaxed to get its global energy minimum. The results are given as Mn = −588.1855 eV and E O = −432.2548 eV in Table 2. Egas gas From Table 2, it can be seen that the Esurface values of the (100) and (110) bulk surfaces are equal to 0.6503 Jm−2 and 0.6794 Jm−2 , respectively, which are similar to the results reported by Tompsett et al. (0.64 Jm−2 and 0.75 Jm−2 , respectively) [21]. However, the surface energy of the [(100 × 110)] microfacet model is equal to 5.0437 Jm−2 , which is the largest value found in all the simulation models. Due to the much greater surface/bulk ratio ␣ (␣ = S/V, wherein S is the surface area and V is the volume, and ␣nano(I) = 0.2341 as listed in Table S1) of the smallest nanorod model (nano(I) as seen in Fig. 1(e)), its surface energy decreases to 1.7040 Jm−2 , although the ␣ value remains larger than that of the (100) or (110) bulk surface models. In the larger nanorod model with eight units of (100) and four units of (110) Miller index slabs (nano(II)), the surface energy increases to 2.4367 Jm−2 . Finally, the surface energy of nano(III), which has eight units of each (100) and (110) Miller index slabs combined to form a Mn112 O224 structure, is equal to 3.0323 Jm−2 . When the evolution of the surface energies is compared systematically, it is found that the Esurface of the bulk surface is smaller than that of the nanorods, a trend that originates from the former’s smaller surface/bulk ratio. However, to our surprise, the surface energy of nano(III) (Esurface = 3.0323 eV) is larger than that of the smallest nano(I) (Esurface = 1.7040 eV), but the surface/bulk ratio ␣ of the former (␣nano(III) = 0.1172, as shown in Table S1) is smaller than that of the latter. As well known, a larger surface energy corresponds to a much more active surface. As such, the surface activity of the MnO2 nanorods should increase with increasing dimensions. Such evolution is contrary to what can be expected based on their decreasing surface/bulk ratios. This trend can be extended to infer that when the dimension of a nanorod increases to approach infinity, such that it resembles the bulk surface with the smallest surface/bulk ratio, it will have the highest surface energy. Such a conclusion would be erroneous. Following the evolution of surface energy presented in Fig. 2, we can predict that there exists a nano(X), which would have largest surface energy, and thus express the best surface activity. Furthermore, the cohesive energies Ecohesive of the crystal, bulk surface and nanorod models were calculated to estimate their structure stability, and the results are given in Table 2. It is found that the Ecohesive of the MnO2 crystal is equal to −4.7223 eV, which is the smallest value in this study. The next lowest value is Ecohesive = −4.6735 eV of the (100) bulk surface, whose Ecohesive is smaller than that of (110) bulk surface by 0.0248 eV, with their trend being similar to that seen for surface energy. For the [(100 × 110)] microfacet model, Ecohesive further increases to −4.2598 eV, which means its structural stability sharply decreasing compared to similar configurations of the (100) and (110) bulk surfaces. Similarly, it is found that the cohesive energy of nano(I) is equal to −4.2188 eV, which is the largest one among nanorods. With the geometry dimensions of the nanorod increasing, the cohesive energy decreases slowly. The Ecohesive of nano(II) and nano(III)
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Table 2 Surface energy (Esurface ) and cohesive energy (Ecohesive ) of MnO2 crystal, bulk surface and nanorod models.
Crystal Bulk surface
Nanorod
Etotal (eV)
a (Å)
b (Å)
S (Å2 )
Esurface (eV)
Esurface (Jm−2 )
Ecohesive (eV)
Mn8 O16 (100)surface (110)surface [(100 × 110)] microfacet nano(I) nano(II) nano(III)
−11734.8960 −46934.8997 −23466.2580 −11734.8960 −41029.8368 −99651.0223 −164137.7267
– 2.9040 2.9040 2.9040 2.9040 2.9040 2.9040
– 19.8440 14.3292 24.2512 68.3480 108.0360 137.0160
– 57.6265 41.6116 70.4255 198.4811 313.7342 397.8914
0 4.6843 3.5340 44.4003 42.2991 95.5937 150.8172
0 0.6503 0.6794 5.0437 1.7040 2.4376 3.0323
−4.7223 −4.6735 −4.6487 −4.2598 −4.2188 −4.2537 −4.2735
Surface Energy Cohesive Energy
0.8
0.6
4
0.4
2
0.2
ΔEcohesive(eV)
6 -2
Esurface(Jm )
8
Models
0
X
Atom Nano(I) Nano(II) Nano(III)
0.0
[(100×110)] (110) (100) Bulk
Model Fig. 2. Sketch map of surface energy & cohesive energy along with geometry dimension.
are equal to −4.2537 eV and −4.2735 eV, respectively. It is obvious that the stability of nanorods increasing by virtue of geometrical dimensions growing. As mentioned earlier, larger surface energy corresponds to greater surface chemical activity. Similarly, smaller cohesive energy corresponds to greater structural stability. Sometimes it is difficult to optimize both properties in a single material. For example, the surface energy of some clusters is larger than the corresponding crystal, but the stability of the crystal is better than that of the clusters. By systematically comparing the surface energy and cohesive energy along the evolution path of crystal → bulk surface → nanorod, it is possible to determine the smallest cohesive energy together with the smallest surface energy for the MnO2 structures under consideration. In the bulk surface models, the evolution of the surface energy is proportional to that of the cohesive energy. But in nanorods, such a correspondence between surface and cohesive energies is not observed. Indeed, the surface energy increases when the cohesive energy decreases. For example, the largest Esurface is obtained for nano(III), which has the smallest cohesive energy among the three nanorod models. Furthermore we figured the difference of surface energy and cohesive energy along with geometry dimension for all models compared with that of crystal MnO2 as shown in Fig. 2. When moving along the series single atom → nanorod → bulk surface → MnO2 crystal, the number of atoms in the models increases from one to infinity. It is found that the surface energy initially increases along with the enlargement of the geometric structure of the nanorods, and then decreases starting with the [(100 × 110)] microfacet until the MnO2 crystal, due to the decrease of the surface/bulk ratio ␣. When considering the differences in the cohesive energy, we find that it decreases along with the addition of atoms in the evolution of the nanorod structures. It also decreases when moving from the bulk surface to the MnO2 crystal. This, we can predict that there exists an optimal nano(X) structure of MnO2 , which has a surface energy suitable for
Fig. 3. Relaxation energy of MnO2 bulk surface and nanorod models.
enhanced surface activity, together with proper cohesive energy for maintaining structural stability. 3.2. Relationship between relaxation energy and geometry As well known, both the surface catalytic effect and the structural stability are dependent on the geometry and structure of the material. Based on their geometry and morphology, all of the configurations considered in this work can be treated as derivatives of the MnO2 crystal. In both the bulk surface and nanorod structures, the original bonding distances are equal to these in bulk MnO2 . But on the free surface planes, the original chemical bonds among atoms are broken. Therefore, they are unstable, and tend to relax in order to release the imbalance of forces on the surface slabs. As a result of this, the characteristic evolution of surface energy and cohesive energy in each structure may have some relationship with the geometry. In order to investigate this relation, we extract the difference of energy between the original structure and optimized structure for each case, as shown in Fig. 3. From a thermodynamic viewpoint, the above relaxation energy originates from the pressure decrease of the crystal to zero in the bulk surface by the slicing process. Therefore, it can be regarded as the relaxation energy of both geometry and electronic structure. Because this energy is negative, the process can be treated as an exothermic reaction. Also, the smaller the relaxation energy, the more easily a defect structure can be formed. Fig. 3 shows that the smallest relaxation energy corresponds to the (100) bulk surface, which means that such defected MnO2 structure can be easily formed. This is confirmed by the (100) bulk surface with the smallest surface energy, and the largest cohesive energy, which is in agreement with experimental results [22]. The next structure in the energy ordering is the (110) bulk surface. Considering nanorods, it is found that the largest energy is found in nano(III), followed by the nano(II) and nano(I) structures. Furthermore, the relaxation energy of the [(100 × 110)] bulk surface is similar to that of nano(II). Analysis of nano(III) shows that, although it has the smallest cohesive energy and largest surface energy among three nanorod models, it also has the largest relax-
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dicular direction takes the order nano(III) > nano(II) > nano(I) along the (100) Miller index plane. Therefore it is evident that the geometry and structure have some relationship with the surface energy and cohesive energy, with the relaxed geometry along a surface slab determining the geometry portion of the surface effect. 3.3. Electronic structure
Fig. 4. Sketch maps of labeled atoms for (100) and (110) Miller index plane.
