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Toward data-enabled process optimization of deformable electronic polymer-based devices
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ScienceDirect Toward data-enabled process optimization of deformable electronic polymer-based devices Michael McBride1, Aaron Liu1, Elsa Reichmanis1,2,3 and Martha A Grover1
The development of new material systems is often an exercise in multi-objective optimization with an insurmountable number of design variables. Experiments generally rely on Edisonian approaches that only focus on a small segment of the total design space. The rise of materials informatics approaches provides a new paradigm to advance materials development but requires accurate descriptions of complex experimental data that are often unstandardized and incomplete. The field of deformable polymer-based electronic devices is an example system requiring desirable electrical and mechanical properties. In this article, advancements in the fabrication of deformable devices are presented with emphasis on processstructure-property relationships of the active, conjugated semiconducting polymer layer. Progress on materials informatics applied to experimental systems is then presented. Holistic, systematic approaches that encompass all data in a uniform and standard format provide an opportunity to rapidly advance materials development.
Both intrinsic and extrinsic (processing-dependent) properties that span multiple length and time scales must be considered simultaneously. Exhaustive exploration of all process-structure-property relationships that govern the behavior of a material class is infeasible due to time, energy, material, and monetary limitations. The rise of computational efforts stemming from the Materials Genome Initiative has enabled tremendous progress in screening thousands of potential material structures for intrinsic properties of interest [1,2,3] including HOMO/ LUMO levels [4], dielectric constants [5], catalytic activity [6], and thermodynamic stability of ternary inorganic materials [7], among others [8,9]. However, developing and optimizing processing methods to convert these candidate materials into usable products remains challenging. Unique to process design of materials is the heavy dependence of structure on processing conditions and ultimately the product’s properties.
Addresses 1 School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332, United States 2 School of Chemistry & Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA, 30332, United States 3 School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA, 30332, United States
As highly accurate mechanistic models are rare in materials processing development due to the exploratory nature of the field, empirical models are leveraged to guide process optimization using design of experiments [10,11]. In materials processing, there are a vast number of potential design variables to consider including composition, formulation, equipment selection, and settings. However, materials development research environments seldom possess the full suite of characterization equipment, expertise, and/or resources to fully investigate the entirety of the design space in a controlled environment with standardized methodologies. Because of this, researchers in the field must piece together findings from individual studies performed under differing conditions. A key bottleneck is the curation of accurate, representative, and interpretable datasets, which is needed to enable globally predictive empirical models for process optimization [1,12,13].
Corresponding authors: Reichmanis, Elsa ([email protected]), Grover, Martha A ([email protected])
Current Opinion in Chemical Engineering 2019, 27:72–80 This review comes from a themed issue on Frontiers of chemical engineering Edited by Rafiqul Gani
https://doi.org/10.1016/j.coche.2019.11.009 2211-3398/ã 2019 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Materials discovery and/or materials processing optimization is seldom a single-objective optimization problem. Instead, countless material properties must be co-optimized to afford a commercially viable product. Current Opinion in Chemical Engineering 2020, 27:72–80
Polymer-based organic electronic devices display many of the challenges associated with multi-objective materials development. The active material of such devices is a semiconducting polymer which provides the electrical properties of interest for a range of applications including displays, sensors, lighting, energy, and so on [14–18]. Semiconducting polymers possess a rigid, p-conjugated backbone to enable electron transport and side chains to promote solution-processability. The selection of the monomer has a profound impact on the final device morphology and thus properties, but these can also be www.sciencedirect.com
Data-enabled process optimization of deformable electronic polymer-based devices McBride et al. 73
tuned via processing conditions. While polymers are typically described as ‘plastics’ with the ability to be deformed (flexed, bent, stretched), the rigid backbone of semiconducting polymers generally limits the plasticity and mechanical properties compared to engineered plastics such as polypropylene or nylon [19]. The near infinite number of possible monomers for conjugated polymer synthesis, each with their own potentially unique optimal processing conditions, presents a design space that is large for traditional experimentation. Herein, we review key advancements in the field of deformable organic polymeric electronic devices to illustrate the need for data-driven methodologies to enable co-optimization of electrical and mechanical properties.
Flexible organic electronics Deformable electronic devices are multimaterial stacks with each layer possessing differing mechanical properties. To fabricate deformable electronic devices, semiconducting polymers are generally integrated with flexible substrates, either through sequential deposition on top of a substrate, such as a plastic substrate, or as a component within a polymer matrix (Figure 1) [20,21,22,23]. In each of these scenarios, the electronic properties are primarily governed by the interactions between neighboring conjugated polymer chains that form a percolative network
through which charges can transfer throughout the film; while mechanical properties are largely governed by the adhesive and cohesive interactions between all components [19,24]. These interactions are tunable via processing decisions in the form of solvent selection, chemical miscibility and compatibility, and deposition equipment settings, which ultimately depend upon the structure of the semiconducting polymer [25]. The electronic properties of polymer-based deformable devices can be extracted from two main platforms: transistors and photovoltaics. A transistor is a three-terminal device, in which the primary property of interest is the charge mobility, which is the speed at which electrons (or holes) move through the material under an applied electric field [26]. Practical applications also require control over the threshold voltage and on-off ratio. Photovoltaics devices are two terminal devices characterized by the power conversion efficiency, a measure of the portion of incoming solar energy converted into electricity [27]. Additionally, the open-circuit voltage and short-circuit current serve as useful metrics to compare photovoltaic devices. Regardless of the device, the Young’s modulus, yield point, and ability to retain performance across strain-release cycles are required mechanical properties. Root et al. provide a comprehensive review of the measurement of these mechanical properties [19].
