Hybrid Isotherm–Machine Learning Modeling of CO₂ Adsorption in Modulator-Tuned MIL-101(Cr)

Machine Learning for Magnetite Nanoparticles Contaminant Adsorption under Uncertainty: Revealing Data Quality Limits

Despite the rapid adoption of machine learning (ML) in materials discovery, its application to water contaminant adsorption remains fundamentally constrained. In this study, we systematically evaluated whether current peer-reviewed literature on magnetite nanoparticle (MNPs) adsorbents contains sufficient descriptor...

Machine learning aided RSM approach to parameter optimization for Rhodamine B removal using sawdust

Machine learning-assisted prediction of cesium adsorption using metal–organic frameworks/reduced graphene oxide composite

Accelerating CO2 uptake modeling in carbon-based adsorbents through machine learning

Climate change is driven by numerous factors but one of the primary drivers includes the rapid increase in atmospheric carbon dioxide (CO 2 ) concentration. This necessitates the need for the development of efficient carbon capture technologies. CO 2 adsorption through porous carbon-based materials has emerged...

Sustainable sugarcane bagasse-derived activated carbon for comparative Cr(VI) and Co(II) removal: experimental and ANN modeling integration

Hybrid sampling approach to machine-learning potentials for gas adsorption: Hydrogen adsorption in MOF-303

Porous materials such as metal-organic frameworks (MOFs) are widely studied for applications in catalysis and gas adsorption, including carbon dioxide capture and hydrogen storage for energy and environmental challenges. Accurate simulation of gas adsorption in these materials remains challenging because most approac...