ation energy, which means it has poor formation ability. Thus in nanorods, the surface activity, structural stability and formation capability must all be considered in their production process. It is well known that the surface energy and relaxation energy of MnO2 are influenced mainly by its geometry and structure. In every simulation model in this paper, each surface plane is a combination of (100) and (110) Miller index planes. Therefore, we compare their surface structure with crystal slabs by similar Miller indices. In order to separately identify the performance at similar sites, we labeled some atoms along the (100) Miller index plane by numbers (1)-(11), and atoms along the (110) Miller index plane were numbered as (I)-(VII). These numbering schemes are shown in Fig. 4(a) and (b) (detailed expression are shown in Figs. S1 and S2). It is found that the atoms in the MnO2 crystal along the (100) Miller index plane have different bond distances, with a bond angle of 185.608◦ , while along the (110) Miller index plane the bond angle is 174.392◦ (see Figs. 5 and S1). However, when the crystal was sliced to be (100) plane, the bond angle was increased to 186.100◦ . This means the Mn(4) atom extends out of the plane a little (Fig. S1(b)). Considering the difference of bond distance compared with which in crystal along horizontal direction (in Fig. 4 as d1-2 , d3-4 , d4-5 , d5-6 , d6-9 and d10-11 ), it becomes longer, and the bigger one in (100) Miller index plane is the d5-6 (Mn(5)-O(6)) and d10-11 (Mn(10)-O(11)) about 0.022 Å (Fig. 5(a)). For the difference of bond distance in perpendicular direction (in Fig. 4 as d2-3 , d6-7 , d6-8 , d9-10 ) is also increased about 0.014 Å in d2-3 (Mn(2)-O(3)) (Fig. 5(b)). However to (110) bulk surface, the difference of bond distance is also up to 0.063 Å between O(V)-Mn(VI) (Fig. 5(c)), and its bond angle is decreased to be 162.062◦ from original 174.392◦ in crystal (Fig. 5(d)). Based on the above data, it is not hard to understand why the surface energy of the (100) bulk surface is larger than that of the (110) bulk surface. The difference originates from the variance in the bond distances, and the larger bond angle in the (110) bulk surface in comparison to the (100) bulk surface. In the other models, it is found that the bond angle of the (100) Miller index planes decreases to 169.489◦ for the [(100 × 110)] bulk surface, and down to 168.044◦ , 168.811◦ and 169.557◦ for nano(I), nano(II) and nano(III) structures, respectively. All of the above angles are smaller than 180◦ , which means the Mn(4) atom is indented compared with its surrounding O atoms. Furthermore, the largest change of bond distance in the horizontal direction is the increase by 0.088 Å of the [(100 × 110)] bulk surface. The changes are 0.074 Å, 0.087 Å and 0.068 Å for nano(I), nano(II) and nano(III), respectively (Fig. 5(a)). Similarly, the largest change of bond distance in the vertical direction is an increase by 0.060 Å of the [(100 × 110)] bulk surface, together with changes of 0.053 Å, 0.065 Å and 0.068 Å for nano(I), nano(II) and nano(III), respectively (Fig. 5(b)). By considering the differences in the bond distances and angles, it is found that the largest surface energy found in the [(100 × 110)] bulk surface originates from its large deformation in the bond distance and angle. In the evolutionary series nano(I)-nano(III), we can see that the trend of the angles and the difference of the bond distances in the perpen-
The surface activity and other surface effects are influenced by the surface slab electrons in both bulk surface and nanorod models. In order to qualitatively evaluate the evolution of surface effects along the series crystal → bulk surface → nanorod, we investigated the covalent bonding between O and Mn atoms by using the Mulliken population analysis method. Mulliken charge Q(A) of an atom A with a bond overlap population QA-B between A and B atoms is defined as follows [27]: Q (A) =
wk
k
wk
A A
Pv (k)Sv (k)
(3)
2Pv (k)Sv (k)
(4)
v
k
QAB =
A A
v
where P (k) and S (k) are the density matrix and the overlap matrix, respectively. wk is the weight associated with the calculated K-points in the Brillouin zone. Usually, the magnitude and sign of Q(A) characterize the ionicity of atom A in the supercell. Then the positive value of Q(A) means the atom A loses electrons as Mn element and the negative value of Q(A) means the atom A gets electrons as O element, respectively. QA-B can be used to approximately measure the average covalent bonding strength between A and B atoms. The results are shown in Figs. S3–S6. In common with the earlier analysis of geometry and structure, we only pay attention to the changes in each model compared with the crystal as Q = QModels -Qcrystal (wherein QModels represents the Q(A) or QA-B of all the simulated models). Thus, we chose the Mulliken population and Mulliken charge of the crystal as the reference points. All of the others values are given by their differences compared with above reference values. A negative value for the difference in the Mulliken population QMn-O means that the bond strength is weaker, and a positive value of the difference in Mulliken charge QA means that the atoms have lost electrons and exhibit reduction capabilities. Therefore, the values of QMn-O and QA have opposite trends. The results are shown in Figs. 6 and 7. Fig. 6(a) and (b) show that the QMn-O values between Mn-O of both (100) and (110) Miller index planes in the simulation models have a complicated trend. For example, some bonds are strengthened (bonds labeled as (3)–(4) and (4)–(5)), while other bonds are weakened (bonds labeled as (5)–(6), and so on in Figs. S3 and S4). However, when the absolute sum values of the differences for the sum ) are compared among the various Mulliken population (QMn-O sum structures under consideration, it is found that the largest QMn-O is for nano(III) along the (100) Miller index plane. A large value is also seen for the (110) Miller index plane of this structure (Fig. 6(a) sum is observed for nano(I) in and (b)). However the smallest QMn-O both the (100) and (110) Miller index planes (Fig. 6(a) and (b)). Comsum values for the three nanorods, it is found that the paring the QMn-O values for nano(III) and nano(I) occupy the extremes because the former is the largest nanorod and the latter is the smallest nanorod considered in this work. Thus, it can be concluded that the Mulliken populations among the nanorods are affected by nanoscale effects. The difference of Mulliken charge QA (Fig. 7(a) and (b)) is found to be positive, which indicates all of the above MnO2 bulk surface and nanorod models lost electrons to gain reduction properties compared with the crystal (Figs. S5 and S6). A more detailed
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Fig. 5. Difference of bond distance and bond angle along (100) and (110) Miller index plane in simulation models.
Fig. 6. Difference of Mulliken population for unit of (100) and (110) Miller index plane in simulation models.
analysis gives positive values of difference in the Mulliken charge for every element, and indicates that the O atoms got much fewer electrons while the Mn atoms lost much more electrons in relation to the MnO2 crystal. This result leads us to question the location of the missing electrons. They may be used in the construction of the surface effect and contribute to the surface relaxation. 3.4. Electronic structure In order to discover the internal mechanism in the difference of their structural stability and catalysis, this paper further calculated the partial density of states (PDOS) (Fig. 8), and further figured out the difference of PDOS (DO-PDOS) compared with that of crystal for
bulk surface and nanorod models (Fig. 9). From Fig. 8, it is shown that although the peak of PDOS for crystal at −17.5 eV is moved to much lower energy state for bulk surface and nanorods, but there exist a large negative DO-PDOS labeled by (I) in Fig. 9. Furthermore, there occurs an energy gap near Fermi energy in the PDOS of MnO2 crystal except for other models. Such phenomenon shows the structure stability of bulk surface and nanorods is decreased by surface effect, which is similar with the results of former cohesive energy. Deeply analyzed their chemical activity at first, the Fermi energy has some reference information because of its separation on bonding and antibonding electrons (in Fig. 8). To bulk models, it is found that the Fermi energy (EF = −2.8808 eV) of MnO2 crystal is the larger
Z. Chen et al. / Applied Surface Science 420 (2017) 205–213
211
PDOS(states/eV)
Fig. 7. Difference of Mulliken charge for unit of (100) and (110) Miller index plane in simulation models.
