Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

HV Thanh, M Rahimi, S Tangparitkul… - International Journal of …, 2024 - Elsevier
This study introduces machine learning (ML) algorithms to predict hydrogen (H 2)
thermodynamic properties for geological storage, focusing on its mixtures with natural gas …

Porous Carbon Materials for Enhanced Carbon Dioxide Capture Toward Post-Combustion: Innovative Application and Future Prospects

Y Ji, W Xu, A Chen, J Du, S Hou - Materials Today Energy, 2024 - Elsevier
With the increasing urgency of global climate change, developing efficient and economical
carbon dioxide (CO 2) capture technologies has become the key to mitigating the …

Sustainable polymeric adsorbents for adsorption-based water remediation and pathogen deactivation: a review

H Alkhaldi, S Alharthi, S Alharthi, HA AlGhamdi… - RSC …, 2024 - pubs.rsc.org
Water is a fundamental resource, yet various contaminants increasingly threaten its quality,
necessitating effective remediation strategies. Sustainable polymeric adsorbents have …

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

M Rahimi, A Mehrpanah, P Mouchani… - Industrial & …, 2024 - ACS Publications
Carbon materials possess active sites and functionalities on the surface that can attract
prominent interest as solid adsorbents for diverse gas adsorption. This study aimed to …

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

N Ramasamy, AJLP Raj, VV Akula… - … Science and Pollution …, 2024 - Springer
CO2 emissions have been steadily increasing and have been a major contributor for climate
change compelling nations to take decisive action fast. The average global temperature …