Approaching the Self-Consistency Challenge of Electrocatalysis with Theory and Computation

This opinion piece centers around challenges involved in developing first- principles electrochemical methods. In recent years, theory and computation have become quintessential tools to navigate the parameter space that controls the activity and stability of electrocatalytic materials and electrochemical devices. Viable methods process as input details on materials structure, composition and reaction conditions. Their output includes metrics for stability and activity, phase diagrams, as well as mechanistic insights on reaction mechanisms and pathways. The core challenge, connecting input to output, is a self-consistency problem that couples the electrode potential to variables for the electronic structure of the solid electrode, solvent properties and ion distributions in the electrolyte as well as specific properties of a boundary region in between. We will discuss a theoretical framework and computational approaches that strive to accomplish this feat

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data and allows for the deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data need to be managed, preserved and shared. The heterogeneity of such data calls for highly flexible models to represent the data from fabrication workflows, measurements and simulations. We propose the use of a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we create an ontology that serves as the blueprint of the data model. The Python framework Django is used to enable seamless integration into the virtual materials intelligence platform VIMI. The Django framework relies on the Object Graph Mapper neomodel to create a mapping between database classes and Python objects. The model can store the whole bandwidth of the data from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research

OER Performances of Cationic Substituted (100)-Oriented IrO 2 Thin Films: A Joint Experimental and Theoretical Study

Cationic substitution was investigated as a strategy to increase the electrocatalytic activity of IrO2-based films for the oxygen evolution reaction (OER). For this purpose, an approach that combines detailed experimental characterization with quantum mechanical calculations based on density functional theory was employed. A series of (100)-oriented Ir1–xMxO2 thin films, with M = Ni, Cr, Mo, W, Sn, Pt, Rh, Ru, V, and Mn, was prepared with a one-step synthesis approach based on pulsed laser deposition, and the electrocatalytic activity of these films for the OER was measured. Matching material compositions and structures were generated in silico for DFT-based calculations of their electronic structure and OER pathway. A comparison of the experimental and theoretical results revealed the viable activity descriptor, paving the way for a systematic search to find the most active Ir-based OER catalyst

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

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-, rod-shaped to amorphous nanoparticles. Finally, the trained models were deployed in a cloud-based analytics platform for the autonomous particle analysis of microscopy images

Deprotonation and Cation Adsorption on the NiOOH/Water Interface: A Grand-Canonical First-Principles Investigation

Nickel-based oxides are highly active, cost-effective materials for the oxygen evolution reaction in alkaline conditions. Recent experimental studies have revealed the importance of surface deprotonation and alkali metal cation adsorption on the activity of Ni oxide surfaces, in contact with aqueous alkaline electrolyte. As a first step to elucidate the role of the alkali adsorption for the activity, we performed first-principles electronic structure calculations to address the stable surface structures of -NiOOH(0001) as a function of the operating conditions in an electrochemical environment. We present a grand-canonical approach to compute the surface Pourbaix diagram of the -NiOOH/water interface for the processes of deprotonation and alkali metal cation adsorption. The results of this study emphasize the importance of double-layer effects, including the adsorbate-induced change of surface dipole moments and the rearrangement of water molecules due to their strong interaction with the adsorbed species, for the most stable interface structure

Understanding the Improved Activity of Dendritic Sn1Pb3 Alloy for the CO2 Electrochemical Reduction: A ComputationalExperimental Investigation

An alloy of Sn and Pb (Sn1Pb3) was prepared by electrodeposition at large negative current. The deposit is porous, with a honeycomb-like primary structure and a dendritic-like secondary structure. The onset potential for the electroreduction of CO2 is 80 mV lower on dendritic Sn1Pb3 as compared to dendritic Pb. The faradaic efficiency for formate formation is close to 100% in the potential range from −1.16 to −1.56 V vs. SHE. Density functional theory (DFT) computations were performed to uncover the origin of the decrease in the onset potential upon alloying Pb with Sn. Explicit treatment of water molecules in DFT calculations turns out as crucial to achieve an agreement with experimentally measured onset potentials

Understanding the Improved Activity of Dendritic Sn 1 Pb 3 Alloy for the CO 2 Electrochemical Reduction: A Computational–Experimental Investigation

An alloy of Sn and Pb (Sn1Pb3) was prepared by electrodeposition at large negative current. The deposit is porous, with a honeycomb-like primary structure and a dendritic-like secondary structure. The onset potential for the electroreduction of CO2 is 80 mV lower on dendritic Sn1Pb3 as compared to dendritic Pb. The faradaic efficiency for formate formation is close to 100% in the potential range from −1.16 to −1.56 V vs. SHE. Density functional theory (DFT) computations were performed to uncover the origin of the decrease in the onset potential upon alloying Pb with Sn. Explicit treatment of water molecules in DFT calculations turns out as crucial to achieve an agreement with experimentally measured onset potentials