1.6 EF=-2.8808eV 1.2 Crystal 0.8 0.4 0.0 EF=-1.1293eV 1.2 0.8 (100) 0.4 0.0 EF=-1.6180eV 1.2 0.8 (110) 0.4 0.0 EF=-1.9861eV 1.2 0.8 [(100×110)] 0.4 0.0 -20 -15 -10 -5
(a)
EF
EF=-3.4977eV
(e)
EF
Nano(I)
(b)
(f)
EF=-2.1204eV
Nano(II)
(c)
(g)
EF=-1.9135eV
Nano(III)
(d)
0
5
10 -20 -15 -10 -5
0
5
10
Energy(eV)
DO-PDOS(states/eV)
Fig. 8. Partial DOS of MnO2 crystal, bulk surface and nanorod models.
0.8 0.0 -0.8 0.8 0.0 -0.8 0.8 0.0 -0.8 -20
(100) (a) (I)
EF
(II)
(110) (b)
Nano(II) (e)
[(100×110)] (c)
Nano(III) (f)
-15
(II) (III) EF
Nano(I) (d)
-10
-5
0 -15 Energy(eV)
-10
-5
0
5
Fig. 9. Difference of PDOS compared with crystal for MnO2 crystal, bulk surface and nanorod models.
than that of bulk surface. And the Fermi energy (EF = −1.6180 eV) of (110) bulk surface is smaller than that (EF = −1.1293 eV) of (100) bulk surface, so the chemical activity of (110) bulk surface is more powerful than that of (100) bulk surface, which is consist with the results of David et al. [21]. To nanorods, the trend of Fermi energy is EF -nano(III) (−1.9135 eV) > EF -nano(II) (−2.1204 eV) > EF nano(I) (−3.4977 eV), which is consisting with that of their surface energy. Secondly, their chemical activity is mainly determined by their electrons near Fermi energy EF . From the DO-PDOS in Fig. 9, it is shown that the electrons below EF are decreased, and the electrons above EF are increased compared with that of crystal (labeled
by (II)), which means their antibonding electrons is increased by surface effect for bulk surface and nanorods. Thirdly, according to frontier molecular orbital theory, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), which are the electrons donor and electrons acceptor, respectively [28,29], control the chemical activity of some catalyst. Then the HOMO and LUMO were calculated in this paper as shown in Figs. S7 and S8 . The results show that the crystal, (100) and (110) bulk surface exhibit both of the performance of electrons donor and electrons acceptor (Figs. S7 and S8(a)–(c)). But to [(100 × 110)] bulk surface and nanorods, there exhibit some abnormal performance. The HOMO is only existed in the atoms along (110) Miller index layers, and the LUMO is only appearing on (100) Miller index layers. Such abnormal electronic structure is absolutely obvious for nanorods. As well known, the selective electronic performance is very important to the catalytic property of MnO2 nanorods. Because the (110) Miller surface layers of MnO2 nanorod will exhibit as an electrons donor, and (100) Miller surface layers as an electrons acceptor. Then the MnO2 nanorods will perform both of electrons donor and electrons acceptor, respectively. But to different reactants, the MnO2 nanorods will have different catalytic property. At present it isn’t hard to understand why MnO2 have selective catalytic activity for Li2 O2 by (100) Miller layer [2] and for As5+ by (110) Miller layer [20], respectively. Conclusively, the electronic structure of (100) and (110) bulk surface has significantly difference with that of nanorods. Then the previous researches [2,30], which investigated the catalysis performance of MnO2 nanostructure by the model of bulk surface, have some deficiency. Such treatment will result in some imprecision because the interaction between neighbor surfaces is neglected for nanometer materials. Although the transitional model, such as [(100 × 110)] bulk surface in this paper, is not confirmed in experiments yet, it has some characteristic and much more approaching performance to nanometer structure. It may be a much more appropriate model to reveal the performance of nanometer materials. However, some other stable surfaces, such as (111), (112) and (211) Miller indexes, are not considered in this paper for MnO2 nanorods following the research of Tompsett et al. [21]. And we believe they will make much more unforeseen characters for the micromechanism of MnO2 nanometer materials in our further research. 4. Discussion It is well known that surface effects are influenced by surface morphologies, due to both geometric and electronic structure considerations. In addition, surface activity is mainly determined
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4.8
6 Surface energy Cohesive energy
[(100×110)]
(110)
4
(100)
4.6 Nano(III) Nano(II) Nano(I)
2
4.4
-Ecohesive(eV)
-2
Esurface(J*m )
crystal
Nano(III) Nano(II) [(100×110)] Nano(I) 4.2
(100)
0
(110) crystal
0.0
0.2
0.4 Total ΔQA
0.6
0.8
1.0
Fig. 10. Sketch map of surface energy (Esurface ) and cohesive energy (Ecohesive ) along with summation of Mulliken charge (QAtotal ).
by surface electrons. Another purpose of this paper is to determine is there existed an optimized MnO2 nanorod structure, which exhibits good stability and excellent surface activity. In order to achieve this goal, the total value of QAtotal by taking the sum of QA100 and QA110 for the models was calculated at first in Fig. 10. It is found that the smallest value is for the (110) bulk surface, and the largest for nano(I). The trend of QAtotal is as follows: nano(I) > nano(II) > nano(III) > [(100 × 110)] bulk surface > (100) bulk surface > (110) bulk surface > MnO2 crystal. The lost electrons along surface planes maybe construct the electronic portion of the surface effect. Furthermore the surface energy and cohesive energy are determined as a function of Mulliken charge, QAtotal , and are also plotted in Fig. 10. The cohesive energy increases along with the QAtotal when moving from crystal to nano(I). It is likely that a stable nanorod (nano(X)) with enhanced properties can be synthesized between nano(III) and the [(100 × 110)] bulk surface. On the other hand, the surface energy is observed to increase along with QAtotal when moving from [(100 × 110)] to the bulk surface, and subsequently decreases when the structure becomes smaller as it moves to the nano(I) form. Systematically comparing the trends of both cohesive energy and surface energy, it can be seen that they have a crossing point that can be associated with the nano(X) structure, indicating that nano(X) has good stability and excellent surface activity. This is further supported by the data shown in Fig. 2. The summation of Mulliken charge, QAtotal , for this structure is expected to be in the 0.5–0.8 range. 5. Conclusion By calculating their geometry and electronic structure based on first principles, the evolution of surface effects and electronic structure for several morphologies of MnO2 were investigated carefully. The results show that: Following the increase of surface/bulk ratio, ␣, the evolution of surface energy has the trend [(100 × 110)] bulk surface> nano(III) > nano(II) > nano(I) > (110) bulk surface > (100) bulk surface > MnO2 crystal; and the evolution of cohesive energy is MnO2 crystal < (100) bulk surface < (110) bulk surface < nano(III) < [(100 × 110)] bulk surface < nano(II) < nano(I). (1) The relaxation energy based on the change of geometry between the bulk surfaces and nanorods shows the following trend: (100) bulk surface > (110) bulk surface > nano(I) > nano(II) > [(100 × 110)] bulk surface > nano(III) > MnO2 crystal. This trend indicates that
the growth of nanorods becomes harder as the relaxation energy decreases. Further analysis of the differences of bond distances and bond angles in the (100) or (110) Miller index planes, it is found the trend of bond angles, as well as the difference of bond distances in the perpendicular direction, is nano(III) > nano(II) > nano(I). (2) Mulliken population analysis showed that the largest absosum , is for nano(III), while the lute sum value difference, QMn-O smallest is for nano(I) only comparing the nanorod models. The differences in Mulliken charge, QA , are always found to be positive, which means that all of the MnO2 bulk surface and nanorod structures lost electrons to gain reduction properties compared with the crystal. The trend of QA is nano(I) > nano(II) > nano(III) > [(100 × 110)] bulk surface > (100) bulk surface > (110) bulk surface > MnO2 crystal. (3) PDOS shows that the structure stability of bulk surface is decreased because their Fermi energy is larger than that of MnO2 crystal. But the chemical activity of bulk surface and nanorod is increased for their decrease of bonding electrons and addition of anti-bonding electrons near Fermi energy. The results of molecular orbital show that the selective catalytic activity of MnO2 nanorods is originated from the preference of HOMO on (110) Miller index layers and LUMO on (100) Miller index layers, respectively. Acknowledgements This work was support from the projects of National Natural Science Foundation of China (Grant No. 51361026 and 11304146, Foundation of Jiangxi Educational Committee (GJJ160684), and Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle (Nanchang Hangkong University) (ST201522014). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apsusc.2017.05. 141. References [1] Y. Yang, J. Huang, S.W. Wang, S.B. Deng, B. Wang, G. Yu, Catalytic removal of gasous unintentional POPs on manganese oxide octahedral molecular sieves, Appl. Catal. B: Environ. 142–143 (2013) 568–578. [2] Y. Crespo, N. Seriani, A lithium peroxide precursor on the ␣-MnO2 (100) surface, J. Mater. Chem. A 2 (2014) 16538–16546. [3] Z.H. Lian, F.D. Liu, H. He, X.Y. Shi, J.S. Mo, Z.B. Wu, Manganese–niobium mixed oxide catalyst for the selective catalytic reduction of NOx with NH3 at low temperatures, Chem. Eng. J. 250 (2014) 390–398. [4] C.M. Vasconcellos, M.L.A. Goncalves, M.M. Pereira, N.M.F. Carvalho, Iron doped manganese oxide octahedral molecular sieve as potential catalyst for SOx removal at FCC, Appl. Catal. A: Gen. 498 (2015) 69–75. [5] X.Y. Wang, L.F. Mei, X.B. Xing, L.B. Liao, G.C. Lv, Z.H. Li, L.M. Wu, Mechanism and process of methylene blue degradation by manganese oxides under microwave irradiation, Appl. Catal. B: Environ. 160–161 (2014) 211–216. [6] G. Wu, Y. Gao, F.W. Ma, B.H. Zheng, L.G. Liu, H.Y. Sun, W. Wu, Catalytic oxidation of benzyl alcohol over manganese oxide supported on MCM-41 zeolite, Chem. Eng. J. 271 (2015) 14–22. [7] E. Eren, M. Guney, B. Eren, H. Gumus, Performance of layered birnessite-type manganese oxide in the thermal-catalytic degradation of polyamide 6, Appl. Catal. B: Environ. 132–133 (2013) 370–378. [8] K.D. Kwon, K. Refson, G. Sposito, Understanding the trends in transition metal sorption by vacancy sites in birnessite, Geochim. Cosmochim. Acta 101 (2013) 222–232. [9] B.K. Zhang, J. Liu, C.G. Zheng, M. Chang, Theoretical study of mercury species adsorption mechanism on MnO2 (110) surface, Chem. Eng. J. 256 (2014) 93–100. [10] M. Pourkhalil, A.Z. Moghaddam, A. Rashidi, J. Towfighi, Y. Mortazavi, Preparation of highly active manganese oxides supported on functionalized MWNTs for low temperature NOx reduction with NH3 , Appl. Surf. Sci. 279 (2013) 250–259.
