Computational Electrochemistry Research Articles

Computational electrochemistry of oxygen 150 years after Priestley.

Computational electrochemistry of oxygen 150 years after Priestley.

Study of the interaction between caffeine and graphenic materials employing computational analysis and electrochemistry

Study of the interaction between caffeine and graphenic materials employing computational analysis and electrochemistry

Converging Divergent Paths: Constant Charge vs Constant Potential Energetics in Computational Electrochemistry

Converging Divergent Paths: Constant Charge vs Constant Potential Energetics in Computational Electrochemistry

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.

Advances in anode configurations for a microbial fuel cell via a computational fluid dynamics electrochemistry and its experimental validation

AbstractBACKGROUNDThe experimental process for optimizing microbial fuel cell (MFC) design under different electrode geometries is restricted. An MFC can be defined as a bio‐electrochemical system (BES) that facilitates the direct conversion of the chemical energy stored in organic matter into electrical energy by harnessing the metabolic activity of microorganisms. Computational fluid dynamics (CFD) tools allow the simulation and evaluation of electro‐analysis phenomena such as cyclic voltammetry and chemical reactions, which can help the optimization of the BESs. In this study, MFC is designed to provide the maximum peak current and efficiency for the applied voltage on various working (or anode) electrode geometries (i.e. hexagonal, square, pentagonal, circular, triangular, rectangular, and rhombus).RESULTSThe CFD simulation results demonstrate that a configuration with a larger perimeter value and surface area (i.e. hexagonal design) of the working electrode shows a higher peak current (2422.75 mA) than other configurations. The experimental findings supported the simulation result and reveal that a hexagonal electrode containing a MFC setup produces a maximum power density of 22.41 ± 0.32 mW m−3 and a current density of 41.58 ± 0.35 mA m−2. The MFC operation demonstrated adequate bioelectricity generation of 540 ± 03 mV on Day 4 of operation at 1000 Ω. Additionally, maximum reduction in chemical oxygen demand (76.13 ± 0.5%) and coulombic efficiency (76.03 ± 0.4%) was achieved for synthetic wastewater using a hexagonal MFC.CONCLUSIONBased on these fundamental discoveries, the CFD simulation and its experimental validation considerably drag focus toward the possibility of employing COMSOL Multiphysics software for system improvement of different MFC system applications. © 2023 Society of Chemical Industry (SCI).

Solvent effects determine the signof the charges of maximum entropy and capacitance at silver electrodes.

Fully harnessing electrochemical interfaces for reactions requires a detailed understanding of solvent effects in the electrochemical double layer. Predicting the significant impact of solvents on entropic and electronic properties of electrochemical interfaces has remained an open challenge of computational electrochemistry. Using molecular dynamics simulations of silver-water and silver-acetonitrile interfaces, we show that switching the solvent changes the signs for both the charge of maximum capacitance (CMC) and charge of maximum entropy (CME). Contrasting the capacitance and CME behavior of these two interfaces, we demonstrate that the preferred orientation of the solvent molecule and the corresponding charge density determine the signof the CMC and CME and, hence, the qualitatively different charge asymmetry of the electrochemical interface.

Redox Potentials with COSMO-RS: Systematic Benchmarking with Different Databases.

Recent techniques of computational electrochemistry can yield redox potentials with accuracy as good as 0.1 V. Yet, many such methods are not universal, easy to use, or computationally efficient. Herein, we provide a systematic benchmarking of a relatively cheap and straightforward computational approach for fairly accurate computations of redox potentials. It is based on a combination of the conductor-like screening model for real solvents (COSMO-RS) and the density functional theory (DFT). The benchmarking is done with databases covering diverse redox systems, including transition-metal aquacomplexes and various organic and inorganic compounds. In addition, we also present our own test set aiming at maximum chemical diversity and maximum range of redox potential values. We assess the performance of the fairly efficient computational protocol combining the COSMO-RS with the BP86 DFT functional. This is done by calibrating it against different high-level state-of-the-art techniques, in particular, polarizable continuum model (PCM) connected to composite G3(MP2,CC)(+) method, domain-based pair natural orbital implementation of coupled cluster theory, or complete basis set CBS-QB3 method. We also elaborate on the absolute reduction potential value of standard hydrogen electrode to be used with COSMO-RS, and we propose the value of approx. 4.4 V. The COSMO-RS/BP86-D3(BJ) combination outperforms other methods on a wide range of redox systems. However, we show that its accuracy is based on a balanced error cancelation and, therefore, it cannot be further systematically improved. As a result, the proposed procedure represents a pragmatic choice for large-scale screening, yet it could be combined with more advanced methods for detailed studies.