Figure 1
(b)
(a)
Encapsulation layer (SEBS) Semiconductor Electrode S/D (CNT) Dielectric layer (SEBS) Gate (CNT)
Substrate (SEBS)
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Electrical Properties Transistors
Charge mobility, μ Threshold voltage, Vt On-Off ratio, Ion/Ioff
Photovoltaics Power conversion efficiency, PCE Open-circuit voltage, VOC Short-circuit current, ISC
Mechanical Properties Young’s modulus, E Yield point Cyclic loading Current Opinion in Chemical Engineering
Example device configurations to enable deformable electronics. (a) Electrodes (source and drain), polymer semiconductor, dielectric, and gate electrode are sequentially deposited on top of a deformable substrate. (From Zhang et al. [23]. Chemistry of Materials, 29:18. With permission. Copyright 2019 American Chemical Society). (b) Semiconducting polymer and elastomer monomers are pre-blended to form a polymer matrix with electrodes, and encapsulation layers are added in subsequent depositions. (From Xu et al. [22]. Science. With permission. Copyright 2019 AAAS). (c) Key electrical and mechanical properties describing performance of deformable devices. www.sciencedirect.com
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The design of semiconducting polymers has experienced tremendous growth since their discovery in the late 1970s, and has focused on the development of two main classes of materials. The first class, semicrystalline polymers, depend upon a rigid backbone and p–p stacking to form crystalline structures for long-range order required for enhanced electronic properties. In the mid-2000s a new class of materials, namely donor-acceptor (D-A) polymers, comprising alternating electron-rich donor moieties with electron-poor acceptor units arose; this class displayed significant electronic performance attributes despite a lack of crystallinity [15,28]. Process-dependent morphologies and thus properties have been observed in both classes of materials [29,30]. Persson et al. captured the immense size of the processing design space in a searchable database based on transistor devices for poly(3-hexylthiophene) (P3HT), presented in Figure 2 [25]. While the development of process-structure-property relationships that describe electrical properties has experienced tremendous advancements, consideration of mechanical properties in co-optimization with electrical properties remains an ongoing challenge. In the following sections, each of these two classes of materials is reviewed with an emphasis on the codesign of processing methods that target electrical and mechanical properties. Importance of thiophenes and semicrystalline polymers
Historically, semiconducting polymers emerged as an alternative technology to small molecule semiconductors for applications requiring organic active materials [31].
Regioregular polythiophenes, particularly poly(3-alkylthiophenes) (P3ATs), with a conjugated ring backbone structure that imparted the polymer with the ability to p-stack into crystalline domains, arose as an initial model semiconductor polymer. Ease of synthesis enabled research investigating aspects of synthetic variables on charge transport including the impact of reactive end-groups [32], solubilizing alkyl chain length [33], regioregularity [34], molecular weight [35,36], and dispersity [37]. Largely single-factor-at-a-time experiments highlighted the importance of regioregular and high molecular weight polythiophenes to achieve percolative charge transport [25]. Recently, alternative approaches to investigate synergistic enhancement of electrical properties afforded by blending samples with distinct molecular weights, regioregularities and/or side chains have been reported [38]. In semicrystalline conjugated polymers, it has been widely accepted that long-range order is a key determinant of electrical properties [39]. While the inclusion of interconnecting chains, termed tie-chains, can arise from an increase in bulk crystallinity/aggregation, improved local and global order is not a prerequisite for enhanced charge transport. Recent processing advances that aid the planarization of these long chains by reducing entanglements and promoting nucleation and growth into interconnected polymer networks has resulted in vast improvements in electrical performance. Such methods include solvent tuning [40], ultrasonication [41], and microfluidic flow induced crystallization [42]. While significant experimental resources have been spent studying
Figure 2
P3HT
Source
P3HT
Charge Carrier Mobility
Drain
Dielectric Gate
Material Number Average Molecular Weight (Mn) Polydispersity (PDI) Regioregularity (RR)
Solution Treatment Initial Concentration Solvents: - Volume Fractions - Boiling Point - Hansen Radius Aging Time/Temp. Sonication Time Cooling Regimen
Deposition Substrate Treatment Deposition Method Spin-Coated: - RPM - Time Dip-Coated: - Dip Rate - Time Film Thickness Environment (N2/Air)
Post-Processing Annealing Time Annealing Temp. Film Thickness
Device Architecture Electrode Configuration Electrode Material Channel Length Channel Width
Characterization Mobility Mobility Regime Environment
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Diagram of the method of fabricating and characterizing a P3HT-based organic field effect transistor and associated processing variables. (From Persson et al. [25]. Current Opinion in Solid State and Materials Science, 20:6. With permission. Copyright 2019 Elsevier). Current Opinion in Chemical Engineering 2020, 27:72–80
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P3ATs rather than other semicrystalline polymers such as poly(3,3000 -dialkylquarterthiophene) (PQT) [43,44] and poly [2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTT) [45], results obtained are expected to be general. Semicrystalline polymer systems exhibit improved electrical properties with improved ordering at both the mesoscale and macroscale.
