Strategies for robust electrocatalytic activity of 2D materials: ORR, OER, HER, and CO2RR

Materials Today Advances, Год журнала: 2024, Номер 22, С. 100488 - 100488

Опубликована: Май 8, 2024

Electrocatalysis utilizing 2D materials is an encouraging approach for advancing sustainable energy conversion technologies. This review explores the strategies employed to achieve robust electrocatalytic activity of in key reactions, namely, OER, HER, and CO2RR. The distinct structural electrical characteristics offer opportunities rapid catalytic performance, indicating significant efficiency selectivity. We systematically discuss factors governing two-dimensional materials, including their intrinsic properties, surface modification techniques, heterostructure engineering, role defects. Furthermore, we summarize recent advances experimental theoretical studies understand fundamental mechanisms with respect behavior. For ORR, defect phase interface heteroatom doping techniques have been explored. In addition, case CO2RR, modification, surface-structure tuning, electrolyte electrolyzer optimization were examined. emphasizes prospective as efficient electrocatalysts processes. Moreover, it provides future insights into this rapidly evolving field highlights possible challenges. conclusion, aims serve a remarkable resource researchers seeking harness potential response applications.

Язык: Английский

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How machine learning can accelerate electrocatalysis discovery and optimization

Materials Horizons, Год журнала: 2022, Номер 10(2), С. 393 - 406

Опубликована: Дек. 9, 2022

Advances in machine learning (ML) provide the means to bypass bottlenecks discovery of new electrocatalysts using traditional approaches. In this review, we highlight currently achieved work ML-accelerated and optimization via a tight collaboration between computational models experiments. First, applicability available methods for constructing machine-learned potentials (MLPs), which accurate energies forces atomistic simulations, are discussed. Meanwhile, current challenges MLPs context electrocatalysis highlighted. Then, review recent progress predicting catalytic activities surrogate models, including microkinetic simulations more global proxies thereof. Several typical applications ML rationalize thermodynamic predict adsorption activation also Next, developments ML-assisted experiments catalyst characterization, synthesis reaction condition illustrated. particular, ML-enhanced spectra analysis use interpret experimental kinetic data Additionally, show how robotics applied high-throughput synthesis, characterization testing accelerate materials exploration process equipment can be assembled into self-driven laboratories.

Язык: Английский

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Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

SmartMat, Год журнала: 2022, Номер 3(1), С. 68 - 83

Опубликована: Март 1, 2022

Abstract In the past decades, machine learning (ML) has impacted field of electrocatalysis. Modern researchers have begun to take advantage ML‐based data‐driven techniques overcome computational and experimental limitations accelerate rational catalyst design. Hence, significant efforts been made perform ML calculation aid electrocatalyst design for CO 2 reduction. This review discusses recent applications discover, design, optimize novel electrocatalysts. First, insights into aided in accelerating are presented. Then, is introduced, including establishing a data set/data source selection validation descriptor algorithms predictions model. Finally, opportunities future challenges summarized reduction with assistance ML.

Язык: Английский

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Electrochemical CO2 conversion towards syngas: Recent catalysts and improving strategies for ratio-tunable syngas

Journal of Power Sources, Год журнала: 2022, Номер 535, С. 231453 - 231453

Опубликована: Апрель 21, 2022

Язык: Английский

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Industrial‐Level CO₂Electroreduction Using Solid‐Electrolyte Devices Enabled by High‐Loading Nickel Atomic Site Catalysts

Advanced Energy Materials, Год журнала: 2022, Номер 12(31)

Опубликована: Июнь 29, 2022

Abstract Transition‐metal atomic site catalysts (ASCs) are a new class of catalytic system for CO 2 electroreduction, however, their practical application is greatly hindered by the challenge that it's still difficult them to simultaneously achieve industrial‐level current density and high selectivity. Herein strategy reported hundreds gram‐scale low‐cost production Ni‐ASCs on 3D porous nanocarbon with high‐loading NiN 3 sites boosting electroreduction both It discovered although (Ni‐ASCs/4.3 wt.%) low‐loading (Ni‐ASCs/0.8 show above 95% Faradic efficiency (FE ) under wide potential range in H‐cell, flow cell, Ni‐ASCs/0.8 wt.% can only FE 43.6% at 343.9 mA cm −2 , significantly lower than those (95.1%, 533.3 Ni‐ASCs/4.3 same potential, first revealing important role high‐loadings single atom promoting high‐selectivity electrolysis density. Most importantly, it demonstrated wt.%‐based membrane electrode assembly (MEA) exhibits outstanding durability 360.0 which one best performances realistic ASCs‐based MEA systems.

Язык: Английский

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An interpretable forecasting framework for energy consumption and CO2 emissions

Applied Energy, Год журнала: 2022, Номер 328, С. 120163 - 120163

Опубликована: Окт. 22, 2022

Язык: Английский

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Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids

npj Computational Materials, Год журнала: 2023, Номер 9(1)

Опубликована: Окт. 6, 2023

Язык: Английский

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The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction

eScience, Год журнала: 2023, Номер 3(4), С. 100136 - 100136

Опубликована: Апрель 17, 2023

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation natural resources that have accompanied development human society. The dioxide reduction reaction (CO2RR) a promising strategy capture convert (CO2) into value-added chemical products. However, traditional trial-and-error method makes it expensive time-consuming understand deeper mechanism behind reaction, discover novel catalysts with superior performance lower cost, determine optimal support structures electrolytes for CO2RR. Emerging machine learning (ML) techniques provide opportunity integrate material science artificial intelligence, which would enable chemists extract implicit knowledge data, be guided insights thereby gained, freed from performing repetitive experiments. In this perspective article, we focus on recent advancements in ML-participated CO2RR applications. After brief introduction ML CO2RR, first ML-accelerated property prediction potential catalysts. Then explore ML-aided catalytic activity selectivity. This followed discussion about ML-guided catalyst electrode design. Next, application ML-assisted experimental synthesis discussed. Finally, present specific challenges opportunities, aim better understanding research using

Язык: Английский

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Advances and perspectives on heteronuclear dual-atomic catalysts for prevailing the linear scaling relationship in electrocatalytic CO2 reduction

Coordination Chemistry Reviews, Год журнала: 2023, Номер 501, С. 215589 - 215589

Опубликована: Дек. 5, 2023

Язык: Английский

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Machine Learning Assisted Understanding and Discovery of CO₂ Reduction Reaction Electrocatalyst

The Journal of Physical Chemistry C, Год журнала: 2023, Номер 127(2), С. 882 - 893

Опубликована: Янв. 8, 2023

Electrochemical CO2 reduction reaction (CO2RR) is an important process which a potential way to recycle excessive in the atmosphere. Although electrocatalyst key toward efficient CO2RR, progress of discovering effective catalysts lagging with current methods. Because cost and time efficiency modern machine learning (ML) algorithm, increasing number researchers have applied ML accelerate screening suitable deepen our understanding mechanism. Hence, we reviewed recent applications research CO2RR by types electrocatalyst. An introduction on general methodology discussion pros cons for such are included.

Язык: Английский

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