Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis

J Lin, Z Liu, Y Guo, S Wang, Z Tao, X Xue, R Li, S Feng… - Nano Today, 2023 - Elsevier
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …

Porous metal–organic frameworks for methane storage and capture: Status and challenges

D Li, L Chen, G Liu, Z Yuan, B Li, X Zhang, J Wei - New Carbon Materials, 2021 - Elsevier
In the process of global transition to a sustainable low-carbon economy, the two major low-
carbon energy technologies, namely, methane (CH 4) storage and methane capture face the …

[HTML][HTML] Assessing CH4/N2 separation potential of MOFs, COFs, IL/MOF, MOF/Polymer, and COF/Polymer composites

HC Gulbalkan, ZP Haslak, C Altintas, A Uzun… - Chemical Engineering …, 2022 - Elsevier
Separating CH 4/N 2 mixture is challenging, and performance of the existing materials is still
open to improvement. In this study, we examined both the adsorption-and membrane-based …

Effect of metal–organic framework (MOF) database selection on the assessment of gas storage and separation potentials of MOFs

H Daglar, HC Gulbalkan, G Avci… - Angewandte Chemie …, 2021 - Wiley Online Library
Abstract Development of computation‐ready metal–organic framework databases (MOF
DBs) has accelerated high‐throughput computational screening (HTCS) of materials to …

DigiMOF: a database of metal–organic framework synthesis information generated via text mining

LT Glasby, K Gubsch, R Bence, R Oktavian… - Chemistry of …, 2023 - ACS Publications
The vastness of materials space, particularly that which is concerned with metal–organic
frameworks (MOFs), creates the critical problem of performing efficient identification of …

Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation

Z Wang, T Zhou, K Sundmacher - Chemical Engineering Journal, 2022 - Elsevier
Interpretable machine learning (ML) is applied to accelerate the discovery of promising
metal–organic frameworks (MOFs) for the selective separation of ethane (C 2 H 6) and …

Nickel and iron-based metal-organic frameworks for removal of organic and inorganic model contaminants

AA Mohammadi, Z Niazi, K Heidari, A Afarinandeh… - Environmental …, 2022 - Elsevier
Metal-organic frameworks (MOFs) are a promising class of porous nanomaterials in the field
of environmental remediation. Ni-MOF and Fe-MOF were chosen for their advantages such …

Shell inspired heterogeneous membrane with smaller bandgap toward sunlight-activated sustainable water purification

Y Feng, C Luo, X Chen, J Gu, Y Zhang, M Chao… - Chemical Engineering …, 2022 - Elsevier
MXene attracts extensive interest due to its unique physicochemical properties and
adjustable transmission channels. However, severe membrane fouling has made the …

Construction of efficient g-C3N4/NH2-UiO-66 (Zr) heterojunction photocatalysts for wastewater purification

J Ren, S Lv, S Wang, M Bao, X Zhang, Y Gao… - Separation and …, 2021 - Elsevier
Abstract Herein, gC 3 N 4/NH 2-UiO-66 (Zr) heterojunction photocatalysts were successfully
constructed by a solvothermal and in-situ deposition route. It presented high-efficiency …

Computational Simulation of CO2/CH4 Separation on a Three-Dimensional Cd-Based Metal–Organic Framework

M Parsaei, K Akhbari, S Kawata - Crystal Growth & Design, 2023 - ACS Publications
Natural gas purification and biogas recovery require efficient separation of CO2 from CH4,
as CH4 is increasingly being recognized as a promising substitute for petroleum due to its …