Modeling Operando Electrochemical CO₂ Reduction

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(12), P. 11085 - 11130

Published: April 27, 2022

Since the seminal works on application of density functional theory and computational hydrogen electrode to electrochemical CO2 reduction (eCO2R) evolution (HER), modeling both reactions has quickly evolved for last two decades. Formulation thermodynamic kinetic linear scaling relationships key intermediates crystalline materials have led definition activity volcano plots, overpotential diagrams, full exploitation these theoretical outcomes at laboratory scale. However, recent studies hint role morphological changes short-lived in ruling catalytic performance under operating conditions, further raising bar electrocatalytic systems. Here, we highlight some novel methodological approaches employed address eCO2R HER reactions. Moving from atomic scale bulk electrolyte, first show how ab initio machine learning methodologies can partially reproduce surface reconstruction operation, thus identifying active sites reaction mechanisms if coupled with microkinetic modeling. Later, introduce potential interpret data Operando spectroelectrochemical techniques, such as Raman spectroscopy extended X-ray absorption fine structure characterization. Next, review electrolyte mass transport effects. Finally, suggest challenges near future well our perspective directions follow.

Language: Английский

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Rare‐Earth Single‐Atom Catalysts: A New Frontier in Photo/Electrocatalysis

Small Methods, Journal Year: 2022, Volume and Issue: 6(8)

Published: June 25, 2022

Abstract Single‐atom catalysts (SACs) provide well‐defined active sites with 100% atom utilization, and can be prepared using a wide range of support materials. Therefore, they are attracting global attention, especially in the fields energy conversion storage. To date, research has focused on transition‐metal precious‐metal‐based SACs. More recently, rare‐earth (RE)‐based SACs have emerged as new frontier photo/electrocatalysis owing to their unique electronic structure arising from spin‐orbit coupling 4f valence orbitals, unsaturated coordination environment, behavior charge‐transport bridges. However, systematic review role RE sites, catalytic mechanisms, synthetic methods for is lacking. this review, latest developments having applications summarized discussed. First, theoretical advantages briefly introduced, focusing roles orbitals coupled levels. In addition, most recent progress several important photo/electrocatalytic reactions corresponding mechanisms Further, strategies production reported. Finally, challenges development highlighted, along future directions perspectives.

Language: Английский

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A Rising 2D Star: Novel MBenes with Excellent Performance in Energy Conversion and Storage

Nano-Micro Letters, Journal Year: 2022, Volume and Issue: 15(1)

Published: Dec. 6, 2022

As a flourishing member of the two-dimensional (2D) nanomaterial family, MXenes have shown great potential in various research areas. In recent years, continued growth interest MXene derivatives, 2D transition metal borides (MBenes), has contributed to emergence this material as latecomer. Due excellent electrical conductivity, mechanical properties and properties, thus MBenes attract more researchers' interest. Extensive experimental theoretical studies that they exciting energy conversion electrochemical storage potential. However, comprehensive systematic review applications not been available so far. For reason, we present summary advances research. We started by summarizing latest fabrication routes MBenes. The focus will then turn their for conversion. Finally, brief challenges opportunities future practical is presented.

Language: Английский

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Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Advanced Energy Materials, Journal Year: 2022, Volume and Issue: 12(20)

Published: March 29, 2022

Abstract The solar‐energy‐driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform into valuable energy sources under mild conditions. As clean‐burning fuel and drop‐in replacement for natural gas, CH 4 is an ideal product photoreduction, but the development highly active selective semiconductor‐based photocatalysts this important transformation remains challenging. Hence, significant efforts have been made in search active, selective, stable, sustainable photocatalysts. In review, recent applications cutting‐edge experimental computational materials design strategies toward discovery novel catalysts photocatalytic conversion are systematically summarized. First, insights effective catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet single atoms, presented. Then, data‐driven photocatalyst spanning density functional theory (DFT) simulations, high‐throughput screening, machine learning (ML) presented through step‐by‐step introduction. combination DFT, ML, experiments emphasized powerful solution accelerating reduction . Last, challenges perspectives concerning interplay between rational industrialization large‐scale technologies described.