Axial Coordination Effect on the Oxygen Reduction Reaction of FeN4 Electrocatalysts Based on Grand Canonical Density Functional Theory

The Fe–N–C materials are promising noble-free alternatives to Pt-based oxygen reduction reaction (ORR) electrocatalysts. Identifying the actual active structure of FeN4-based moieties is helpful for the regulation and design of high-performance electrocatalysts. However, the current theoretical researches were mostly based on the charge-neutral model (CNM), which cannot accurately describe the electrochemical interface. Herein, we employed the constant potential based grand canonical density functional theory (GC-DFT) computations to study the ORR mechanism of axially decorated FeN4 electrocatalysts. We studied around ten types of axial ligands, and the constant potential energetics and microkinetic modeling demonstrated that the Fe center can exhibit excellent activity for boosting the four-electron ORR via covalently linked −NH2 ligand. The lowering of the antibonding dz2–pz orbital is responsible for weakening the adsorption of oxygen in FeN4–Ls to promote ORR activity, and the −NH2 decoration led to the lowest antibonding orbital energy. In particular, the adsorption free energy (ΔG*O2) and O–O bond length (LO–O) of the adsorbed O2 reactant can be used as the effective energetic and geometric descriptors to describe the ORR activity of FeN4–Ls electrocatalysts. Our results not only elucidate the axial coordination effect on the ORR performance of FeN4 SACs but also demonstrate the importance of electrode potential in computational electrochemistry.

Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology

AbstractElectrochemical processes underlie the functioning of electrochemical devices for energy storage and conversion. In this paper, electrochemoinformatics is defined as a scientific discipline, a part of computational electrochemistry, dealing with the application of information technologies, specifically data science, machine learning (ML), and artificial intelligence, to solve problems related to electrochemical processes, including the development of materials and electrochemical devices. Electrochemoinformatics is closely related to chemoinformatics and materials informatics and relies on the technologies they use. The definitions and the basics of chemoinformatics and materials informatics are reviewed with an emphasis on the role of data science, data representation, the use of ML methods, and molecular and material descriptors. The third constituent part of electrochemoinformatics is full‐cell informatics, which deals with the performance and design of whole electrochemical devices. The article focuses on the use of electrochemoinformatics in battery science and technology (battery informatics), including the development of electrolyte and electrode materials, battery performance monitoring, modeling, and design. The scope and the main challenges facing electrochemoinformatics are discussed.

Computational electrochemistry focusing on nanostructured catalysts: challenges and opportunities

Computational approaches to describe catalysts under electrochemical conditions are steadily increasing. Yet, particularly the theoretical description of nanostructured catalysts, which have the advantage of a high surface area or unique electronic properties through refined synthetic protocols, is still hampered by the occurrence of pitfalls that need to be circumvented. In this perspective, we aim to introduce the reader to common pitfalls in the modeling of nanostructured catalysts with applications in energy conversion and storage, and we discuss the application of machine learning techniques as a potential solution to overcome the associated gaps.

Dimerization of [FeIII(bpy)3]3+ in Aqueous Solutions: Elucidating a Mechanism Based on Historical Proposals, Electrochemical Data, and Computational Free Energy Analysis.

Iron(II) tris-bipyridine, [FeII(bpy)3]2+, is a historically significant organometallic coordination complex with attractive redox and photophysical properties. With respect to energy storage, it is a low-cost, high-redox potential complex and thus attractive for use as a catholyte in aqueous redox flow batteries. Despite these favorable characteristics, its oxidized Fe(III) form undergoes dimerization to form μ-O-[FeIII(bpy)2(H2O)]24+, leading to a dramatic ∼0.7 V decrease during battery discharge. To date, the energetics and complete mechanism of this slow, sequential electrochemical-chemical (EC) process, which includes electron transfer, nucleophilic attack, ligand cleavage, μ-oxo bond formation, and spin state transition, have not been elucidated. Using cyclic voltammetry, redox flow battery data, and density functional theory calculations guided by previously proposed mechanisms, we modeled more than 100 complexes and performed more than 50 geometry scans to resolve the key steps dictating these complex chemical processes. Quantitative free energy surfaces are developed to model the mechanism of dimerization accounting for the spins and identities of any possible Fe(II), Fe(III), or Fe(IV) intermediates. Electrochemical reduction of the dimer regenerates [FeII(bpy)3]2+ in an overall reversible process. Computational electrochemistry interrogates the influence of spin state, coordination environment, and molecular conformation at the electrode-electrolyte interface through a proposed stepwise dimer reduction process. Experimentally, we show that the considerable overpotential associated with this event can be catalytically mitigated with disparate materials, including platinum, copper hexacyanoferrate, and activated carbon. The findings are of fundamental and applied significance and could elevate [FeII(bpy)3]2+ and its derivatives to play a vital role in the burgeoning renewable energy economy.

