Recent development of metal-organic frameworks and their composites in electromagnetic wave absorption and shielding applications

Advances in Colloid and Interface Science, Год журнала: 2024, Номер 332, С. 103271 - 103271

Опубликована: Авг. 8, 2024

Язык: Английский

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Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery

Coordination Chemistry Reviews, Год журнала: 2024, Номер 514, С. 215888 - 215888

Опубликована: Май 8, 2024

Язык: Английский

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Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning

Materials Today Sustainability, Год журнала: 2024, Номер 27, С. 100900 - 100900

Опубликована: Июнь 29, 2024

Язык: Английский

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MOF membranes for gas separations

Progress in Materials Science, Год журнала: 2025, Номер unknown, С. 101432 - 101432

Опубликована: Янв. 1, 2025

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Ionic Liquid-Functionalized Metal–Organic Frameworks/Covalent–Organic Frameworks for CO₂ Capture and Conversion

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(8), С. 3443 - 3464

Опубликована: Фев. 14, 2024

CO2 capture and conversion have garnered worldwide attention in view of the objective sustainable development carbon neutrality. Recently, ionic liquid-functionalized metal–organic frameworks (MOFs) or covalent–organic (COFs) (MOFs/COFs) offer a rising platform for effective separation from specific gas mixture into value-added chemicals. Benefiting synergistic effect offered by ILs MOFs/COFs, IL-MOFs/COFs exhibit better exceptional adsorption/catalytic performance than pristine MOFs/COFs ILs. Herein, review intends to establish primary database recently emerging conversion, covering functionalization strategies, interaction between representative applications, aiding rational design optimization novel with properties real-world application. Along this line, different systems (CO2/N2, CO2/CH4, CO2/C2H2) further multiproducts (cyclic carbonate, hydrocarbon, alcohol, others), along mechanism insight such processes are summarized discussed. Furthermore, challenges prospects topical fields been elaborated.

Язык: Английский

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Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

Advanced Materials, Год журнала: 2024, Номер unknown

Опубликована: Июль 27, 2024

Abstract Metal–organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption separation applications. Shortly after discovery through experimental synthesis, computational simulations quickly become an important method in broadening use MOFs by offering deep insights into structural, functional, performance properties. This review specifically addresses pivotal role molecular enlarging understanding enhancing applications, particularly adsorption. After reviewing historical development implementation simulation methods field MOFs, high‐throughput screening (HTCS) studies used to unlock potential CO 2 capture, CH 4 storage, H water harvesting are visited recent advancements these applications highlighted. The transformative impact integrating artificial intelligence with HTCS on prediction MOFs’ directing efforts promising materials is addressed. An outlook current opportunities challenges accelerate finally provided.

Язык: Английский

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Machine learning for the advancement of membrane science and technology: A critical review

Journal of Membrane Science, Год журнала: 2024, Номер 713, С. 123256 - 123256

Опубликована: Сен. 3, 2024

Machine learning (ML) has been rapidly transforming the landscape of natural sciences and potential to revolutionize process data analysis hypothesis formulation as well expand scientific knowledge. ML particularly instrumental in advancement cheminformatics materials science, including membrane technology. In this review, we analyze current state-of-the-art membrane-related applications from perspectives. We first discuss foundations different algorithms design choices. Then, traditional deep methods, application examples literature, are reported. also importance both molecular membrane-system featurization. Moreover, follow up on discussion with science detail literature using data-driven methods property prediction fabrication. Various fields discussed, such reverse osmosis, gas separation, nanofiltration. differentiate between downstream predictive tasks generative design. Additionally, formulate best practices minimum requirements for reporting reproducible studies field membranes. This is systematic comprehensive review science.

Язык: Английский

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Assessing CO2 Separation Performances of IL/ZIF-8 Composites Using Molecular Features of ILs

Carbon Capture Science & Technology, Год журнала: 2025, Номер 14, С. 100373 - 100373

Опубликована: Янв. 21, 2025

Язык: Английский

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Machine learning-assisted computational exploration of the optimal loading of IL in IL/COF composites for carbon dioxide capture

Journal of Materials Chemistry A, Год журнала: 2023, Номер 11(27), С. 14911 - 14920

Опубликована: Янв. 1, 2023

The “volumetric loading ratio” is proposed as a descriptor for regulating the IL of IL/COF composites. and COFs can form CO 2 favorable “wire-tube” “wall-arm” type structures in with pore sizes <10 Å ≥10 Å, respectively.

Язык: Английский

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Accelerated Discovery of Metal–Organic Frameworks for CO₂ Capture by Artificial Intelligence

Industrial & Engineering Chemistry Research, Год журнала: 2023, Номер 63(1), С. 37 - 48

Опубликована: Дек. 25, 2023

The existence of a very large number porous materials is great opportunity to develop innovative technologies for carbon dioxide (CO2) capture address the climate change problem. On other hand, identifying most promising adsorbent and membrane candidates using iterative experimental testing brute-force computer simulations challenging due enormous variety materials. Artificial intelligence (AI) has recently been integrated into molecular modeling materials, specifically metal–organic frameworks (MOFs), accelerate design discovery high-performing adsorbents membranes CO2 adsorption separation. In this perspective, we highlight pioneering works in which AI, simulations, experiments have combined produce exceptional MOFs MOF-based composites that outperform traditional capture. We outline future directions by discussing current opportunities challenges field harnessing experiments, theory, AI accelerated

Язык: Английский

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