Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO2 Capture and Conversion with FAIR Principles DOI Creative Commons
Claudia Bizzarri, Manuel Tsotsalas

Advanced Energy and Sustainability Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

Metal‐organic frameworks (MOFs) have emerged as key materials for carbon capture and conversion, particularly in photocatalytic CO 2 reduction. However, inconsistent reporting of essential parameters the literature hinders informed decisions about material selection optimization. This perspective highlights need a user‐friendly, centralized database supported by automated data extraction using natural language processing tools to streamline comparisons MOF materials. By consolidating crucial from scientific literature, such promotes efficient decision‐making utilization. Emphasizing significance open‐source initiatives principles FAIR data—ensuring are Findable, Accessible, Interoperable, Reusable—a collaborative approach management sharing is advocated for. Making database‐accessible worldwide enhances quality reliability, fostering innovation progress conversion Additionally, databases valuable creating artificial intelligence assist researchers discovery synthesis conversion.

Language: Английский

Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries DOI Creative Commons
Jiadong Shen, Junjie Chen,

Xiaosa Xu

et al.

Nano Letters, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhances the ionic conductivity transference numbers. However, solvent complexity hinders general screening methods. We establish a universal criterion linking electronic (highest occupied molecular orbital (HOMO), lowest unoccupied (LUMO)) macroscopic properties (dielectric constant, dipole moment, polarizability) via machine learning on an ∼10 000-solvent dataset from high-throughput density functional theory (DFT). Two solvents, N-methoxy-N-methyl-2,2,2-trifluoroacetamide 2,2,2-trifluoro-N,N-dimethylacetamide were identified. Experimental incorporation of trace into poly(vinylidene fluoride-co-hexafluoropropylene) matrix achieves 4.5 V window, 5.5 × 10-4 S cm-1 (30 °C), Li+ number 0.78. The cell retains 86.7% capacity over 500 cycles (LiFePO4) 98.7% after 200 at 2C (LiNi0.9Co0.05Mn0.05O2), outperforming 2,2,2-trifluoro-N,N-dimethylacetamide, dimethylformamide, N-methyl-2-pyrrolidone, dimethyl sulfoxide. This synergy enables balanced ion transport, wide stability, cycling durability, advancing safer high-energy lithium metal batteries. Our integrated approach establishes paradigm for rational SPE design, accelerating next-generation battery development.

Language: Английский

Citations

0

A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials DOI
Chuang Wang, Xingxing Cheng, Kai Luo

et al.

Materials Science and Engineering R Reports, Journal Year: 2025, Volume and Issue: 165, P. 101010 - 101010

Published: May 3, 2025

Language: Английский

Citations

0

Development of the CO2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms DOI
Hossein Mashhadimoslem, Mohammad Ali Abdol,

Kourosh Zanganeh

et al.

ACS Applied Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Language: Английский

Citations

2

Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment DOI
Qiang Ma, Yi Wang, Xianglong Zhang

et al.

Materials Science and Engineering B, Journal Year: 2024, Volume and Issue: 311, P. 117765 - 117765

Published: Nov. 1, 2024

Language: Английский

Citations

0

Closing the Loop in the Carbon Cycle: Enzymatic Reactions Housed in Metal–Organic Frameworks for CO2 Conversion to Methanol DOI

Praise K Moyo,

Gift Mehlana,

Banothile C. E. Makhubela

et al.

Applied Biochemistry and Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 26, 2024

Language: Английский

Citations

0

Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO2 Capture and Conversion with FAIR Principles DOI Creative Commons
Claudia Bizzarri, Manuel Tsotsalas

Advanced Energy and Sustainability Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

Metal‐organic frameworks (MOFs) have emerged as key materials for carbon capture and conversion, particularly in photocatalytic CO 2 reduction. However, inconsistent reporting of essential parameters the literature hinders informed decisions about material selection optimization. This perspective highlights need a user‐friendly, centralized database supported by automated data extraction using natural language processing tools to streamline comparisons MOF materials. By consolidating crucial from scientific literature, such promotes efficient decision‐making utilization. Emphasizing significance open‐source initiatives principles FAIR data—ensuring are Findable, Accessible, Interoperable, Reusable—a collaborative approach management sharing is advocated for. Making database‐accessible worldwide enhances quality reliability, fostering innovation progress conversion Additionally, databases valuable creating artificial intelligence assist researchers discovery synthesis conversion.

Language: Английский

Citations

0