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Applied Surface Science journal homepage: www.elsevier.com/locate/apsusc
Full Length Article
Mechanism of surface effect and selective catalytic performance of MnO2 nanorod: DFT+U study Zheng Chen a , Guifa Li a,b,∗ , Haizhong Zheng a , Xiaoyong Shu a , Jianping Zou a , Ping Peng b a b
Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Jiangxi, 330063, China School of Material Science and Engineering, Hunan University, Hunan 410082, China
a r t i c l e
i n f o
Article history: Received 13 February 2017 Received in revised form 15 May 2017 Accepted 16 May 2017 Available online 17 May 2017 Keywords: MnO2 nanorod Surface nanometer Selective catalysis Microcosmic mechanism First-principles
a b s t r a c t The mechanism of surface effect and selective catalytic performance of MnO2 nanorods remain mysterious at present. Using first-principles pseudo-potential plane wave method, the surface energy, cohesive energy, geometrical and electronic structure for MnO2 in the evolution of crystal → bulk surface → nanorod morphologies have been systematically calculated and analyzed. The results show that the surface energy is increased along with the decrease of geometry configuration in crystal → bulk surface → nanorod as a whole. However in three nanorod morphologies, the surface energy is increased along with the additional geometry configuration, wherein the largest nano(III) has the largest surface energy and lowest cohesive energy. These characters are originated from their changes in geometry structure and lost in Mulliken charges of atoms along surface planes. Electronic structure shows that the selective catalytic activity of MnO2 nanorods is originated from their unique states of valence electrons, which occupying the highest occupied molecular orbital (HOMO) only appears on (110) Miller surface layers and the lowest unoccupied molecular orbital (LUMO) only on (100) Miller surface layers, respectively. Such attracting phenomenon has significantly difference with that of MnO2 bulk surface. Thereinto, a transitional model of [(100 × 110)] microfacet model is found to exhibit much more approaching surface performance to MnO2 nanometer structure somewhere. Thus, our findings open an avenue for detailed and comprehensive studies on the growth and catalysis of MnO2 nanomaterials. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Environmental pollution resulting from economic development has attracted the attention of many researchers due to its effect on the quality of life [1]. Reducing the toxicity of pollutants is a major problem facing the world. One of the potential materials typically suggested as an absorbent for pollutants is manganese dioxide (MnO2 ), which theoretically has high capacitance, low cost, environmental compatibility, and naturally abundant. These properties are used when MnO2 is employed in electrochemical capacitors and Li-ion battery cathodes [2]. Furthermore, as a good selective catalytic reduction (SCR) agent, it can be used in many oxygen reduction reaction (ORR) processes, such as the oxidation of ammonia with NO [3], SOx [4], and methane in the presence of visible
∗ Corresponding author at: Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Jiangxi, 330063, China. E-mail addresses: lgf [email protected], [email protected] (G. Li). http://dx.doi.org/10.1016/j.apsusc.2017.05.141 0169-4332/© 2017 Elsevier B.V. All rights reserved.
light or water [2]. It can also be put to use in the degradation of organic pollutants such as methylene blue [5], benzyl alcohol [6], and polyamide [7]. Such excellent catalytic activity arises from the unique redox couple Mn3+ /Mn4+ presenting on the surface and its tunnel-like structure. In addition, MnO2 ’s special configuration allows another important use in the form of reduction for heavy metals in environmental pollutants, such as Zn, Cu, Ni, Co [8], and Hg [9]. However, its poor conductivity and the constrained activity of the atoms along the surface have a detrimental effect on both the utilization of the catalyst and the rate of reaction. Thus, many methods have been used to improve the structure, stability, and surface reaction rate. For example, carbon nanotubes (CNTs) [10] and graphene [11] are used to form composites with MnO2 . Doping Ag [12], Au [13], Co [14], and Al [15] into MnO2 is an excellent method of enhancing its chemical properties. And nanotechnology is another effective and straightforward technique to promote the catalytic activity of a material by improving its surface structure, surface active sites, and surface area, all of which have a direct bearing on the crystal surface, crystal planes, and stereochemistry of the catalytic process. Due to the greatly enhanced surface
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reaction rates of nanostructured MnO2 compared with the bulk material surface, the former has attracted the attention of many researchers. In addition to enhancements in the catalytic properties, the absorption capability of nanomaterials is 5–10 times that of micron size counterparts [16]. Huang et al. [17] found that the efficiency of removal of NO was affected by the content of magnesium in doped nanostructured manganese oxides, wherein the best results were obtained for samples doped with 10 wt% magnesium, while the worst was for 5 wt%. Also, different nanoscale configurations of MnO2 were synthesized by a hydrothermal method to exhibit different catalytic effects. All of such variations originate from the surface mechanisms, especially due to nanoscale effects. These surface mechanisms are not yet fully understood, and theoretical research is vital in elucidating the effect of microstructure on the mechanisms. For example, Crespo et al. [2] discovered the special absorbing style of oxygen on ␣-MnO2 through O2 molecule instead of single O atom, which was the primary condition for lithium-air batteries. Zhang et al. [18] studied the adsorption ability and adsorption structures of mercury species (Hg, HgCl and HgCl2 ) on MnO2 , and described their desorption mechanisms. Loganathan et al. [19] studied the influence of pH on the absorption ability of Ni on ␦-MnO2 . It is well known that the surface structure and surface area are the main elements influencing the adsorption ability of MnO2 material. However, questions remain as to what the internal differences are between bulk and nanostructured MnO2 , the relationship between these different forms, and the optimal configuration for nanorod MnO2 . Why MnO2 have selective catalytic activity for Li2 O2 by (100) Miller surface layer [2] and As5+ by (110) Miller surface layer [20], respectively. Such questions are difficult to answer in experiment. In this paper, we attempt to illuminate the evolution mechanisms of the electronic structure for bulk and
nanorod MnO2 , and reveal the selective catalysis mechanism of MnO2 . This can be considered a first and vital step in understanding the unusual properties of nanoscale MnO2 . 2. Simulation models and method In order to illuminate the surface effect of the nanoscale material, several models of MnO2 in topological configurations of crystal, bulk surface and nanorod were constructed and studied systematically by their stiochiometric proportion. In the above constructs, the surface area increases from zero to a very high value. Based on the research of Tompsett et al. [21], only two prominent and stable Miller index planes, such as (100) and (110), were considered. In this study, the nanorod models were constructed following the results of experiments by Zhang et al. [22] and Wulff [21]. A transitional [(100 × 110)] microfacet structure only composed by (100) and (110) Miller indices was built to link the bulk surface and nanorod morphologies. All of the simulation models are shown in Fig. 1. For (100) and (110) bulk surfaces there exist twice units of (100) and (110) Miller index slabs in Fig. 1(b) and (c), respectively. About nanorod models, all of them were also combined by (110) and (100) Miller index slabs in Fig. 1(e–f). The smallest nanorod, consisting of four units of the (100) and (110) Miller index slabs, is labeled as nano(I) in Fig. 1(e). The second MnO2 nanorod (nano(II)) contains eight units of the (100) and four units of the (110) Miller index slabs (Fig. 1(f)). The largest MnO2 nanorod in this paper is labeled as nano(III), and is modeled with eight units of each (100) and (110) Miller index slabs to construct a Mn112 O224 supercell (Fig. 1(g)). This last model is the largest possible based on the capability of our computer cluster. All of these primitive cells of the nanorods can be repeated to make larger supercells by using their
Fig. 1. Simulated several morphologies of MnO2 models, wherein (a) MnO2 crystal (Mn8 O16 ) (b) (100) bulk surface (Mn32 O64 ) (c) (110) bulk surface (Mn16 O32 ) (d) [(100 × 110)] microfacet (Mn32 O64 ) (e) nano(I) (Mn28 O56 ) (f) nano(II) (Mn68 O136 ) (g) nano(III) (Mn112 O224 ).