diimide (NDI) [59] as acceptor moieties. Further, pyridinyl [60] and thiazole [61] moieties have been used to manipulate the electron affinity and ionization potential of the polymer. Notably, small structural changes to the conjugated cores can have a significant impact on the molecular packing, the conjugation length, and ultimately the electrical properties [62].
Unfortunately, the ordered structures formed from semiconducting polymers that are conducive to enhanced electrical properties are typically also detrimental to mechanical properties. Mechanical (tensile) properties are heavily dependent on chain entanglements and van der Waals cohesive forces between monomers. Process approaches that facilitate organization of conjugated polymers into more ordered domains have been shown to negatively impact the resistance of those thin films to mechanical deformation [46]. Lipomi et al. systematically explored relationships between polymer chain structure and mechanical properties using the poly(3-alkylthiophene) family as a model system [47,48]. Of particular importance was the relationship between alkyl chain length and both electrical (power conversion efficiencies: PCE) and mechanical (tensile modulus) properties. Comparing P3HT, poly(3-heptylthiophene) (P3HpT), and poly(3-octylthiophene) (P3OT), significant changes in both mechanical and electrical properties were observed upon varying side-chain length, with optimal properties found in P3HpT [47]. Notably the processing conditions (solvent selection, concentration, etc.) were kept constant despite changes in the solubility and thus processability of the polymers as a result of the side chain variations. These mechanisms and trends may be universal across material systems, but are costly to verify across the combined material and process design space. Moving beyond Edisonian approaches, experiments designed using all available information would be expected to provide more value from each experiment.
The relationship between electrical performance and mechanical properties in D-A copolymers has received significantly less attention compared to P3ATs. Lipomi et al. systematically synthesized and characterized the electrical (PCE) and mechanical (tensile) properties of combinations of 14 acceptor and 9 donor units as shown in Figure 3 [63]. They proposed two design guidelines both related to interactions between polymer chains: 1) fused rings increase the tensile modulus and 2) branched solubilizing side-chains decrease the tensile modulus. However, these guidelines were not globally predictive within this study, suggesting the presence of more complex process-structure-property relationships.
Advancements in donor-acceptor moieties and the amorphous polymers
Advances in data science and data mining
More recently, the design of semiconducting polymers has progressed toward a donor-acceptor motif where the frontier molecular orbitals can be fine-tuned for enhanced charge transport [15]. While D-A polymers often lack the crystallinity initially believed to be a requirement for efficient charge transport, their electrical performance has surpassed that of polythiophene-based semicrystalline polymers. Since the donor moiety is frequently unsubstituted, solubilization of D-A polymers frequently relies on side-chain engineering, whereby long, linear and branched side chains are introduced on the acceptor units [49–51]. Several alternative D and A moieties have been investigated for incorporation into these push-pull polymers including oligothiophenes [52], thienothiophene [53], benzodithiophene [54], and bithiophene [55] as donor units, with isoindigo [56], diketopyrrolopyrrole (DPP) [57,58], and naphthalene www.sciencedirect.com
Semiconducting polymers have also been blended with insulating elastomers to afford a stretchable layer for device applications. Here, it has been demonstrated that control of the phase behavior of the semiconducting polymer — insulating polymer — solvent system can lead to a closely packed percolative semiconducting network that exhibits improved electrical characteristics even at very low concentrations of conjugated polymer [22,23,64]. This processing method has been shown to enhance the electrical and mechanical performance of both amorphous D-A copolymer systems and more traditional semicrystalline conjugated polymers. Development of process-structure-property relationships for these blend systems to predict solvent and polymer compatibilities remains an ongoing challenge.