DFT calculations and machine learning approach to predict catalytic properties of nanoscale electrocatalysts in solution for clean fuel generation

<h2>Abstract</h2> A nanocatalyst is at the central position as a promoter of various chemical. Innovative design of highly functional catalyst materials has been, however, delayed. In molecular level computational electrochemistry new research paradigm has been established, which substantially incorporates IT-based artificial intelligence (AI) technology into machine learning algorism. Using the new computational methodology high-throughput screening of promising nanoparticle candidates has been attempted for various desired applications. Whether the frontier approach is successful or not is significantly controlled by the reliability and accuracy of input database. It is true that substantial amounts of the data are come by previous literatures and often ab-initio calculations with idealized model systems. The conditions in which the data were generated may be so different from the operando circumstances of the target materials. To secure extreme-level integrity of the database the in-situ measurement of nanoparticle structures should be carried out, from which the reliable correlation of the structure-performance-design principle can be identified. Using first-principles calculations we studied nanoparticles with adsorbate ligands in liquid solution to establish three-dimensional (3D) structure and property database, which are, then, analyzed through AI-based neural-network approach with high speed and accuracy. The information includes sizes, lattice distortions, and defects with picometer resolution under non-vacuum conditions. The computational outcomes are rigorously validated from the 3D liquid-cell electron microscopy. The approach is indeed ‘knowledge-based' AI, which can be expected to make groundbreaking ways toward the quantum nanoarchitecture for hybrid interface materials.

Influence of Ionic Coordination on the Cathode Reaction Mechanisms of Al/S Batteries

The lack of knowledge on electrochemical reaction pathways for Al/S batteries prevents the development of practical approaches to mitigate the irreversibility and poor cycling performances of this appealing secondary battery system, which is, in theory, scalable, inexpensive, and energy-dense. Different from the Li/S system, Al/S batteries use ionic liquids (ILs) as electrolytes. The choice of the IL, i.e., whether the IL is based on a conventional EMImCl-based electrolyte or in a deep eutectic mixture of aluminum chloride with urea (or any of its derivatives), strongly affects the electrochemical energy-storage performance of the cell. To shed some light on the Al/S battery chemistry, here, we present the computational electrochemistry research work to determine the most favorable reaction pathways and thermodynamically stable reaction intermediates. We also discuss the effect of the coordination of ionic species (originated from aluminum-containing deep eutectic electrolytes) with polysulfide intermediates, which lead to alterations in the reaction pathway and electrochemical behavior of the Al/S system. The spectroscopic signatures from various reaction intermediates are also reported and validated via comparison with experimental observations.

Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning.

Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCEs) have attracted great interest due to their high electrochemical performances and stability. Previous studies have revealed changes in solvation structures and shifts in lowest unoccupied molecular orbitals from solvents to anions, promoting the formation of an anion-derived solid-electrolyte-interphase (SEI) in SCE. However, a direct connection at the atomic level to electrochemical properties is still missing, hindering the rational optimization of electrolytes. Herein, we combine ab initio molecular dynamics with the free energy calculation method to compute redox potentials of propylene carbonate electrolytes at a range of LiTFSI concentrations, and moreover employ an unsupervised machine learning model with a local structure descriptor to establish the structure-property relations. Our calculation indicates that the network of TFSI- in SCE not only helps stabilize the added electron and renders the anion more prone to reductive decomposition, but also impedes the solvation of F- and favors LiF precipitation, together leading to effective formation of protective SEI layers. Our work provides new insights into the solvation structures and electrochemistry of concentrated electrolytes which are essential to electrolyte design in batteries.

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.