Z. Chen et al. / Applied Surface Science 420 (2017) 205–213 Table 1 Predicted PBE + U, experimental and theoretical lattice parameters for ␣-MnO2. ␣-MnO2
a(Å)
b(Å)
c(Å)
This work Ref. [21] Exp. [26]
9.922 9.907 9.750
9.922 9.907 9.750
2.904 2.927 2.861
the coefficient of 2 is not necessary. The cohesive energy is representative of the work required for a crystal to be decomposed into atoms, which in turn denotes the stability of the respective simulation model. Herein, the Ecohesive of several MnO2 models have been calculated from the following equation: Ecohesive =
periodic boundary conditions and transitional symmetry. The aim of such constructions is to quantify the surface effect of different Miller indices over several models. All of the bulk surface models were calculated based on slabs with a minimum thickness of 14 Å, and a vacuum thickness greater than 12 Å was used throughout to separate the slabs from their periodic image for bulk surface and nanorod models. All of the above simulation models were relaxed via the following process: a first-principles pseudopotential plane-wave method, based on density functional theory, was implemented in the CASTEP code [23]. The electronic structure was calculated using the Generalized Gradient Approximation (GGA) of Perdew, Burke, and Ernzerh with Hubbard U corrections (PBE + U) [24]. The PBE + U exchange–correlation functional has been demonstrated to give a good description of defect properties in MnO2 [21]. All calculations were performed in a ferromagnetic spin polarized configuration, while effects of more complex magnetic orders were left for future work due to their low energy scale. All subsequent calculations were performed based on the equilibrium lattice constants obtained without cell relaxation using a cutoff of 450 eV. This included the recalculation of the energy for the bulk unit cell so that all comparative energies were obtained. A minimum of 2*1*1 k-points was used in the Brillouin zone of the conventional cell and scaled appropriately for supercells. All atomic positions in these primitive cells were relaxed according to the total energy and force using the BFGS scheme [25], based on the cell optimization criteria (RMS force of 0.03 eV/Å, a stress of 0.01 GPa, and displacement of 0.002 Å). The convergence criteria of self-consistent field (SCF) and energy tolerances were set at 1.0 × 10−4 and 5.0 × 10−4 eV/atom, respectively. The value of the U parameter for our PBE + U calculations was determined by ab initio calculations. Previous work conducted by some of the present authors [2] demonstrated that a good description of structural stability, band gaps and magnetic interactions can be obtained when PBE + U was applied in the fully-localized limit, which is therefore used in this work as well. We employ U = 1.6 eV. The ␣-MnO2 crystal occurs in the tetragonal space group I4/m with lattice parameters a = b = 9.775 Å and c = 2.904 Å. In Table 1, we show the calculated lattice parameters for ␣-MnO2 from PBE + U. These results agree with the experimental parameters within 1.8%, but the common tendency for PBE + U to overestimate the unit cell volume is evident. 3. Results and discussion 3.1. Structural stability In Table 2, we show the surface energy Esurface and cohesive energy Ecohesive for ␣-MnO2 obtained via PBE + U calculations. The surface energy is calculated by taking the difference between the energy of a constructed slab and the same number of ␣-MnO2 formula units in the bulk: Esurface =
Etotal − nEb 2·S
(1)
where Etotal is the energy of a surface or nanorod model containing n formula crystal units, and Eb is the total energy of the ␣-MnO2 crystal. S is the area of the bulk surface, and the coefficient of 2 indicates that each bulk surface model has two surfaces. For nanorods,
207
1 Mn O − mEgas ) (E Mnl Om − lEgas l + m total
(2)
wherein l and m represent the number of Mn and O atoms in Mnl Om denotes the total the respective morphologies of MnO2 . Etotal Mn and E O are the energies of energy of the Mnl Om models. Egas gas the gaseous Mn and O atoms, respectively. Before optimizing the gaseous atoms, we constructed a 10 × 10 × 10 (Å3 ) vacuum box and put a single atom, such as Mn or O in the center of the box to be relaxed to get its global energy minimum. The results are given as Mn = −588.1855 eV and E O = −432.2548 eV in Table 2. Egas gas From Table 2, it can be seen that the Esurface values of the (100) and (110) bulk surfaces are equal to 0.6503 Jm−2 and 0.6794 Jm−2 , respectively, which are similar to the results reported by Tompsett et al. (0.64 Jm−2 and 0.75 Jm−2 , respectively) [21]. However, the surface energy of the [(100 × 110)] microfacet model is equal to 5.0437 Jm−2 , which is the largest value found in all the simulation models. Due to the much greater surface/bulk ratio ␣ (␣ = S/V, wherein S is the surface area and V is the volume, and ␣nano(I) = 0.2341 as listed in Table S1) of the smallest nanorod model (nano(I) as seen in Fig. 1(e)), its surface energy decreases to 1.7040 Jm−2 , although the ␣ value remains larger than that of the (100) or (110) bulk surface models. In the larger nanorod model with eight units of (100) and four units of (110) Miller index slabs (nano(II)), the surface energy increases to 2.4367 Jm−2 . Finally, the surface energy of nano(III), which has eight units of each (100) and (110) Miller index slabs combined to form a Mn112 O224 structure, is equal to 3.0323 Jm−2 . When the evolution of the surface energies is compared systematically, it is found that the Esurface of the bulk surface is smaller than that of the nanorods, a trend that originates from the former’s smaller surface/bulk ratio. However, to our surprise, the surface energy of nano(III) (Esurface = 3.0323 eV) is larger than that of the smallest nano(I) (Esurface = 1.7040 eV), but the surface/bulk ratio ␣ of the former (␣nano(III) = 0.1172, as shown in Table S1) is smaller than that of the latter. As well known, a larger surface energy corresponds to a much more active surface. As such, the surface activity of the MnO2 nanorods should increase with increasing dimensions. Such evolution is contrary to what can be expected based on their decreasing surface/bulk ratios. This trend can be extended to infer that when the dimension of a nanorod increases to approach infinity, such that it resembles the bulk surface with the smallest surface/bulk ratio, it will have the highest surface energy. Such a conclusion would be erroneous. Following the evolution of surface energy presented in Fig. 2, we can predict that there exists a nano(X), which would have largest surface energy, and thus express the best surface activity. Furthermore, the cohesive energies Ecohesive of the crystal, bulk surface and nanorod models were calculated to estimate their structure stability, and the results are given in Table 2. It is found that the Ecohesive of the MnO2 crystal is equal to −4.7223 eV, which is the smallest value in this study. The next lowest value is Ecohesive = −4.6735 eV of the (100) bulk surface, whose Ecohesive is smaller than that of (110) bulk surface by 0.0248 eV, with their trend being similar to that seen for surface energy. For the [(100 × 110)] microfacet model, Ecohesive further increases to −4.2598 eV, which means its structural stability sharply decreasing compared to similar configurations of the (100) and (110) bulk surfaces. Similarly, it is found that the cohesive energy of nano(I) is equal to −4.2188 eV, which is the largest one among nanorods. With the geometry dimensions of the nanorod increasing, the cohesive energy decreases slowly. The Ecohesive of nano(II) and nano(III)
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Table 2 Surface energy (Esurface ) and cohesive energy (Ecohesive ) of MnO2 crystal, bulk surface and nanorod models.