The adaptation of machine learning (ML), data science, and data mining tools to materials development has made tremendous strides in the past decade. For the discovery of new advanced material systems, high-throughput virtual experimentation has been enabled by density functional theory (DFT) calculations and molecular fingerprinting approaches [2,65,66]. Incorporation of materials domain knowledge enhances the efficiency and effectiveness of ML techniques, allowing such algorithms to be used on smaller experimental databases with fewer, more specific descriptors [13]. These techniques have been successfully leveraged to generate structured virtual datasets and predict polymer properties including polarization [67] and refractive index [68]. While advances in data science have made large progressions in using computational data for structure-property relationships, there exists a gap with respect to integrating Current Opinion in Chemical Engineering 2020, 27:72–80
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Figure 3
(a)
(d)
Donor
Tensile Modulus (GPa) Acceptor
(b)
(e)
Crack-onset Strain (%) Acceptor
Both
Donor
E. only CoS only
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Chemical structures of the (a) 13 acceptor monomers and (b) 8 donor monomers. (c) Table of the combination of D–A polymers measured in this work. Tensile moduli (Ef) were measured for a total of 43 polymers, the crack-onset strains (CoS) were measured for 47 polymers, and both quantities were measured for 39 polymers. The ‘missing’ combinations are the result of failure to obtain the material by synthesis, failure to create devices via roll coating, or insufficient material available. Resulting (d) tensile modulus and (e) crack-onset strain values. (From Roth et al. [63]. Chemistry of Materials, 28:7. With permission. Copyright 2019 American Chemical Society).
experimental data in materials development to learn process-structure-property relationships. The ability to efficiently curate relevant experimental data in a centralized databank is a key success factor, and there has been significant growth in developing methodologies to facilitate this process. Kim et al. used a set of natural language processing (NLP) techniques and toolkits to extract data from over 12 000 published sources to predict the results of various metal oxide synthesis routes [69]. While data extraction techniques and toolboxes have been demonstrated to semiautomate curation, usually non-trivial human interference is necessary to meaningfully organize the data. Particularly, for the construction of smaller datasets common for our polymer systems of interest, these techniques may not add a significant time savings for the curator [12]. Current Opinion in Chemical Engineering 2020, 27:72–80
Specifically, for flexible electronics development, the necessity to consider both mechanical and electrical properties leads to a complex, multi-objective optimization problem, which points to using these computational methods to gain insights in this domain. Co-optimizing for a set of desired performance indicators [Y] (Figure 1) with respect to a chosen set of design variables [X] (Figure 2) requires guidance from an objective function, which may either be derived from a theoretical model or an empirical correlation. However, the former requires mechanistic understanding which is limited due to the exploratory nature of the field, and both theoretical and empirical models necessitate the ability to create sufficiently rich experimental databases. www.sciencedirect.com
Data-enabled process optimization of deformable electronic polymer-based devices McBride et al. 77
While the reporting of performance indicators for polymeric organic devices is relatively consistent [24], it remains difficult to standardize the reporting of experimental design variables due to the considerable range of potential parameters. The objectives for optimization in polymer electronics are sensitive to structural features, and materials and processing parameters, which yields an incredibly expansive design space. These challenges compounded with the aforementioned database and data-mining gaps make it difficult to categorize and select meaningful features and enable traditional data-driven methods. Recently, we have demonstrated that a classification approach can reveal insights on the hole mobility of P3HT using a data-mined literature database containing sparse and missing data [12]. Within this approach, a reduced design space that includes all high performing points is identified to focus future experimental efforts, as illustrated in Figure 4. However, the general inconsistency in reporting, compounded with the sparsity of available data in polymer electronics, can lead to difficulty in applying machine learning techniques to perform
optimization exercises and learn trends from assembled small databases.
Perspective and outlook The materials community is continuing to embrace and incorporate holistic and systematic approaches for discovery and optimization of materials and devices. A nontrivial pool of domain expertise has been gathered in the field of polymer electronics but addressing key areas for community consensus is crucial to enable accelerated progress through data-driven methodologies. Moving beyond traditional design of experiments, a greater emphasis on quantitation of experimental data, standardization of experimental protocols, and integration of all sources of information will provide better understanding and efficiency in the design and processing of complex macromolecules and mixtures. Dissemination of software tools customized for materials informatics should accelerate this trend and provide greater uniformity for database construction.
Figure 4
(b) 6
High Low Design Region
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Classification approach of (a) one-dimensional, (b) two-dimensional, and (c) three-dimensional reduced design spaces containing devices with charge mobility values exceeding 0.1 cm2/V-s. Blue squares are data points above the cutoff, red dots represent points below the cutoff but within the target design region and red x markers indicate all other data points below the cutoff. (From McBride et al. [12]. Processes. With permission. Copyright 2019 MDPI). www.sciencedirect.com
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In the field of deformable organic polymeric devices, scale-up of laboratory discoveries to high-throughput manufacturing processes will require predictive models for electrical and mechanical properties, together with models for their processing characteristics including viscosity and fibril growth rate. Such models may be derived from first-principles, from experimental data, or more likely from the systematic use of both types of information.
Conflict of interest statement Nothing declared.
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Acknowledgements Financial support from Konica Minolta and from NSF-DMREF (DMR 1922111) is gratefully acknowledged.
