Application of machine learning in adsorption energy storage using metal organic frameworks: A review

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115363 - 115363

Published: Jan. 13, 2025

Language: Английский

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Artificial Intelligence (AI) Can Advance Plastic Sustainability and Circular Economy

ACS Sustainable Resource Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

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Architecting Metal–Organic Frameworks at Molecular Level toward Direct Air Capture

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Escalating carbon dioxide (CO2) emissions have intensified the greenhouse effect, posing a significant long-term threat to environmental sustainability. Direct air capture (DAC) has emerged as promising approach achieving net-zero future, which offers several practical advantages, such independence from specific CO2 emission sources, economic feasibility, flexible deployment, and minimal risk of leakage. The design optimization DAC sorbents are crucial for accelerating industrial adoption. Metal-organic frameworks (MOFs), with high structural order tunable pore sizes, present an ideal solution strong guest-host interactions under trace conditions. This perspective highlights recent advancements in using MOFs DAC, examines molecular-level effects water vapor on capture, reviews data-driven computational screening methods develop molecularly programmable MOF platform identifying optimal sorbents, discusses scale-up cost DAC.

Language: Английский

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Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Even if MOFs are recently developed for large-scale applications, the road to applications of is long and rocky. This requires overcome challenges associated with phase discovery, synthesis optimization, basic advanced characterization, computational studies. Lab-scale results need be transferred processes, which often not trivial, life-cycle analyses techno-economic performed realistically assess their potential industrial relevance. Based on experience in field stable, functional combining synthesis, modeling, this mini-review gives recommendations especially non-specialists, example, from chemical engineers medical doctors, accelerate facilitate knowledge transfer will ultimately lead application MOFs. The include reporting characterization data as well standardization detailed information required mining machine learning techniques, increasingly used discovery new materials analysis. Once a suitable MOF identified its key properties determined, translational studies shall finally carried out collaboration end-users validate performance under real conditions allow understanding processes involved.

Language: Английский

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Zinc Single-Atom Nanozyme As Carbonic Anhydrase Mimic for CO₂ Capture and Conversion

ACS Materials Au, Journal Year: 2025, Volume and Issue: 5(2), P. 377 - 384

Published: Jan. 31, 2025

Single-atom nanozymes (SANs) are a class of with metal centers that mimic the structure metalloenzymes. Herein, we report synthesis Zn–N–C SAN, which mimics action natural carbonic anhydrase enzyme. The two-step annealing technique led to content more than 18 wt %. Since act as active sites, this high loading resulted in superior catalytic activity. Zn-SAN showed CO2 uptake 2.3 mmol/g and final conversion bicarbonate 91%. was converted via biomimetic process by allowing its adsorption catalyst, followed addition catalyst HEPES buffer (pH = 8) start into HCO3–. Afterward, CaCl2 added form white CaCO3 precipitate, then filtered, dried, weighed. Active carbon MCM-41 were used controls under same reaction conditions. According findings, sequestration capacity 42 mg CaCO3/mg Zn-SAN. Some amino acids (AAs) binding affinity for Zn able suppress enzymatic activity blocking centers. This strategy detection His, Cys, Glu, Asp limits 0.011, 0.031, 0.029, 0.062 μM, respectively, hence utilized quantifying these AAs commercial dietary supplements.

Language: Английский

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Review and analysis of porous adsorbents for effective CO2 capture

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 215, P. 115589 - 115589

Published: March 16, 2025

Language: Английский

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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials

Communications Materials, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 2, 2025

Language: Английский

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High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Metal-embedded complexes (MECs), including transition metal (TMCs) and metal-organic frameworks (MOFs), are important in catalysis, materials science, molecular devices due to their unique atom centrality complex coordination environments. However, modeling predicting properties accurately is challenging. A new attention (MA) framework for graph neural networks (GNNs) was proposed address the limitations of traditional methods, which fail differentiate core structures from ordinary covalent bonds. This MA converts heterogeneous graphs into homogeneous ones with distinct features by highlighting key metal-feature through hierarchical pooling a cross-attention. To assess its performance, 11 widely used GNN algorithms, three heterogeneous, were compared. Experimental results indicate significant improvements accuracy: an average 32.07% TMC up 23.01% MOF CO2 absorption. Moreover, tests on framework's robustness regarding data set size variation comparison larger non-MA model show that enhanced performance stems architecture, not merely increasing capacity. The potential offers potent statistical tool optimizing designing like catalysts gas storage systems.

Language: Английский

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Machine Learning-Driven Prediction and Design of High-Performance Metal–Organic Frameworks for Co₂ Capture

Published: Jan. 1, 2025

Language: Английский

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Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 30, 2025

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods material research, which mainly rely on manual experimentation, not particularly efficient, while with advancements in computer science, high-throughput computational screening based molecular simulation have become crucial discovery, yet they face limitations terms resources time. Currently, machine learning (ML) has emerged as transformative tool many fields, capable analyzing large data sets, identifying underlying patterns, predicting performance efficiently accurately. This approach, termed "materials genomics", combines ML to predict design high-performance materials, significantly speeding up the discovery process compared traditional methods. review discusses functions screening, design, prediction COFs highlights their applications across various domains like thereby providing new research directions enhancing understanding COF applications.

Language: Английский

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