Hierarchical Modeling of the Local Reaction Environment in Electrocatalysis.

ConspectusElectrocatalytic reactions, such as oxygen reduction/evolution reactions and CO2 reduction reaction that are pivotal for the energy transition, are multistep processes that occur in a nanoscale electric double layer (EDL) at a solid-liquid interface. Conventional analyses based on the Sabatier principle, using binding energies or effective electronic structure properties such as the d-band center as descriptors, are able to grasp overall trends in catalytic activity in specific groups of catalysts. However, thermodynamic approaches often fail to account for electrolyte effects that arise in the EDL, including pH, cation, and anion effects. These effects exert strong impacts on electrocatalytic reactions. There is growing consensus that the local reaction environment (LRE) prevailing in the EDL is the key to deciphering these complex and hitherto perplexing electrolyte effects. Increasing attention is thus paid to designing electrolyte properties, positioning the LRE at center stage. To this end, unraveling the LRE is becoming essential for designing electrocatalysts with specifically tailored properties, which could enable much needed breakthroughs in electrochemical energy science.Theory and modeling are getting more and more important and powerful in addressing this multifaceted problem that involves physical phenomena at different scales and interacting in a multidimensional parametric space. Theoretical models developed for this purpose should treat intrinsic multistep kinetics of electrocatalytic reactions, EDL effects from subnm scale to the scale of 10 nm, and mass transport phenomena bridging scales from <0.1 to 100 µm. Given the diverse physical phenomena and scales involved, it is evident that the challenge at hand surpasses the capabilities of any single theoretical or computational approach.In this Account, we present a hierarchical theoretical framework to address the above challenge. It seamlessly integrates several modules: (i) microkinetic modeling that accounts for various reaction pathways; (ii) an LRE model that describes the interfacial region extending from the nanometric EDL continuously to the solution bulk; (iii) first-principles calculations that provide parameters, e.g., adsorption energies, activation barriers and EDL parameters. The microkinetic model considers all elementary steps without designating an a priori rate-determining step. The kinetics of these elementary steps are expressed in terms of local concentrations, potential and electric field that are codetermined by EDL charging and mass transport in the LRE model. Vital insights on electrode kinetic phenomena, i.e., potential-dependent Tafel slopes, cation effects, and pH effects, obtained from this hierarchical framework are then reviewed. Finally, an outlook on further improvement of the model framework is presented, in view of recent developments in first-principles based simulation of electrocatalysis, observations of dynamic reconstruction of catalysts, and machine-learning assisted computational simulations.

Pure Water Splitting Driven by Overlapping Electric Double Layers.

In pursuit of a sustainable future powered by renewable energy, hydrogen production through water splitting should achieve high energy efficiency with economical materials. Here, we present a nanofluidic electrolyzer that leverages overlapping cathode and anode electric double layers (EDLs) to drive the splitting of pure water. Convective flow is introduced between the nanogap electrodes to suppress the crossover of generated gases. The strong electric field within the overlapping EDLs enhances ion migration and facilitates the dissociation of water molecules. Acidic and basic environments, which are created in situ at the cathode and anode, respectively, enable the use of nonprecious metal catalysts. All these merits allow the reactor to exhibit a current density of 2.8 A·cm-2 at 1.7 V with a nickel anode. This paves the way toward a new type of water electrolyzer that needs no membrane, no supporting electrolyte, and no precious metal catalysts.

Deep learning-enhanced characterization of bubble dynamics in proton exchange membrane water electrolyzers.