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ScienceDirect Toward data-enabled process optimization of deformable electronic polymer-based devices Michael McBride1, Aaron Liu1, Elsa Reichmanis1,2,3 and Martha A Grover1
The development of new material systems is often an exercise in multi-objective optimization with an insurmountable number of design variables. Experiments generally rely on Edisonian approaches that only focus on a small segment of the total design space. The rise of materials informatics approaches provides a new paradigm to advance materials development but requires accurate descriptions of complex experimental data that are often unstandardized and incomplete. The field of deformable polymer-based electronic devices is an example system requiring desirable electrical and mechanical properties. In this article, advancements in the fabrication of deformable devices are presented with emphasis on processstructure-property relationships of the active, conjugated semiconducting polymer layer. Progress on materials informatics applied to experimental systems is then presented. Holistic, systematic approaches that encompass all data in a uniform and standard format provide an opportunity to rapidly advance materials development.
Both intrinsic and extrinsic (processing-dependent) properties that span multiple length and time scales must be considered simultaneously. Exhaustive exploration of all process-structure-property relationships that govern the behavior of a material class is infeasible due to time, energy, material, and monetary limitations. The rise of computational efforts stemming from the Materials Genome Initiative has enabled tremendous progress in screening thousands of potential material structures for intrinsic properties of interest [1,2,3] including HOMO/ LUMO levels [4], dielectric constants [5], catalytic activity [6], and thermodynamic stability of ternary inorganic materials [7], among others [8,9]. However, developing and optimizing processing methods to convert these candidate materials into usable products remains challenging. Unique to process design of materials is the heavy dependence of structure on processing conditions and ultimately the product’s properties.
Addresses 1 School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332, United States 2 School of Chemistry & Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA, 30332, United States 3 School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA, 30332, United States
As highly accurate mechanistic models are rare in materials processing development due to the exploratory nature of the field, empirical models are leveraged to guide process optimization using design of experiments [10,11]. In materials processing, there are a vast number of potential design variables to consider including composition, formulation, equipment selection, and settings. However, materials development research environments seldom possess the full suite of characterization equipment, expertise, and/or resources to fully investigate the entirety of the design space in a controlled environment with standardized methodologies. Because of this, researchers in the field must piece together findings from individual studies performed under differing conditions. A key bottleneck is the curation of accurate, representative, and interpretable datasets, which is needed to enable globally predictive empirical models for process optimization [1,12,13].
Corresponding authors: Reichmanis, Elsa ([email protected]), Grover, Martha A ([email protected])
Current Opinion in Chemical Engineering 2019, 27:72–80 This review comes from a themed issue on Frontiers of chemical engineering Edited by Rafiqul Gani
https://doi.org/10.1016/j.coche.2019.11.009 2211-3398/ã 2019 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Materials discovery and/or materials processing optimization is seldom a single-objective optimization problem. Instead, countless material properties must be co-optimized to afford a commercially viable product. Current Opinion in Chemical Engineering 2020, 27:72–80
Polymer-based organic electronic devices display many of the challenges associated with multi-objective materials development. The active material of such devices is a semiconducting polymer which provides the electrical properties of interest for a range of applications including displays, sensors, lighting, energy, and so on [14–18]. Semiconducting polymers possess a rigid, p-conjugated backbone to enable electron transport and side chains to promote solution-processability. The selection of the monomer has a profound impact on the final device morphology and thus properties, but these can also be www.sciencedirect.com
Data-enabled process optimization of deformable electronic polymer-based devices McBride et al. 73
tuned via processing conditions. While polymers are typically described as ‘plastics’ with the ability to be deformed (flexed, bent, stretched), the rigid backbone of semiconducting polymers generally limits the plasticity and mechanical properties compared to engineered plastics such as polypropylene or nylon [19]. The near infinite number of possible monomers for conjugated polymer synthesis, each with their own potentially unique optimal processing conditions, presents a design space that is large for traditional experimentation. Herein, we review key advancements in the field of deformable organic polymeric electronic devices to illustrate the need for data-driven methodologies to enable co-optimization of electrical and mechanical properties.
Flexible organic electronics Deformable electronic devices are multimaterial stacks with each layer possessing differing mechanical properties. To fabricate deformable electronic devices, semiconducting polymers are generally integrated with flexible substrates, either through sequential deposition on top of a substrate, such as a plastic substrate, or as a component within a polymer matrix (Figure 1) [20,21,22,23]. In each of these scenarios, the electronic properties are primarily governed by the interactions between neighboring conjugated polymer chains that form a percolative network
through which charges can transfer throughout the film; while mechanical properties are largely governed by the adhesive and cohesive interactions between all components [19,24]. These interactions are tunable via processing decisions in the form of solvent selection, chemical miscibility and compatibility, and deposition equipment settings, which ultimately depend upon the structure of the semiconducting polymer [25]. The electronic properties of polymer-based deformable devices can be extracted from two main platforms: transistors and photovoltaics. A transistor is a three-terminal device, in which the primary property of interest is the charge mobility, which is the speed at which electrons (or holes) move through the material under an applied electric field [26]. Practical applications also require control over the threshold voltage and on-off ratio. Photovoltaics devices are two terminal devices characterized by the power conversion efficiency, a measure of the portion of incoming solar energy converted into electricity [27]. Additionally, the open-circuit voltage and short-circuit current serve as useful metrics to compare photovoltaic devices. Regardless of the device, the Young’s modulus, yield point, and ability to retain performance across strain-release cycles are required mechanical properties. Root et al. provide a comprehensive review of the measurement of these mechanical properties [19].