Language: Английский

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Atomically Dispersed Metal‐Based Catalysts for Zn–CO₂Batteries

Small Structures, Journal Year: 2022, Volume and Issue: 3(12)

Published: Aug. 19, 2022

Rechargeable aqueous Zn–CO 2 batteries show great promise in meeting severe environmental problems and energy crises due to their combination of CO utilization output, as well advantages high theoretical density, abundant raw materials, safety. Developing high‐efficiency stable reduction reaction (CO RR) electrocatalysts is critical importance for the promotion this technology. Atomically dispersed metal‐based catalysts (ADMCs), with extremely atom‐utilization efficiency, tunable coordination environments, superior intrinsic catalytic activity, are emerging promising candidates batteries. Herein, some recent developments atomically summarized, including transition metal non‐transition sites. Moreover, various synthetic strategies, characterization methods, relationship between active site structures RR activity/Zn–CO battery performance introduced. Finally, challenges perspectives also proposed future development ADMCs

Language: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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Machine Learning: A New Paradigm in Computational Electrocatalysis

The Journal of Physical Chemistry Letters, Journal Year: 2022, Volume and Issue: 13(34), P. 7920 - 7930

Published: Aug. 18, 2022

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, uncovering scientific insights lie the center of development electrocatalysis. Despite certain success in experiments computations, it is still difficult to achieve above objectives due complexity systems vastness chemical space for candidate electrocatalysts. With advantage machine learning (ML) increasing interest electrocatalysis energy conversion storage, data-driven research motivated by artificial intelligence (AI) has provided new opportunities discover promising investigate dynamic reaction processes, extract knowledge from huge data. In this Perspective, we summarize recent applications ML electrocatalysis, including electrocatalysts simulation processes. Furthermore, interpretable methods are discussed accelerate generation. Finally, blueprint envisaged future

Language: Английский

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Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO₂ Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(14), P. 9616 - 9628

Published: July 7, 2023

Electrocatalytic CO2 reduction reactions (CO2RR) based on scalable and highly efficient catalysis provide an attractive strategy for reducing emissions. In this work, we combined first-principles density functional theory (DFT) machine learning (ML) to comprehensively explore the potential of double-atom catalysts (DACs) featuring inverse sandwich structure anchored defective graphene (gra) catalyze CO2RR generate C1 products. We started with five homonuclear M2⊥gra (M = Co, Ni, Rh, Ir, Pt), followed by 127 heteronuclear MM′⊥gra Pt, M′ Sc–Au). Stable DACs were screened evaluating their binding energy, formation dissolution metal atoms, as well conducting molecular dynamics simulations without solvent water molecules. Based DFT calculations, Rh2⊥gra DAC was found outperform other four Rh-based single- noninverse structures. Out DACs, 14 be stable have good catalytic performance. An ML approach adopted correlate key factors activity stability including sum radii ligand atoms (dM–M′, dM–C, dM′–C), difference electronegativity two (PM + PM′, PM – PM′), first ionization energy (IM IM′, IM IM′), electron affinity (AM AM′, AM AM′), number d-electrons (Nd). The obtained models further used predict 154 electrocatalysts out 784 possible same configuration. Overall, work not only identified promising reported but also provided insights into atomic characteristics associated high activity.

Language: Английский

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Machine Learning Descriptors for Data‐Driven Catalysis Study

Advanced Science, Journal Year: 2023, Volume and Issue: 10(22)

Published: May 16, 2023

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful predictive abilities. The selection of appropriate input features (descriptors) plays decisive role in improving the accuracy ML models uncovering key factors that influence activity selectivity. This review introduces tactics utilization extraction descriptors ML-assisted experimental research. In addition effectiveness advantages various descriptors, their limitations are also discussed. Highlighted both 1) newly developed spectral performance prediction 2) novel paradigm combining computational through suitable intermediate descriptors. Current challenges future perspectives on application techniques presented.

Language: Английский

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Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 917, P. 170085 - 170085

Published: Jan. 15, 2024

Language: Английский

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