The ever-increasing utility of imaging technology in proton exchange membrane water electrolyzer research raises the demand for rapid and precise image analysis. In particular, for optical video recordings, the challenge primarily lies in the large number of frames that impede the delineation of bubble dynamics with standard methods. In order to address this problem, the present study supports the automation of data analysis to facilitate swift, comprehensive, and measurable insights from captured imagery. We present a deep learning-based framework to perform high-throughput analyses of bubble dynamics using optical images of proton exchange membrane water electrolyzers. Leveraging a relatively small annotated imaging dataset of just 35 images, various configurations of the U-Net architecture were trained to perform bubble segmentation tasks. The best model achieved a precision of 95%, a recall of 78%, and an F1-score of 86% on the validation set. Subsequent to segmentation, the methodology enabled the rapid extraction of parameters such as time-resolved bubble area, size distributions, bubble position probability density, and individual bubble shape analytics. The findings underscore the potential of deep learning to enhance the analysis of polymer electrolyte membrane water electrolyzer imaging, offering a path toward more efficient and informative evaluations in electrochemical research.

Incorporating Electrolyte Correlation Effects into Variational Models of Electrochemical Interfaces.

We propose a way for obtaining a classical free energy density functional for electrolytes based on a first-principle many-body partition function. Via a one-loop expansion, we include coulombic correlations beyond the conventional mean-field approximation. To examine electrochemical interfaces, we integrate the electrolyte free energy functional into a hybrid quantum-classical model. This scheme self-consistently couples electronic, ionic, and solvent degrees of freedom and incorporates electrolyte correlation effects. The derived free energy functional causes a correlation-induced enhancement in interfacial counterion density and leads to an overall increase in capacitance. This effect is partially compensated by a reduction of the dielectric permittivity of interfacial water. At larger surface charge densities, ion crowding at the interface stifles these correlation effects. While scientifically intriguing already at planar interfaces, we anticipate these correlation effects to play an essential role for electrolytes in nanoconfinement.

UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning.

This work presents the development and implementation of a deep learning-based workflow for autonomous image analysis in nanoscience. A versatile, agnostic, and configurable tool was developed to generate instance-segmented imaging datasets of nanoparticles. The synthetic generator tool employs domain randomization to expand the image/mask pairs dataset for training supervised deep learning models. The approach eliminates tedious manual annotation and allows training of high-performance models for microscopy image analysis based on convolutional neural networks. We demonstrate how the expanded training set can significantly improve the performance of the classification and instance segmentation models for a variety of nanoparticle shapes, ranging from spherical-, cubic-, to rod-shaped nanoparticles. Finally, the trained models were deployed in a cloud-based analytics platform for the autonomous particle analysis of microscopy images.

Dynamic Response of Ion Transport in Nanoconfined Electrolytes.

Ion transport in nanoconfined electrolytes exhibits nonlinear effects caused by large driving forces and pronounced boundary effects. An improved understanding of these impacts is urgently needed to guide the design of key components of the electrochemical energy systems. Herein, we employ a nonlinear Poisson-Nernst-Planck theory to describe ion transport in nanoconfined electrolytes coupled with two sets of boundary conditions to mimic different cell configurations in experiments. A peculiar nonmonotonic charging behavior is discovered when the electrolyte is placed between a blocking electrode and an electrolyte reservoir, while normal monotonic behaviors are seen when the electrolyte is placed between two blocking electrodes. We reveal that impedance shapes depend on the definition of surface charge and the electrode potential. Particularly, an additional arc can emerge in the intermediate-frequency range at potentials away from the potential of zero charge. The obtained insights are instrumental to experimental characterization of ion transport in nanoconfined electrolytes.

Correlated surface-charging behaviors of two electrodes in an electrochemical cell.

It is revealed herein that surface-charging behaviors of the two electrodes constituting an electrochemical cell cannot be described independently by their respective electric double-layer (EDL) properties. Instead, they are correlated in such a way that the surface-charging behavior of each electrode is determined by the EDL and the reaction kinetics at both electrodes. Two fundamental equations describing the correlated surface-charging behaviors are derived, and approximate analytical solutions are obtained at low and high current densities, respectively, to facilitate transparent understanding. Important implications of the presented conceptual analysis for theoretical and computational electrochemistry are discussed. A strategy of modulating the activity of one electrode by tuning EDL parameters of the other in a two-electrode electrochemical cell is demonstrated.