Crystal Bulk surface
Nanorod
Etotal (eV)
a (Å)
b (Å)
S (Å2 )
Esurface (eV)
Esurface (Jm−2 )
Ecohesive (eV)
Mn8 O16 (100)surface (110)surface [(100 × 110)] microfacet nano(I) nano(II) nano(III)
−11734.8960 −46934.8997 −23466.2580 −11734.8960 −41029.8368 −99651.0223 −164137.7267
– 2.9040 2.9040 2.9040 2.9040 2.9040 2.9040
– 19.8440 14.3292 24.2512 68.3480 108.0360 137.0160
– 57.6265 41.6116 70.4255 198.4811 313.7342 397.8914
0 4.6843 3.5340 44.4003 42.2991 95.5937 150.8172
0 0.6503 0.6794 5.0437 1.7040 2.4376 3.0323
−4.7223 −4.6735 −4.6487 −4.2598 −4.2188 −4.2537 −4.2735
Surface Energy Cohesive Energy
0.8
0.6
4
0.4
2
0.2
ΔEcohesive(eV)
6 -2
Esurface(Jm )
8
Models
0
X
Atom Nano(I) Nano(II) Nano(III)
0.0
[(100×110)] (110) (100) Bulk
Model Fig. 2. Sketch map of surface energy & cohesive energy along with geometry dimension.
are equal to −4.2537 eV and −4.2735 eV, respectively. It is obvious that the stability of nanorods increasing by virtue of geometrical dimensions growing. As mentioned earlier, larger surface energy corresponds to greater surface chemical activity. Similarly, smaller cohesive energy corresponds to greater structural stability. Sometimes it is difficult to optimize both properties in a single material. For example, the surface energy of some clusters is larger than the corresponding crystal, but the stability of the crystal is better than that of the clusters. By systematically comparing the surface energy and cohesive energy along the evolution path of crystal → bulk surface → nanorod, it is possible to determine the smallest cohesive energy together with the smallest surface energy for the MnO2 structures under consideration. In the bulk surface models, the evolution of the surface energy is proportional to that of the cohesive energy. But in nanorods, such a correspondence between surface and cohesive energies is not observed. Indeed, the surface energy increases when the cohesive energy decreases. For example, the largest Esurface is obtained for nano(III), which has the smallest cohesive energy among the three nanorod models. Furthermore we figured the difference of surface energy and cohesive energy along with geometry dimension for all models compared with that of crystal MnO2 as shown in Fig. 2. When moving along the series single atom → nanorod → bulk surface → MnO2 crystal, the number of atoms in the models increases from one to infinity. It is found that the surface energy initially increases along with the enlargement of the geometric structure of the nanorods, and then decreases starting with the [(100 × 110)] microfacet until the MnO2 crystal, due to the decrease of the surface/bulk ratio ␣. When considering the differences in the cohesive energy, we find that it decreases along with the addition of atoms in the evolution of the nanorod structures. It also decreases when moving from the bulk surface to the MnO2 crystal. This, we can predict that there exists an optimal nano(X) structure of MnO2 , which has a surface energy suitable for
Fig. 3. Relaxation energy of MnO2 bulk surface and nanorod models.
enhanced surface activity, together with proper cohesive energy for maintaining structural stability. 3.2. Relationship between relaxation energy and geometry As well known, both the surface catalytic effect and the structural stability are dependent on the geometry and structure of the material. Based on their geometry and morphology, all of the configurations considered in this work can be treated as derivatives of the MnO2 crystal. In both the bulk surface and nanorod structures, the original bonding distances are equal to these in bulk MnO2 . But on the free surface planes, the original chemical bonds among atoms are broken. Therefore, they are unstable, and tend to relax in order to release the imbalance of forces on the surface slabs. As a result of this, the characteristic evolution of surface energy and cohesive energy in each structure may have some relationship with the geometry. In order to investigate this relation, we extract the difference of energy between the original structure and optimized structure for each case, as shown in Fig. 3. From a thermodynamic viewpoint, the above relaxation energy originates from the pressure decrease of the crystal to zero in the bulk surface by the slicing process. Therefore, it can be regarded as the relaxation energy of both geometry and electronic structure. Because this energy is negative, the process can be treated as an exothermic reaction. Also, the smaller the relaxation energy, the more easily a defect structure can be formed. Fig. 3 shows that the smallest relaxation energy corresponds to the (100) bulk surface, which means that such defected MnO2 structure can be easily formed. This is confirmed by the (100) bulk surface with the smallest surface energy, and the largest cohesive energy, which is in agreement with experimental results [22]. The next structure in the energy ordering is the (110) bulk surface. Considering nanorods, it is found that the largest energy is found in nano(III), followed by the nano(II) and nano(I) structures. Furthermore, the relaxation energy of the [(100 × 110)] bulk surface is similar to that of nano(II). Analysis of nano(III) shows that, although it has the smallest cohesive energy and largest surface energy among three nanorod models, it also has the largest relax-
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dicular direction takes the order nano(III) > nano(II) > nano(I) along the (100) Miller index plane. Therefore it is evident that the geometry and structure have some relationship with the surface energy and cohesive energy, with the relaxed geometry along a surface slab determining the geometry portion of the surface effect. 3.3. Electronic structure
Fig. 4. Sketch maps of labeled atoms for (100) and (110) Miller index plane.