Recent progress on multiscale modeling of electrochemistry

AbstractComputational electrochemistry, an important branch of electrochemistry, has shown its advantages in studying electrode/electrolyte interfaces, such as the structures of electric double layers. However, modeling electrochemical systems is still a challenge, especially in interface electrochemistry, because not only solvation effects and ion distribution in electrolyte solutions should be considered, but also the treatment of the electrode potential and the response of electrolytes to applied potentials. Here, we review the latest development in the field of computational electrochemistry. We first introduce various energy models used in simulating electrolytes and electrodes at multiple scales. Then, to better explain and compare between different methods, we discuss the calculation methods of solution electrochemistry and interface electrochemistry in separate. At last, we introduce the methods to electrify the interfaces in various multiscale models. This review aims to help understand various levels of methods in simulations of different scenarios in electrochemistry, and summarizes a set of schemes covering multiple scales.This article is categorized under: Electronic Structure Theory &gt; Combined QM/MM Methods Molecular and Statistical Mechanics &gt; Molecular Dynamics and Monte‐Carlo Methods Electronic Structure Theory &gt; Density Functional Theory

Correction to “Computational Electrochemistry as a Reliable Probe of Experimentally Elusive Mononuclear Non-Heme Iron Species”

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Computational Electrochemistry as a Reliable Probe of Experimentally Elusive Mononuclear Non-Heme Iron Species”Daniel BímDaniel BímMore by Daniel Bím, Lubomír RulíšekLubomír RulíšekMore by Lubomír Rulíšekhttp://orcid.org/0000-0002-7769-7059, and Martin Srnec*Martin SrnecMore by Martin Srnechttp://orcid.org/0000-0001-5118-141XCite this: J. Phys. Chem. C 2021, 125, 1, 1169Publication Date (Web):December 23, 2020Publication History Published online23 December 2020Published inissue 14 January 2021https://doi.org/10.1021/acs.jpcc.0c11167Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views424Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (523 KB) Get e-Alerts Get e-Alerts

Tuning the Performance of Aqueous Organic Redox Flow Batteries via First-Principles Calculations.

Aqueous organic redox flow batteries have many appealing properties in the application of large-scale energy storage. The large chemical tunability of organic electrolytes shows great potential to improve the performance of flow batteries. Computational studies at the quantum-mechanics level are very useful for guiding experiments, but in previous studies, explicit water interactions and thermodynamic effects were ignored. Here, we applied the computational electrochemistry method based on ab initio molecular dynamics and thermodynamic integration to calculate redox potentials of quinones and their derivatives. The calculated results are in excellent agreement with experimental data. We mixed side chains to tune their reduction potentials and found that solvation interactions and entropy effects play a significant role in side-chain engineering. On the basis of our calculations, we proposed several high-performance negative and positive electrolytes. Our first-principles study paves the way toward the development of large-scale and sustainable electrical energy storage.

Molecular modeling of organic redox‐active battery materials

AbstractOrganic redox‐active battery materials are an emerging alternative to their inorganic counterparts currently used in the commercialized battery technologies. The main advantages of organic batteries are the potential for low‐cost manufacturing, tunability of electrochemical properties through molecular engineering, and their environmental sustainability. The search for organic electroactive materials that could be used for energy storage in mobile and stationary applications is an active area of research. Computer simulations are used extensively to improve the understanding of the fundamental processes in the existing materials and to accelerate the discovery of new materials with improved performance. We will focus on two main types of redox‐active organic battery materials, that is, solid‐state organic electrode materials and organic electrolytes for redox flow batteries. Because organic materials are made of molecular building blocks, the molecular modeling methodology is usually the most appropriate to investigate their properties at the electronic and atomistic scales. After introducing the fundamentals of computational organic electrochemistry, we will survey its most recent applications in organic battery research and outline some of the remaining challenges for the development and applications of atomic‐scale modeling techniques in the organic battery context.This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Software &gt; Molecular Modeling Electronic Structure Theory &gt; Density Functional Theory

Identifying Iron-Nitrogen/Carbon Active Structures for Oxygen Reduction Reaction under the Effect of Electrode Potential.

Transition-metal-nitrogen/carbon (TM-N/C) materials are promising alternatives to Pt-based oxygen reduction reaction (ORR) electrocatalysts of fuel cells. Identifying the highly active sites is the prerequisite for the design of high-performance electrocatalysts, in which the density functional theory (DFT) calculation is an important tool. However, the DFT simulation was usually conducted with a charge-neutral model, which is far away from the working condition, that is, under certain potentials. Herein, by using the DFT method with the explicit consideration of electrode potential, we systematically compared the activities of the Fe-N/C moieties previously proposed in the literature and identified the best one. This study not only demonstrates the significance of the electrode potential in computational electrochemistry but also suggests a feasible experimental strategy to increase the ORR performance of Fe-N/C electrocatalysts by creating edges defects and coordinating with the axial ligands on the Fe center, which is of practical significance for exploring the advanced non-precious-metal-based ORR electrocatalysts and related devices.