Stay Hydrated! Impact of Solvation Phenomena on the CO2 Reduction Reaction at Pb(100) and Ag(100) surfaces.

Herein, a comprehensive computational study of the impact of solvation on the reduction reaction of CO2 to formic acid (HCOOH) and carbon monoxide on Pb(100) and Ag(100) surfaces is presented. Results further the understanding of how solvation phenomena influence the adsorption energies of reaction intermediates. We applied an explicit solvation scheme harnessing a combined density functional theory (DFT)/microkinetic modeling approach for the CO2 reduction reaction. This approach reveals high selectivities for CO formation at Ag and HCOOH formation on Pb, resolving the prior disparity between ab initio calculations and experimental observations. Furthermore, the detailed analysis of adsorption energies of relevant reaction intermediates shows that the total number of hydrogen bonds formed by HCOO plays a primary role for the adsorption strength of intermediates and the electrocatalytic activity. Results emphasize the importance of explicit solvation for adsorption and electrochemical reaction phenomena on metal surfaces.

Low-spin state of Fe in Fe-doped NiOOH electrocatalysts.

Doping with Fe boosts the electrocatalytic performance of NiOOH for the oxygen evolution reaction (OER). To understand this effect, we have employed state-of-the-art electronic structure calculations and thermodynamic modeling. Our study reveals that at low concentrations Fe exists in a low-spin state. Only this spin state explains the large solubility limit of Fe and similarity of Fe-O and Ni-O bond lengths measured in the Fe-doped NiOOH phase. The low-spin state renders the surface Fe sites highly active for the OER. The low-to-high spin transition at the Fe concentration of ~ 25% is consistent with the experimentally determined solubility limit of Fe in NiOOH. The thermodynamic overpotentials computed for doped and pure materials, η = 0.42 V and 0.77 V, agree well with the measured values. Our results indicate a key role of the low-spin state of Fe for the OER activity of Fe-doped NiOOH electrocatalysts.

pH Effects in a Model Electrocatalytic Reaction Disentangled.

Varying the solution pH not only changes the reactant concentrations in bulk solution but also the local reaction environment (LRE) that is shaped furthermore by macroscopic mass transport and microscopic electric double layer (EDL) effects. Understanding ubiquitous pH effects in electrocatalysis requires disentangling these interwoven factors, which is a difficult, if not impossible, task without physical modeling. Herein, we demonstrate how a hierarchical model that integrates microkinetics, double-layer charging, and macroscopic mass transport can help understand pH effects of the formic acid oxidation reaction (FAOR). In terms of the relation between the peak activity and the solution pH, intrinsic pH effects without consideration of changes in the LRE would lead to a bell-shaped curve with a peak at pH = 6. Adding only macroscopic mass transport, we can already reproduce qualitatively the experimentally observed trapezoidal shape with a plateau between pH 5 and 10 in perchlorate and sulfate solutions. A quantitative agreement with experimental data requires consideration of EDL effects beyond Frumkin correlations. Specifically, the peculiar nonmonotonic surface charging relation affects the free energies of adsorbed intermediates. We further discuss pH effects of FAOR in phosphate and chloride-containing solutions, for which anion adsorption becomes important. This study underpins the importance of a full consideration of multiple interrelated factors for the interpretation of pH effects in electrocatalysis.

Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments.

Replacing fossil fuels with energy sources and carriers that are sustainable, environmentally benign, and affordable is amongst the most pressing challenges for future socio-economic development. To that goal, hydrogen is presumed to be the most promising energy carrier. Electrocatalytic water splitting, if driven by green electricity, would provide hydrogen with minimal CO2 footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. Recently developed strategies are described for the optimisation and discovery of active and durable materials for electrodes that ever-increasingly harness first-principles calculations and machine learning. In addition, a technoeconomic analysis of water electrolysis is included that allows an assessment of the extent to which a large-scale implementation of water splitting can help to combat climate change. This review article is intended to cross-pollinate and strengthen efforts from fundamental understanding to technical implementation and to improve the 'junctions' between the field's physical chemists, materials scientists and engineers, as well as stimulate much-needed exchange among these groups on challenges encountered in the different domains.

Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells.

The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.

Cation Overcrowding Effect on the Oxygen Evolution Reaction.

The influence of electrolyte ions on the catalytic activity of electrode/electrolyte interfaces is a controversial topic for many electrocatalytic reactions. Herein, we focus on an effect that is usually neglected, namely, how the local reaction conditions are shaped by nonspecifically adsorbed cations. We scrutinize the oxygen evolution reaction (OER) at nickel (oxy)hydroxide catalysts, using a physicochemical model that integrates density functional theory calculations, a microkinetic submodel, and a mean-field submodel of the electric double layer. The aptness of the model is verified by comparison with experiments. The robustness of model-based insights against uncertainties and variations in model parameters is examined, with a sensitivity analysis using Monto Carlo simulations. We interpret the decrease in OER activity with the increasing effective size of electrolyte cations as a consequence of cation overcrowding near the negatively charged electrode surface. The same reasoning could explain why the OER activity increases with solution pH on the RHE scale and why the OER activity decreases in the presence of bivalent cations. Overall, this work stresses the importance of correctly accounting for local reaction conditions in electrocatalytic reactions to obtain an accurate picture of factors that determine the electrode activity.

Properties of the Pt(111)/electrolyte electrochemical interface studied with a hybrid DFT-solvation approach.

Self-consistent modeling of the interface between solid metal electrode and liquid electrolyte is a crucial challenge in computational electrochemistry. In this contribution, we adopt the effective screening medium reference interaction site method (ESM-RISM) to study the charged interface between a Pt(111) surface that is partially covered with chemisorbed oxygen and an aqueous acidic electrolyte. This method proves to be well suited to describe the chemisorption and charging state of the interface at controlled electrode potential. We present an in-depth assessment of the ESM-RISM parameterization and of the importance of computing near-surface water molecules explicitly at the quantum mechanical level. We found that ESM-RISM is able to reproduce some key interface properties, including the peculiar, non-monotonic charging relation of the Pt(111)/electrolyte interface. The comparison with independent theoretical models and explicit simulations of the interface reveals strengths and limitations of ESM-RISM for modeling electrochemical interfaces.

Grand-Canonical Model of Electrochemical Double Layers from a Hybrid Density-Potential Functional.

A hybrid density-potential functional of an electrochemical interface that encompasses major effects in the contacting metal and electrolyte phases is formulated. Variational analysis of this functional yields a grand-canonical model of the electrochemical double layer (EDL). Specifically, metal electrons are described using the Thomas-Fermi-Dirac-Wigner theory of an inhomogeneous electron gas. The electrolyte solution is treated classically at the mean-field level, taking into account electrostatic interactions, ion size effects, and nonlinear solvent polarization. The model uses parametrizable force relations to describe the short-range forces between metal cationic cores, metal electrons, and electrolyte ions and solvent molecules. Therefore, the gap between the metal skeleton and the electrolyte solution, key to properties of the EDL, varies consistently as a function of the electrode potential. Partial charge transfer in the presence of ion specific adsorption is described using an Anderson-Newns type theory. This model is parametrized with density functional theory calculations, compared with experimental data, and then employed to unravel several interfacial properties of fundamental significance in electrochemistry. In particular, a closer approach of the solution phase toward the metal surface, for example, caused by a stronger ion specific adsorption, decreases the potential of zero charge and elevates the double-layer capacitance curve. In addition, the ion specific adsorption can lead to surface depolarization of ions. The present model represents a viable framework to model (reactive) EDLs under the constant potential condition, which can be used to understand multifaceted EDL effects in electrocatalysis.

Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells.

The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, particularly the catalyst layers. The structural building blocks of catalyst layers are formed during the processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and data augmentation that works on multiple scales in training robust and accurate classification pipelines.

Three-Dimensional Cathodes for Electrochemical Reduction of CO2: From Macro- to Nano-Engineering.

Rising anthropogenic CO2 emissions and their climate warming effects have triggered a global response in research and development to reduce the emissions of this harmful greenhouse gas. The use of CO2 as a feedstock for the production of value-added fuels and chemicals is a promising pathway for development of renewable energy storage and reduction of carbon emissions. Electrochemical CO2 conversion offers a promising route for value-added products. Considerable challenges still remain, limiting this technology for industrial deployment. This work reviews the latest developments in experimental and modeling studies of three-dimensional cathodes towards high-performance electrochemical reduction of CO2. The fabrication-microstructure-performance relationships of electrodes are examined from the macro- to nanoscale. Furthermore, future challenges, perspectives and recommendations for high-performance cathodes are also presented.

Fracture dynamics of correlated percolation on ionomer networks.

This article presents a random network model to the study fracture dynamics on a scaffold of charged and elastic ionomer bundles that constitute the stable skeleton of a polymer electrolyte membrane. The swelling pressure upon water uptake by this system creates the internal stress under which ionomer bundles undergo breakage. Depending on the local stress and the strength of bundle-to-bundle correlations, different fracture regimes can be observed. We use kinetic Monte Carlo simulations to study these dynamics. The breakage of individual bundles is described with an exponential breakdown rule and the stress transfer from failed to intact bundles is assumed to exhibit a power-law-type spatial decay. A central property considered in the analysis is the frequency distribution of percolation thresholds, which is employed to analyze fracture regimes as a function of the stress and the effective range of stress transfer. Based on this distribution, we introduce an order parameter for the transition from random breakage to crack growth regimes. Moreover, as a practically important outcome, the time to fracture is analyzed as a descriptor for the lifetime of polymer electrolyte membranes.

Asymmetric double-layer charging in a cylindrical nanopore under closed confinement.

This article presents a physical-mathematical treatment and numerical simulations of electric double layer charging in a closed, finite, and cylindrical nanopore of circular cross section, embedded in a polymeric host with charged walls and sealed at both ends by metal electrodes under an external voltage bias. Modified Poisson-Nernst-Planck equations were used to account for finite ion sizes, subject to an electroneutrality condition. The time evolution of the formation and relaxation of the double layers was explored. Moreover, equilibrium ion distributions and differential capacitance curves were investigated as functions of the pore surface charge density, electrolyte concentration, ion sizes, and pore size. Asymmetric properties of the differential capacitance curves reveal that the structure of the double layer near each electrode is controlled by the charge concentration along the pore surface and by charge asymmetry in the electrolyte. These results carry implications for accurately simulating cylindrical capacitors and electroactuators.

Electronic Structure and Conformational Properties of Polybenzimidazole-Based Ionenes-A Density Functional Theory Investigation.

Polybenzimidazole-based ionenes are explored for use in both alkaline anion-exchange membrane fuel cells and alkaline polymer electrolyzers. Poly-(hexamethyl-p-terphenylbenzimidazolium) (HMT-PMBI), the material of interest in this article, is exceptionally hydroxide-stable and water-insoluble. The impact of the degree of methylation on conformations and electronic structure properties of HMT-PMBI oligomers, from the monomer to the pentamer, is studied with density functional theory calculations. Optimization studies are presented for both the gas phase and in the presence of implicit water. In addition, time-dependent density functional theory is employed to generate the UV-vis absorption spectra of the studied systems. Results are insightful for experimentalists and theorists investigating the impact of synthetic and environmental conditions on the conformation and electronic properties of polybenzimidazole-based membranes.