Figure 1
(b)
(a)
Encapsulation layer (SEBS) Semiconductor Electrode S/D (CNT) Dielectric layer (SEBS) Gate (CNT)
Substrate (SEBS)
(c)
Electrical Properties Transistors
Charge mobility, μ Threshold voltage, Vt On-Off ratio, Ion/Ioff
Photovoltaics Power conversion efficiency, PCE Open-circuit voltage, VOC Short-circuit current, ISC
Mechanical Properties Young’s modulus, E Yield point Cyclic loading Current Opinion in Chemical Engineering
Example device configurations to enable deformable electronics. (a) Electrodes (source and drain), polymer semiconductor, dielectric, and gate electrode are sequentially deposited on top of a deformable substrate. (From Zhang et al. [23]. Chemistry of Materials, 29:18. With permission. Copyright 2019 American Chemical Society). (b) Semiconducting polymer and elastomer monomers are pre-blended to form a polymer matrix with electrodes, and encapsulation layers are added in subsequent depositions. (From Xu et al. [22]. Science. With permission. Copyright 2019 AAAS). (c) Key electrical and mechanical properties describing performance of deformable devices. www.sciencedirect.com
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The design of semiconducting polymers has experienced tremendous growth since their discovery in the late 1970s, and has focused on the development of two main classes of materials. The first class, semicrystalline polymers, depend upon a rigid backbone and p–p stacking to form crystalline structures for long-range order required for enhanced electronic properties. In the mid-2000s a new class of materials, namely donor-acceptor (D-A) polymers, comprising alternating electron-rich donor moieties with electron-poor acceptor units arose; this class displayed significant electronic performance attributes despite a lack of crystallinity [15,28]. Process-dependent morphologies and thus properties have been observed in both classes of materials [29,30]. Persson et al. captured the immense size of the processing design space in a searchable database based on transistor devices for poly(3-hexylthiophene) (P3HT), presented in Figure 2 [25]. While the development of process-structure-property relationships that describe electrical properties has experienced tremendous advancements, consideration of mechanical properties in co-optimization with electrical properties remains an ongoing challenge. In the following sections, each of these two classes of materials is reviewed with an emphasis on the codesign of processing methods that target electrical and mechanical properties. Importance of thiophenes and semicrystalline polymers
Historically, semiconducting polymers emerged as an alternative technology to small molecule semiconductors for applications requiring organic active materials [31].
Regioregular polythiophenes, particularly poly(3-alkylthiophenes) (P3ATs), with a conjugated ring backbone structure that imparted the polymer with the ability to p-stack into crystalline domains, arose as an initial model semiconductor polymer. Ease of synthesis enabled research investigating aspects of synthetic variables on charge transport including the impact of reactive end-groups [32], solubilizing alkyl chain length [33], regioregularity [34], molecular weight [35,36], and dispersity [37]. Largely single-factor-at-a-time experiments highlighted the importance of regioregular and high molecular weight polythiophenes to achieve percolative charge transport [25]. Recently, alternative approaches to investigate synergistic enhancement of electrical properties afforded by blending samples with distinct molecular weights, regioregularities and/or side chains have been reported [38]. In semicrystalline conjugated polymers, it has been widely accepted that long-range order is a key determinant of electrical properties [39]. While the inclusion of interconnecting chains, termed tie-chains, can arise from an increase in bulk crystallinity/aggregation, improved local and global order is not a prerequisite for enhanced charge transport. Recent processing advances that aid the planarization of these long chains by reducing entanglements and promoting nucleation and growth into interconnected polymer networks has resulted in vast improvements in electrical performance. Such methods include solvent tuning [40], ultrasonication [41], and microfluidic flow induced crystallization [42]. While significant experimental resources have been spent studying
Figure 2
P3HT
Source
P3HT
Charge Carrier Mobility
Drain
Dielectric Gate
Material Number Average Molecular Weight (Mn) Polydispersity (PDI) Regioregularity (RR)
Solution Treatment Initial Concentration Solvents: - Volume Fractions - Boiling Point - Hansen Radius Aging Time/Temp. Sonication Time Cooling Regimen
Deposition Substrate Treatment Deposition Method Spin-Coated: - RPM - Time Dip-Coated: - Dip Rate - Time Film Thickness Environment (N2/Air)
Post-Processing Annealing Time Annealing Temp. Film Thickness
Device Architecture Electrode Configuration Electrode Material Channel Length Channel Width
Characterization Mobility Mobility Regime Environment
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Diagram of the method of fabricating and characterizing a P3HT-based organic field effect transistor and associated processing variables. (From Persson et al. [25]. Current Opinion in Solid State and Materials Science, 20:6. With permission. Copyright 2019 Elsevier). Current Opinion in Chemical Engineering 2020, 27:72–80
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P3ATs rather than other semicrystalline polymers such as poly(3,3000 -dialkylquarterthiophene) (PQT) [43,44] and poly [2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTT) [45], results obtained are expected to be general. Semicrystalline polymer systems exhibit improved electrical properties with improved ordering at both the mesoscale and macroscale.
