Promoting Electrocatalytic Oxygen Reactions Using Advanced Heterostructures for Rechargeable Zinc–Air Battery Applications

ACS Nano, Journal Year: 2024, Volume and Issue: 18(33), P. 21651 - 21684

Published: Aug. 12, 2024

In order to facilitate electrochemical oxygen reactions in electrically rechargeable zinc-air batteries (ZABs), there is a need develop innovative approaches for efficient electrocatalysts. Due their reliability, high energy density, material abundance, and ecofriendliness, ZABs hold promise as next-generation storage conversion devices. However, the large-scale application of currently hindered by slow kinetics reduction reaction (ORR) evolution (OER). development heterostructure-based electrocatalysts has potential surpass limitations imposed intrinsic properties single material. This Account begins with an explanation configurations fundamentals electrochemistry air electrode. Then, we summarize recent progress respect variety heterostructures that exploit bifunctional electrocatalytic overview impact on ZAB performance. The range heterointerfacial engineering strategies improving ORR/OER performance includes tailoring surface chemistry, dimensionality catalysts, interfacial charge transfer, mass transport, morphology. We highlight multicomponent design take these features into account create advanced highly active catalysts. Finally, discuss challenges future perspectives this important topic aim enhance activity batteries.

Language: Английский

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Catalysis in the digital age: Unlocking the power of data with machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2024, Volume and Issue: 14(5)

Published: Sept. 1, 2024

Abstract The design and discovery of new improved catalysts are driving forces for accelerating scientific technological innovations in the fields energy conversion, environmental remediation, chemical industry. Recently, use machine learning (ML) combination with experimental and/or theoretical data has emerged as a powerful tool identifying optimal various applications. This review focuses on how ML algorithms can be used computational catalysis materials science to gain deeper understanding relationships between properties their stability, activity, selectivity. development repositories, mining techniques, tools that navigate structural optimization problems highlighted, leading highly efficient sustainable future. Several data‐driven models commonly research diverse applications reaction prediction discussed. key challenges limitations using presented, which arise from catalyst's intrinsic complex nature. Finally, we conclude by summarizing potential future directions area ML‐guided catalyst development. article is categorized under: Structure Mechanism > Reaction Mechanisms Catalysis Data Science Artificial Intelligence/Machine Learning Electronic Theory Density Functional

Language: Английский

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Photochromic spiro-indolium iodide derivatives; synthesis, Molecular geometry DFT assessment and UV-vis photophysical investigation

Journal of Molecular Structure, Journal Year: 2025, Volume and Issue: unknown, P. 141740 - 141740

Published: Feb. 1, 2025

Language: Английский

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Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Materials, Journal Year: 2024, Volume and Issue: 17(9), P. 2119 - 2119

Published: April 30, 2024

Photocatalysis is a fascinating process in which photocatalyst plays pivotal role driving chemical reaction when exposed to light. Its capacity harness light energy triggers cascade of reactions that lead the formation intermediate compounds, culminating desired final product(s). The essence this interaction between photocatalyst’s excited state and its specific interactions with reactants, resulting creation intermediates. process’s appeal further enhanced by cyclic nature—the rejuvenated after each cycle, ensuring ongoing sustainable catalytic action. Nevertheless, comprehending photocatalytic through modeling photoactive materials molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, machine learning. This review analyzes contemporary theoretical spanning range lengths accuracy scales, assesses strengths limitations these methods. It also explores future challenges complex nano-photocatalysts, underscoring necessity integrating various methods hierarchically optimize resource distribution across different scales. Additionally, discussion includes chemistry, crucial element understanding photocatalysis.

Language: Английский

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The accurate assessment of the chemical speciation of complex systems through multi-technique approaches

Pure and Applied Chemistry, Journal Year: 2024, Volume and Issue: 96(4), P. 597 - 623

Published: April 1, 2024

Abstract Chemical speciation studies, i.e., the study of distribution an element or compound among its various species in a system interest, are fundamental importance. investigations can be performed mainly by either direct measurement chemical different analytical techniques, modeling through equilibrium thermodynamic data, based on use stability constants (and other parameters) formed species. For these purposes, series techniques used. As soon as complexity systems interest increases, need for more detailed information arises. such, multi-technique approach is essential to derive complementary data define system. In this tutorial review we analyzed most common instrumental employed studies and analysis. The main advantages disadvantages potentiometry, voltammetry, coulometry, UV–vis spectrophotometry, spectrofluorimetry, NMR, EPR, ITC, HRMS quantum mechanical calculations, together with brief mention less discussed practical examples their application. aim provide guide all scientists interested field.

Language: Английский

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Experiment–Theory Synergy: Connecting the Kinetics of the Molecular Catalysis of Electrochemical Reactions with Calculated Energy Landscapes

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 2504 - 2514

Published: Jan. 27, 2025

While energy profiles from quantum mechanical calculations suggest mechanisms for molecular catalysis of electrochemical reactions, they frequently lack experimental kinetic validation due to limited data or ambiguities linking calculated and observables. Herein, we expand the "energetic span model", traditionally applied in homogeneous systems, molecularly catalyzed reactions focusing on EC1..CnE′-type mechanisms. We thus establish a framework aligning theoretical turnover frequency estimates with practical cyclic voltammetry measurements i.e., extracted rate constants accounting diffusion–reaction layer complexities. The analysis also identifies specific zones, defining conditions under which different catalyst intermediates dominate layer. This approach helps refine energetic model may improve alignment calculation. It is experimentally well-studied reduction CO2 CO using an iron tetraphenylporphyrin phenol as proton donor. Previously explored pathways align partly data, but important discrepancies exist, especially regarding reaction's dependence binding donor concentration. findings highlight challenges predicting behavior underscore significance intermediate energetics reaction modeling. Nonetheless, cross-talk between solid studies should be reasonable path toward reaching mechanistic consensus.

Language: Английский

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Resolution of Selectivity Steps of CO Reduction Reaction on Copper by Quantum Monte Carlo

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 1494 - 1500

Published: Feb. 3, 2025

Electrochemical reduction of carbon monoxide to valuable fuels and chemicals on copper surfaces remains a challenging area in catalysis due limited understanding adsorption mechanisms reaction pathways. Although density functional theory (DFT)-based studies have investigated these processes, their accuracy varies across different functionals. Here, we present the application fixed-node diffusion Monte Carlo (FNDMC) benchmark energies CO*, H*, key CO (CORR) intermediates, COH* CHO* Cu(111) surface. Our results for CO* H* closely align with experimentally measured chemisorption reactions, highlighting limitations DFT providing site-specific energy comparisons that are often not available experimentally. Additionally, explore effect explicit solvation, demonstrating how water stabilizes over CHO*, thus suggesting critical role CORR. Finally, release our high-accuracy FNDMC benchmarks testing developing new functionals electrocatalysis. Overall, this study underscores potential detailed surface chemistry offers insights into catalytic processes.

Language: Английский

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Environmental Life-Cycle Assessment and Green Principles in Process Intensification: A Review of Novel Catalysts from Solid Waste

Chemical Engineering and Processing - Process Intensification, Journal Year: 2025, Volume and Issue: unknown, P. 110208 - 110208

Published: Feb. 1, 2025

Language: Английский

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Interaction of tautomers of doxorubicin with guanine-cytosine base pair: a density functional theory study

Journal of Molecular Modeling, Journal Year: 2025, Volume and Issue: 31(4)

Published: March 3, 2025

Language: Английский

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Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis

Published: Jan. 1, 2025

Language: Английский

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