From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(15), P. 11749 - 11779

Published: July 24, 2024

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field heterogeneous catalysis, presenting a broad spectrum contemporary methodologies innovations. We methodically segmented text three core areas: catalyst characterization, data-driven exploitation, discovery. In characterization part, we outline current prospective techniques used for HTE how AI-driven strategies can streamline or automate their analysis. The exploitation part is divided themes, strategies, that offer flexibility either modular application creation customized solutions. exploration present applications enable areas outside experimentally tested chemical space, incorporating section on computational methods identifying new prospects. concludes by addressing limitations within suggesting possible avenues future research.

Language: Английский

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An atomically economical and environmentally benign approach for the scalable synthesis of rare‐earth metal‐organic framework catalysts

AIChE Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Abstract Metal–organic frameworks (MOFs), as an emerging class of porous materials, demonstrated substantial potential for applications in chemical engineering processes. However, their production often generates significant waste including ionic residues and organic solvents, posing considerable environmental concerns. Herein, we report the environmentally benign MOF synthesis with metal oxides precursors, yielding water exclusive by‐product. This approach not only enhances atom economy but also promotes solvent recycling by preventing anion accumulation, significantly reducing generation. The can be easily scaled up a 10‐L reactor impressive space–time yield 608 kg·m −3 ·day −1 . Furthermore, MOF‐76(Y) was implemented catalytic cycloaddition reaction at kg‐scale. Through optimization interfacial contact between CO 2 liquid phase, 96.7% achieved under mild conditions. work demonstrates rare example scalable sustainable materials coupled catalysis implementation kilogram scale.

Language: Английский

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Emerging MOF-based antibiotic detection methods in water environments: recent advances, challenges, and prospects

Water Cycle, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Machine Learning-Assisted Screening of Transition Metal-Doped TMDs for Binding Energy and Charge Transfer Prediction

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112603 - 112603

Published: April 1, 2025

Language: Английский

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Enhancing arsenate removal through interpretable machine learning guiding the modular design of metal–organic frameworks

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 497, P. 155058 - 155058

Published: Aug. 23, 2024

Language: Английский

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Machine learning of metal-organic framework design for carbon dioxide capture and utilization

Journal of CO2 Utilization, Journal Year: 2024, Volume and Issue: 89, P. 102941 - 102941

Published: Oct. 21, 2024

Language: Английский

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A resorcin[4]arene-based MOF as a Lewis acid catalyst for the CO2 coupling reaction

Inorganic Chemistry Communications, Journal Year: 2024, Volume and Issue: unknown, P. 113341 - 113341

Published: Oct. 1, 2024

Language: Английский

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