diimide (NDI) [59] as acceptor moieties. Further, pyridinyl [60] and thiazole [61] moieties have been used to manipulate the electron affinity and ionization potential of the polymer. Notably, small structural changes to the conjugated cores can have a significant impact on the molecular packing, the conjugation length, and ultimately the electrical properties [62].
Unfortunately, the ordered structures formed from semiconducting polymers that are conducive to enhanced electrical properties are typically also detrimental to mechanical properties. Mechanical (tensile) properties are heavily dependent on chain entanglements and van der Waals cohesive forces between monomers. Process approaches that facilitate organization of conjugated polymers into more ordered domains have been shown to negatively impact the resistance of those thin films to mechanical deformation [46]. Lipomi et al. systematically explored relationships between polymer chain structure and mechanical properties using the poly(3-alkylthiophene) family as a model system [47,48]. Of particular importance was the relationship between alkyl chain length and both electrical (power conversion efficiencies: PCE) and mechanical (tensile modulus) properties. Comparing P3HT, poly(3-heptylthiophene) (P3HpT), and poly(3-octylthiophene) (P3OT), significant changes in both mechanical and electrical properties were observed upon varying side-chain length, with optimal properties found in P3HpT [47]. Notably the processing conditions (solvent selection, concentration, etc.) were kept constant despite changes in the solubility and thus processability of the polymers as a result of the side chain variations. These mechanisms and trends may be universal across material systems, but are costly to verify across the combined material and process design space. Moving beyond Edisonian approaches, experiments designed using all available information would be expected to provide more value from each experiment.
The relationship between electrical performance and mechanical properties in D-A copolymers has received significantly less attention compared to P3ATs. Lipomi et al. systematically synthesized and characterized the electrical (PCE) and mechanical (tensile) properties of combinations of 14 acceptor and 9 donor units as shown in Figure 3 [63]. They proposed two design guidelines both related to interactions between polymer chains: 1) fused rings increase the tensile modulus and 2) branched solubilizing side-chains decrease the tensile modulus. However, these guidelines were not globally predictive within this study, suggesting the presence of more complex process-structure-property relationships.
Advancements in donor-acceptor moieties and the amorphous polymers
Advances in data science and data mining
More recently, the design of semiconducting polymers has progressed toward a donor-acceptor motif where the frontier molecular orbitals can be fine-tuned for enhanced charge transport [15]. While D-A polymers often lack the crystallinity initially believed to be a requirement for efficient charge transport, their electrical performance has surpassed that of polythiophene-based semicrystalline polymers. Since the donor moiety is frequently unsubstituted, solubilization of D-A polymers frequently relies on side-chain engineering, whereby long, linear and branched side chains are introduced on the acceptor units [49–51]. Several alternative D and A moieties have been investigated for incorporation into these push-pull polymers including oligothiophenes [52], thienothiophene [53], benzodithiophene [54], and bithiophene [55] as donor units, with isoindigo [56], diketopyrrolopyrrole (DPP) [57,58], and naphthalene www.sciencedirect.com
Semiconducting polymers have also been blended with insulating elastomers to afford a stretchable layer for device applications. Here, it has been demonstrated that control of the phase behavior of the semiconducting polymer — insulating polymer — solvent system can lead to a closely packed percolative semiconducting network that exhibits improved electrical characteristics even at very low concentrations of conjugated polymer [22,23,64]. This processing method has been shown to enhance the electrical and mechanical performance of both amorphous D-A copolymer systems and more traditional semicrystalline conjugated polymers. Development of process-structure-property relationships for these blend systems to predict solvent and polymer compatibilities remains an ongoing challenge.
