Synthesis of high-entropy materials

Nature Synthesis, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Язык: Английский

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High-entropy alloy catalysts: high-throughput and machine learning-driven design

Journal of Materials Informatics, Год журнала: 2022, Номер 2(4), С. 19 - 19

Опубликована: Янв. 1, 2022

High-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising catalytic performance. Most current studies focus on designing HEA through trial-and-error methods. This produces scattered data and is not conducive obtaining a fundamental understanding of the structure-property-performance relationships for catalysts, thereby hindering rational design. High-throughput (HT) techniques machine learning (ML) methods show significant potential in generating, processing analyzing databases with vast amount data, providing new strategy further development catalysts. In this review, we summarize recent literature HT synthesis, characterization performance testing. We also review ML models that are used process analyze existing accelerate discovery Finally, challenges perspectives presented promote development.

Язык: Английский

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A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

The Journal of Physical Chemistry C, Год журнала: 2023, Номер 127(2), С. 871 - 881

Опубликована: Янв. 5, 2023

Machine learning (ML) with its indigenous predicting ability has been influential in the current scientific world and enabled a paradigm shift field of CO2 reduction reaction (CO2RR). In this perspective, research progress ML approaches heterogeneous electrocatalytic CO2RR demonstrated. The important findings related to systems comprising features, output descriptors, models have summarized. Further, opportunities challenges using state-of-the-art methodologies along ways circumventing those are discussed. Finally, interpretation black box extensive usages interpretable glass gray for encouraged obtaining proper physical interpretations. future directions on utilizing several such evolving methods predict catalytic activity descriptors can help broader way explore novel efficient other similar reactions.

Язык: Английский

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Electrocatalytic CO2 reduction to C2H4: From lab to fab

Journal of Energy Chemistry, Год журнала: 2023, Номер 90, С. 540 - 564

Опубликована: Ноя. 26, 2023

The global concerns of energy crisis and climate change, primarily caused by carbon dioxide (CO2), are utmost importance. Recently, the electrocatalytic CO2 reduction reaction (CO2RR) to high value-added multi-carbon (C2+) products driven renewable electricity has emerged as a highly promising solution alleviate shortages achieve neutrality. Among these C2+ products, ethylene (C2H4) holds particular importance in petrochemical industry. Accordingly, this review aims establish connection between fundamentals (CO2RR-to-C2H4) laboratory-scale research (lab) its potential applications industrial-level fabrication (fab). begins summarizing fundamental aspects, including design strategies high-performance Cu-based electrocatalysts advanced electrolyzer devices. Subsequently, innovative techniques presented address inherent challenges encountered during implementations CO2RR-to-C2H4 industrial scenarios. Additionally, case studies techno-economic analysis process discussed, taking into factors such cost-effectiveness, scalability, market potential. concludes outlining perspectives associated with scaling up process. insights expected make valuable contribution advancing from lab fab.

Язык: Английский

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A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO2

Applied Thermal Engineering, Год журнала: 2024, Номер 251, С. 123517 - 123517

Опубликована: Май 31, 2024

Язык: Английский

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