Results 141 to 150 of about 54,430 (261)

Magnetic field‐assisted concentration swing adsorption for CO2 capture over Fe3O4/fibrous nanosilica‐PEI adsorbent

open access: yesAIChE Journal, EarlyView.
Abstract Magnetic field‐assisted concentration swing adsorption (CSA) provides an electrification‐compatible alternative to conventional temperature swing adsorption (TSA) by enabling rapid heating/cooling and energy‐efficient regeneration under near‐isothermal conditions, thereby eliminating the need for sensible heat input.
Xiaohao Jia, Fateme Rezaei
wiley   +1 more source

Perimeter‐intensified inverse Zr‐Co oxygen carrier for biomass chemical‐looping gasification with high CO selectivity

open access: yesAIChE Journal, EarlyView.
Abstract Chemical‐looping gasification (CLG) offers a promising route for renewable biomass valorization, yet conventional oxygen carrier regeneration with O₂/H₂O is energy‐intensive and often produces low‐quality syngas. Here, we develop an inverse ZrO2/Co3O4 oxygen carrier that enables selective biochar oxidation and efficient lattice‐oxygen transfer
Junling Gao   +9 more
wiley   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod   +10 more
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

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