The adaptation of machine learning (ML), data science, and data mining tools to materials development has made tremendous strides in the past decade. For the discovery of new advanced material systems, high-throughput virtual experimentation has been enabled by density functional theory (DFT) calculations and molecular fingerprinting approaches [2,65,66]. Incorporation of materials domain knowledge enhances the efficiency and effectiveness of ML techniques, allowing such algorithms to be used on smaller experimental databases with fewer, more specific descriptors [13]. These techniques have been successfully leveraged to generate structured virtual datasets and predict polymer properties including polarization [67] and refractive index [68]. While advances in data science have made large progressions in using computational data for structure-property relationships, there exists a gap with respect to integrating Current Opinion in Chemical Engineering 2020, 27:72–80
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Figure 3
(a)
(d)
Donor
Tensile Modulus (GPa) Acceptor
(b)
(e)
Crack-onset Strain (%) Acceptor
Both
Donor
E. only CoS only
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(c)
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Chemical structures of the (a) 13 acceptor monomers and (b) 8 donor monomers. (c) Table of the combination of D–A polymers measured in this work. Tensile moduli (Ef) were measured for a total of 43 polymers, the crack-onset strains (CoS) were measured for 47 polymers, and both quantities were measured for 39 polymers. The ‘missing’ combinations are the result of failure to obtain the material by synthesis, failure to create devices via roll coating, or insufficient material available. Resulting (d) tensile modulus and (e) crack-onset strain values. (From Roth et al. [63]. Chemistry of Materials, 28:7. With permission. Copyright 2019 American Chemical Society).
experimental data in materials development to learn process-structure-property relationships. The ability to efficiently curate relevant experimental data in a centralized databank is a key success factor, and there has been significant growth in developing methodologies to facilitate this process. Kim et al. used a set of natural language processing (NLP) techniques and toolkits to extract data from over 12 000 published sources to predict the results of various metal oxide synthesis routes [69]. While data extraction techniques and toolboxes have been demonstrated to semiautomate curation, usually non-trivial human interference is necessary to meaningfully organize the data. Particularly, for the construction of smaller datasets common for our polymer systems of interest, these techniques may not add a significant time savings for the curator [12]. Current Opinion in Chemical Engineering 2020, 27:72–80
Specifically, for flexible electronics development, the necessity to consider both mechanical and electrical properties leads to a complex, multi-objective optimization problem, which points to using these computational methods to gain insights in this domain. Co-optimizing for a set of desired performance indicators [Y] (Figure 1) with respect to a chosen set of design variables [X] (Figure 2) requires guidance from an objective function, which may either be derived from a theoretical model or an empirical correlation. However, the former requires mechanistic understanding which is limited due to the exploratory nature of the field, and both theoretical and empirical models necessitate the ability to create sufficiently rich experimental databases. www.sciencedirect.com
Data-enabled process optimization of deformable electronic polymer-based devices McBride et al. 77
While the reporting of performance indicators for polymeric organic devices is relatively consistent [24], it remains difficult to standardize the reporting of experimental design variables due to the considerable range of potential parameters. The objectives for optimization in polymer electronics are sensitive to structural features, and materials and processing parameters, which yields an incredibly expansive design space. These challenges compounded with the aforementioned database and data-mining gaps make it difficult to categorize and select meaningful features and enable traditional data-driven methods. Recently, we have demonstrated that a classification approach can reveal insights on the hole mobility of P3HT using a data-mined literature database containing sparse and missing data [12]. Within this approach, a reduced design space that includes all high performing points is identified to focus future experimental efforts, as illustrated in Figure 4. However, the general inconsistency in reporting, compounded with the sparsity of available data in polymer electronics, can lead to difficulty in applying machine learning techniques to perform
optimization exercises and learn trends from assembled small databases.
Perspective and outlook The materials community is continuing to embrace and incorporate holistic and systematic approaches for discovery and optimization of materials and devices. A nontrivial pool of domain expertise has been gathered in the field of polymer electronics but addressing key areas for community consensus is crucial to enable accelerated progress through data-driven methodologies. Moving beyond traditional design of experiments, a greater emphasis on quantitation of experimental data, standardization of experimental protocols, and integration of all sources of information will provide better understanding and efficiency in the design and processing of complex macromolecules and mixtures. Dissemination of software tools customized for materials informatics should accelerate this trend and provide greater uniformity for database construction.
Figure 4
(b) 6
High Low Design Region
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100 80
5 4.5 4 3.5 3 2.5
60
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40 20 0 I R . t . e s s e n h h M Mw PD R onc oin diu Rat es mp im ngt idt n Te l T e W P a n C k i R l g L l a ic l a n iti ili en In Bo ns a H
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Classification approach of (a) one-dimensional, (b) two-dimensional, and (c) three-dimensional reduced design spaces containing devices with charge mobility values exceeding 0.1 cm2/V-s. Blue squares are data points above the cutoff, red dots represent points below the cutoff but within the target design region and red x markers indicate all other data points below the cutoff. (From McBride et al. [12]. Processes. With permission. Copyright 2019 MDPI). www.sciencedirect.com
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In the field of deformable organic polymeric devices, scale-up of laboratory discoveries to high-throughput manufacturing processes will require predictive models for electrical and mechanical properties, together with models for their processing characteristics including viscosity and fibril growth rate. Such models may be derived from first-principles, from experimental data, or more likely from the systematic use of both types of information.
Conflict of interest statement Nothing declared.
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Acknowledgements Financial support from Konica Minolta and from NSF-DMREF (DMR 1922111) is gratefully acknowledged.
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