ation energy, which means it has poor formation ability. Thus in nanorods, the surface activity, structural stability and formation capability must all be considered in their production process. It is well known that the surface energy and relaxation energy of MnO2 are influenced mainly by its geometry and structure. In every simulation model in this paper, each surface plane is a combination of (100) and (110) Miller index planes. Therefore, we compare their surface structure with crystal slabs by similar Miller indices. In order to separately identify the performance at similar sites, we labeled some atoms along the (100) Miller index plane by numbers (1)-(11), and atoms along the (110) Miller index plane were numbered as (I)-(VII). These numbering schemes are shown in Fig. 4(a) and (b) (detailed expression are shown in Figs. S1 and S2). It is found that the atoms in the MnO2 crystal along the (100) Miller index plane have different bond distances, with a bond angle of 185.608◦ , while along the (110) Miller index plane the bond angle is 174.392◦ (see Figs. 5 and S1). However, when the crystal was sliced to be (100) plane, the bond angle was increased to 186.100◦ . This means the Mn(4) atom extends out of the plane a little (Fig. S1(b)). Considering the difference of bond distance compared with which in crystal along horizontal direction (in Fig. 4 as d1-2 , d3-4 , d4-5 , d5-6 , d6-9 and d10-11 ), it becomes longer, and the bigger one in (100) Miller index plane is the d5-6 (Mn(5)-O(6)) and d10-11 (Mn(10)-O(11)) about 0.022 Å (Fig. 5(a)). For the difference of bond distance in perpendicular direction (in Fig. 4 as d2-3 , d6-7 , d6-8 , d9-10 ) is also increased about 0.014 Å in d2-3 (Mn(2)-O(3)) (Fig. 5(b)). However to (110) bulk surface, the difference of bond distance is also up to 0.063 Å between O(V)-Mn(VI) (Fig. 5(c)), and its bond angle is decreased to be 162.062◦ from original 174.392◦ in crystal (Fig. 5(d)). Based on the above data, it is not hard to understand why the surface energy of the (100) bulk surface is larger than that of the (110) bulk surface. The difference originates from the variance in the bond distances, and the larger bond angle in the (110) bulk surface in comparison to the (100) bulk surface. In the other models, it is found that the bond angle of the (100) Miller index planes decreases to 169.489◦ for the [(100 × 110)] bulk surface, and down to 168.044◦ , 168.811◦ and 169.557◦ for nano(I), nano(II) and nano(III) structures, respectively. All of the above angles are smaller than 180◦ , which means the Mn(4) atom is indented compared with its surrounding O atoms. Furthermore, the largest change of bond distance in the horizontal direction is the increase by 0.088 Å of the [(100 × 110)] bulk surface. The changes are 0.074 Å, 0.087 Å and 0.068 Å for nano(I), nano(II) and nano(III), respectively (Fig. 5(a)). Similarly, the largest change of bond distance in the vertical direction is an increase by 0.060 Å of the [(100 × 110)] bulk surface, together with changes of 0.053 Å, 0.065 Å and 0.068 Å for nano(I), nano(II) and nano(III), respectively (Fig. 5(b)). By considering the differences in the bond distances and angles, it is found that the largest surface energy found in the [(100 × 110)] bulk surface originates from its large deformation in the bond distance and angle. In the evolutionary series nano(I)-nano(III), we can see that the trend of the angles and the difference of the bond distances in the perpen-
The surface activity and other surface effects are influenced by the surface slab electrons in both bulk surface and nanorod models. In order to qualitatively evaluate the evolution of surface effects along the series crystal → bulk surface → nanorod, we investigated the covalent bonding between O and Mn atoms by using the Mulliken population analysis method. Mulliken charge Q(A) of an atom A with a bond overlap population QA-B between A and B atoms is defined as follows [27]: Q (A) =
wk
k
wk
A A
Pv (k)Sv (k)
(3)
2Pv (k)Sv (k)
(4)
v
k
QAB =
A A
v
where P (k) and S (k) are the density matrix and the overlap matrix, respectively. wk is the weight associated with the calculated K-points in the Brillouin zone. Usually, the magnitude and sign of Q(A) characterize the ionicity of atom A in the supercell. Then the positive value of Q(A) means the atom A loses electrons as Mn element and the negative value of Q(A) means the atom A gets electrons as O element, respectively. QA-B can be used to approximately measure the average covalent bonding strength between A and B atoms. The results are shown in Figs. S3–S6. In common with the earlier analysis of geometry and structure, we only pay attention to the changes in each model compared with the crystal as Q = QModels -Qcrystal (wherein QModels represents the Q(A) or QA-B of all the simulated models). Thus, we chose the Mulliken population and Mulliken charge of the crystal as the reference points. All of the others values are given by their differences compared with above reference values. A negative value for the difference in the Mulliken population QMn-O means that the bond strength is weaker, and a positive value of the difference in Mulliken charge QA means that the atoms have lost electrons and exhibit reduction capabilities. Therefore, the values of QMn-O and QA have opposite trends. The results are shown in Figs. 6 and 7. Fig. 6(a) and (b) show that the QMn-O values between Mn-O of both (100) and (110) Miller index planes in the simulation models have a complicated trend. For example, some bonds are strengthened (bonds labeled as (3)–(4) and (4)–(5)), while other bonds are weakened (bonds labeled as (5)–(6), and so on in Figs. S3 and S4). However, when the absolute sum values of the differences for the sum ) are compared among the various Mulliken population (QMn-O sum structures under consideration, it is found that the largest QMn-O is for nano(III) along the (100) Miller index plane. A large value is also seen for the (110) Miller index plane of this structure (Fig. 6(a) sum is observed for nano(I) in and (b)). However the smallest QMn-O both the (100) and (110) Miller index planes (Fig. 6(a) and (b)). Comsum values for the three nanorods, it is found that the paring the QMn-O values for nano(III) and nano(I) occupy the extremes because the former is the largest nanorod and the latter is the smallest nanorod considered in this work. Thus, it can be concluded that the Mulliken populations among the nanorods are affected by nanoscale effects. The difference of Mulliken charge QA (Fig. 7(a) and (b)) is found to be positive, which indicates all of the above MnO2 bulk surface and nanorod models lost electrons to gain reduction properties compared with the crystal (Figs. S5 and S6). A more detailed
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Fig. 5. Difference of bond distance and bond angle along (100) and (110) Miller index plane in simulation models.
Fig. 6. Difference of Mulliken population for unit of (100) and (110) Miller index plane in simulation models.
analysis gives positive values of difference in the Mulliken charge for every element, and indicates that the O atoms got much fewer electrons while the Mn atoms lost much more electrons in relation to the MnO2 crystal. This result leads us to question the location of the missing electrons. They may be used in the construction of the surface effect and contribute to the surface relaxation. 3.4. Electronic structure In order to discover the internal mechanism in the difference of their structural stability and catalysis, this paper further calculated the partial density of states (PDOS) (Fig. 8), and further figured out the difference of PDOS (DO-PDOS) compared with that of crystal for
bulk surface and nanorod models (Fig. 9). From Fig. 8, it is shown that although the peak of PDOS for crystal at −17.5 eV is moved to much lower energy state for bulk surface and nanorods, but there exist a large negative DO-PDOS labeled by (I) in Fig. 9. Furthermore, there occurs an energy gap near Fermi energy in the PDOS of MnO2 crystal except for other models. Such phenomenon shows the structure stability of bulk surface and nanorods is decreased by surface effect, which is similar with the results of former cohesive energy. Deeply analyzed their chemical activity at first, the Fermi energy has some reference information because of its separation on bonding and antibonding electrons (in Fig. 8). To bulk models, it is found that the Fermi energy (EF = −2.8808 eV) of MnO2 crystal is the larger
Z. Chen et al. / Applied Surface Science 420 (2017) 205–213
211
PDOS(states/eV)
Fig. 7. Difference of Mulliken charge for unit of (100) and (110) Miller index plane in simulation models.
1.6 EF=-2.8808eV 1.2 Crystal 0.8 0.4 0.0 EF=-1.1293eV 1.2 0.8 (100) 0.4 0.0 EF=-1.6180eV 1.2 0.8 (110) 0.4 0.0 EF=-1.9861eV 1.2 0.8 [(100×110)] 0.4 0.0 -20 -15 -10 -5
(a)
EF
EF=-3.4977eV
(e)
EF
Nano(I)
(b)
(f)
EF=-2.1204eV
Nano(II)
(c)
(g)
EF=-1.9135eV
Nano(III)
(d)
0
5
10 -20 -15 -10 -5
0
5
10
Energy(eV)
DO-PDOS(states/eV)
Fig. 8. Partial DOS of MnO2 crystal, bulk surface and nanorod models.
0.8 0.0 -0.8 0.8 0.0 -0.8 0.8 0.0 -0.8 -20
(100) (a) (I)
EF
(II)
(110) (b)
Nano(II) (e)
[(100×110)] (c)
Nano(III) (f)
-15
(II) (III) EF
Nano(I) (d)
-10
-5
0 -15 Energy(eV)
-10
-5
0
5
Fig. 9. Difference of PDOS compared with crystal for MnO2 crystal, bulk surface and nanorod models.
