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

Journal of Energy Storage, Год журнала: 2025, Номер 111, С. 115363 - 115363

Опубликована: Янв. 13, 2025

Язык: Английский

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Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning

Applied Energy, Год журнала: 2025, Номер 383, С. 125346 - 125346

Опубликована: Янв. 17, 2025

Язык: Английский

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Boosting the Water Absorption of CAU-10: The Role of Hydrochloric Acid in Regulating Defect Concentration

Microporous and Mesoporous Materials, Год журнала: 2025, Номер unknown, С. 113510 - 113510

Опубликована: Янв. 1, 2025

Язык: Английский

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Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 132481 - 132481

Опубликована: Март 1, 2025

Язык: Английский

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Predicting CO2 Adsorption in Metal-Organic Frameworks: Integrating Machine Learning with Virtual Sample Generation

Results in Surfaces and Interfaces, Год журнала: 2025, Номер unknown, С. 100505 - 100505

Опубликована: Март 1, 2025

Язык: Английский

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A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints

Nanomaterials, Год журнала: 2025, Номер 15(3), С. 183 - 183

Опубликована: Янв. 24, 2025

Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal–organic frameworks (MOFs), with their unique structural properties, show significant potential HD adsorption applications. Due to the hazards of HD, most experimental studies focus on simulants, but molecular simulation research these simulants remains limited. Simulation analyses can uncover structure–performance relationships enable validation, optimizing methods, improving material design performance predictions. This study integrates simulations, machine learning (ML), fingerprinting (MFs) identify MOFs high simulant diethyl sulfide (DES), followed by in-depth analysis comparison. First, are categorized into Top, Middle, Bottom materials based efficiency. Univariate analysis, learning, then used compare distinguishing features fingerprints each category. helps optimal ranges Top materials, providing reference initial screening. Machine feature importance combined SHAP identifies key that significantly influence model predictions across categories, offering valuable insights future design. Molecular fingerprint reveals critical combinations, showing optimized when such as metal oxides, nitrogen-containing heterocycles, six-membered rings, C=C double bonds co-exist. The integrated using HTCS, ML, MFs provides new perspectives designing high-performance demonstrates developing CWAs simulants.

Язык: Английский

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Fe Template-Based Metal-Organic Frameworks for Advanced Gas and Vapor Adsorption: Unveiling the Role of Mesoporous Diffusivity and Density Functional Theory Insights in Functional Compatibility

Опубликована: Янв. 1, 2025

Язык: Английский

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Multifunctional Hydrogel Electronics for Synergistic Therapy and Visual Monitoring in Wound Healing

Advanced Healthcare Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 9, 2025

To overcome the limitations of precise monitoring and inefficient wound exudate management in healing, an advanced multifunctional hydrogel electronics (MHE) platform based on MXene@MOF/Fe3O4@C photonic crystal is developed. This combines optical/electrical sensing, synergistic therapy, real-time visual into a single, efficient system, offering comprehensive solution for healing. Under photothermal stimulation, releases metal ions that generate hydroxyl radicals, effectively eliminating antibiotic-resistant bacteria. Beyond its antibacterial efficacy, this system offers unprecedented through temperature-responsive visualization, while structural color changes upon absorption provide clear indication dressing replacement. By integrating these functionalities, MHE allows control therapeutic process, significantly improving healing treatment monitoring. The platform's sensing capabilities further broaden potential applications across other biomedical fields. breakthrough technology provides clinicians with powerful tool to optimize outcomes, marking major advancement care applications.

Язык: Английский

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Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review

Industrial & Engineering Chemistry Research, Год журнала: 2025, Номер 64(9), С. 4637 - 4668

Опубликована: Фев. 24, 2025

This review discusses the transformative impact of convergence artificial intelligence (AI) and laboratory automation on discovery synthesis metal–organic frameworks (MOFs). MOFs, known for their tunable structures extensive applications in fields such as energy storage, drug delivery, environmental remediation, pose significant challenges due to complex processes high structural diversity. Laboratory has streamlined repetitive tasks, enabled high-throughput screening reaction conditions, accelerated optimization protocols. The integration AI, particularly Transformers large language models (LLMs), further revolutionized MOF research by analyzing massive data sets, predicting material properties, guiding experimental design. emergence self-driving laboratories (SDLs), where AI-driven decision-making is coupled with automated experimentation, represents next frontier research. While remain fully realizing potential this synergistic approach, AI heralds a new era efficiency innovation engineering materials.

Язык: Английский

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Advances in methanol synthesis via MOF-based catalysts: A comprehensive review and critical analysis

Inorganic Chemistry Communications, Год журнала: 2025, Номер unknown, С. 114266 - 114266

Опубликована: Март 1, 2025

Язык: Английский

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