than that of bulk surface. And the Fermi energy (EF = −1.6180 eV) of (110) bulk surface is smaller than that (EF = −1.1293 eV) of (100) bulk surface, so the chemical activity of (110) bulk surface is more powerful than that of (100) bulk surface, which is consist with the results of David et al. [21]. To nanorods, the trend of Fermi energy is EF -nano(III) (−1.9135 eV) > EF -nano(II) (−2.1204 eV) > EF nano(I) (−3.4977 eV), which is consisting with that of their surface energy. Secondly, their chemical activity is mainly determined by their electrons near Fermi energy EF . From the DO-PDOS in Fig. 9, it is shown that the electrons below EF are decreased, and the electrons above EF are increased compared with that of crystal (labeled
by (II)), which means their antibonding electrons is increased by surface effect for bulk surface and nanorods. Thirdly, according to frontier molecular orbital theory, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), which are the electrons donor and electrons acceptor, respectively [28,29], control the chemical activity of some catalyst. Then the HOMO and LUMO were calculated in this paper as shown in Figs. S7 and S8 . The results show that the crystal, (100) and (110) bulk surface exhibit both of the performance of electrons donor and electrons acceptor (Figs. S7 and S8(a)–(c)). But to [(100 × 110)] bulk surface and nanorods, there exhibit some abnormal performance. The HOMO is only existed in the atoms along (110) Miller index layers, and the LUMO is only appearing on (100) Miller index layers. Such abnormal electronic structure is absolutely obvious for nanorods. As well known, the selective electronic performance is very important to the catalytic property of MnO2 nanorods. Because the (110) Miller surface layers of MnO2 nanorod will exhibit as an electrons donor, and (100) Miller surface layers as an electrons acceptor. Then the MnO2 nanorods will perform both of electrons donor and electrons acceptor, respectively. But to different reactants, the MnO2 nanorods will have different catalytic property. At present it isn’t hard to understand why MnO2 have selective catalytic activity for Li2 O2 by (100) Miller layer [2] and for As5+ by (110) Miller layer [20], respectively. Conclusively, the electronic structure of (100) and (110) bulk surface has significantly difference with that of nanorods. Then the previous researches [2,30], which investigated the catalysis performance of MnO2 nanostructure by the model of bulk surface, have some deficiency. Such treatment will result in some imprecision because the interaction between neighbor surfaces is neglected for nanometer materials. Although the transitional model, such as [(100 × 110)] bulk surface in this paper, is not confirmed in experiments yet, it has some characteristic and much more approaching performance to nanometer structure. It may be a much more appropriate model to reveal the performance of nanometer materials. However, some other stable surfaces, such as (111), (112) and (211) Miller indexes, are not considered in this paper for MnO2 nanorods following the research of Tompsett et al. [21]. And we believe they will make much more unforeseen characters for the micromechanism of MnO2 nanometer materials in our further research. 4. Discussion It is well known that surface effects are influenced by surface morphologies, due to both geometric and electronic structure considerations. In addition, surface activity is mainly determined
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4.8
6 Surface energy Cohesive energy
[(100×110)]
(110)
4
(100)
4.6 Nano(III) Nano(II) Nano(I)
2
4.4
-Ecohesive(eV)
-2
Esurface(J*m )
crystal
Nano(III) Nano(II) [(100×110)] Nano(I) 4.2
(100)
0
(110) crystal
0.0
0.2
0.4 Total ΔQA
0.6
0.8
1.0
Fig. 10. Sketch map of surface energy (Esurface ) and cohesive energy (Ecohesive ) along with summation of Mulliken charge (QAtotal ).
by surface electrons. Another purpose of this paper is to determine is there existed an optimized MnO2 nanorod structure, which exhibits good stability and excellent surface activity. In order to achieve this goal, the total value of QAtotal by taking the sum of QA100 and QA110 for the models was calculated at first in Fig. 10. It is found that the smallest value is for the (110) bulk surface, and the largest for nano(I). The trend of QAtotal is as follows: nano(I) > nano(II) > nano(III) > [(100 × 110)] bulk surface > (100) bulk surface > (110) bulk surface > MnO2 crystal. The lost electrons along surface planes maybe construct the electronic portion of the surface effect. Furthermore the surface energy and cohesive energy are determined as a function of Mulliken charge, QAtotal , and are also plotted in Fig. 10. The cohesive energy increases along with the QAtotal when moving from crystal to nano(I). It is likely that a stable nanorod (nano(X)) with enhanced properties can be synthesized between nano(III) and the [(100 × 110)] bulk surface. On the other hand, the surface energy is observed to increase along with QAtotal when moving from [(100 × 110)] to the bulk surface, and subsequently decreases when the structure becomes smaller as it moves to the nano(I) form. Systematically comparing the trends of both cohesive energy and surface energy, it can be seen that they have a crossing point that can be associated with the nano(X) structure, indicating that nano(X) has good stability and excellent surface activity. This is further supported by the data shown in Fig. 2. The summation of Mulliken charge, QAtotal , for this structure is expected to be in the 0.5–0.8 range. 5. Conclusion By calculating their geometry and electronic structure based on first principles, the evolution of surface effects and electronic structure for several morphologies of MnO2 were investigated carefully. The results show that: Following the increase of surface/bulk ratio, ␣, the evolution of surface energy has the trend [(100 × 110)] bulk surface> nano(III) > nano(II) > nano(I) > (110) bulk surface > (100) bulk surface > MnO2 crystal; and the evolution of cohesive energy is MnO2 crystal < (100) bulk surface < (110) bulk surface < nano(III) < [(100 × 110)] bulk surface < nano(II) < nano(I). (1) The relaxation energy based on the change of geometry between the bulk surfaces and nanorods shows the following trend: (100) bulk surface > (110) bulk surface > nano(I) > nano(II) > [(100 × 110)] bulk surface > nano(III) > MnO2 crystal. This trend indicates that
the growth of nanorods becomes harder as the relaxation energy decreases. Further analysis of the differences of bond distances and bond angles in the (100) or (110) Miller index planes, it is found the trend of bond angles, as well as the difference of bond distances in the perpendicular direction, is nano(III) > nano(II) > nano(I). (2) Mulliken population analysis showed that the largest absosum , is for nano(III), while the lute sum value difference, QMn-O smallest is for nano(I) only comparing the nanorod models. The differences in Mulliken charge, QA , are always found to be positive, which means that all of the MnO2 bulk surface and nanorod structures lost electrons to gain reduction properties compared with the crystal. The trend of QA is nano(I) > nano(II) > nano(III) > [(100 × 110)] bulk surface > (100) bulk surface > (110) bulk surface > MnO2 crystal. (3) PDOS shows that the structure stability of bulk surface is decreased because their Fermi energy is larger than that of MnO2 crystal. But the chemical activity of bulk surface and nanorod is increased for their decrease of bonding electrons and addition of anti-bonding electrons near Fermi energy. The results of molecular orbital show that the selective catalytic activity of MnO2 nanorods is originated from the preference of HOMO on (110) Miller index layers and LUMO on (100) Miller index layers, respectively. Acknowledgements This work was support from the projects of National Natural Science Foundation of China (Grant No. 51361026 and 11304146, Foundation of Jiangxi Educational Committee (GJJ160684), and Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle (Nanchang Hangkong University) (ST201522014). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apsusc.2017.05. 141. References [1] Y. Yang, J. Huang, S.W. Wang, S.B. Deng, B. Wang, G. Yu, Catalytic removal of gasous unintentional POPs on manganese oxide octahedral molecular sieves, Appl. Catal. B: Environ. 142–143 (2013) 568–578. [2] Y. Crespo, N. Seriani, A lithium peroxide precursor on the ␣-MnO2 (100) surface, J. Mater. Chem. A 2 (2014) 16538–16546. [3] Z.H. Lian, F.D. Liu, H. He, X.Y. Shi, J.S. Mo, Z.B. Wu, Manganese–niobium mixed oxide catalyst for the selective catalytic reduction of NOx with NH3 at low temperatures, Chem. Eng. J. 250 (2014) 390–398. [4] C.M. Vasconcellos, M.L.A. Goncalves, M.M. Pereira, N.M.F. Carvalho, Iron doped manganese oxide octahedral molecular sieve as potential catalyst for SOx removal at FCC, Appl. Catal. A: Gen. 498 (2015) 69–75. [5] X.Y. Wang, L.F. Mei, X.B. Xing, L.B. Liao, G.C. Lv, Z.H. Li, L.M. Wu, Mechanism and process of methylene blue degradation by manganese oxides under microwave irradiation, Appl. Catal. B: Environ. 160–161 (2014) 211–216. [6] G. Wu, Y. Gao, F.W. Ma, B.H. Zheng, L.G. Liu, H.Y. Sun, W. Wu, Catalytic oxidation of benzyl alcohol over manganese oxide supported on MCM-41 zeolite, Chem. Eng. J. 271 (2015) 14–22. [7] E. Eren, M. Guney, B. Eren, H. Gumus, Performance of layered birnessite-type manganese oxide in the thermal-catalytic degradation of polyamide 6, Appl. Catal. B: Environ. 132–133 (2013) 370–378. [8] K.D. Kwon, K. Refson, G. Sposito, Understanding the trends in transition metal sorption by vacancy sites in birnessite, Geochim. Cosmochim. Acta 101 (2013) 222–232. [9] B.K. Zhang, J. Liu, C.G. Zheng, M. Chang, Theoretical study of mercury species adsorption mechanism on MnO2 (110) surface, Chem. Eng. J. 256 (2014) 93–100. [10] M. Pourkhalil, A.Z. Moghaddam, A. Rashidi, J. Towfighi, Y. Mortazavi, Preparation of highly active manganese oxides supported on functionalized MWNTs for low temperature NOx reduction with NH3 , Appl. Surf. Sci. 279 (2013) 250–259.
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