Conventional aqueous alkanolamines suffer from high regeneration energy, corrosion, and solvent degradation, limiting large‐scale CO
2
capture. Here, a machine‐learning‐assisted strategy was developed to screen nonaqueous absorbents from a limited experimental dataset by correlating molecular structure with CO
2
desorption performance. The identified 2‐(butylamino)ethanol (BEA)‐tetramethylethylenediamine (TMEDA) system achieved a desorption capacity of 0.668 mol CO
2
·mol
−1
amine and a maximum desorption rate of 0.0513 mol CO
2
·mol
−1
amine·min
−1
, while exhibiting good cyclic stability, low‐temperature performance, and low corrosivity. Its regeneration energy requirement was only 1.38 GJ·t
−1
CO
2
, 62.3% lower than that of aqueous monoethanolamine. Quantitative
13
C NMR spectroscopy and density functional theory calculations revealed that BEA governs CO
2
fixation, whereas TMEDA acts as a proton acceptor, facilitates proton transfer, destabilizes the intermediate products, and lowers the heat of reaction. This work establishes a data‐driven and mechanistically informed framework for the rational development of energy‐efficient nonaqueous absorbents for CO
2
capture.
Machine learning (ML) offers a promising approach for predicting CO
2
solubility, yet its application to the systematic screening of non‐ionic deep eutectic solvents (DESs) remains limited. Herein, a ML‐assisted framework integrating COSMO‐RS‐derived molecular descriptors, theoretical screening, and experimental...
Two‐dimensional covalent organic frameworks (2D‐COFs) with tunable structures offer a promising platform for photocatalytic overall water splitting (OWS) under visible‐light. However, it is challenging to quickly find photocatalytic OWS materials from the massive amounts of 2D‐COFs and then achieve precise synthesi...
Rui Zhang, Zhao-Di Yang, Xiao-Yu Chu et al.· Advanced Energy Materials· 0 citations
Solid‐state lithium batteries are a leading candidate for next‐generation high‐energy‐density and high‐safety batteries but suffer from inherent trade‐offs among ionic conductivity, electrochemical stability, and mechanical strength in solid‐state electrolytes, as well as complex electrode–electrolyte interfacial cou...
Ji-Han Wu, Hui-Qun Wang, Chen Wang et al.· Advanced Functional Material...· 0 citations
This review offers a comprehensive and mechanistic analysis of the six principal modification strategies: lattice engineering, heteroatom doping, heterojunction and interface engineering, carbon‐based hybridization, morphology engineering, and surface reconstruction and pre‐catalyst design.
Y. El Issmaeli, Amina Lahrichi, Hoje Chun et al.· Advanced Functional Material...· 0 citations
Carbon dioxide (CO2) electroreduction is a promising route for carbon recycling and energy regeneration, with the development of dual-atom catalysts (DACs) being critical to its advancement. Conventional density functional theory (DFT) calculations and machine learning (ML) models based on DFT-derived descriptors are p...
Molecular design of an active electrocatalyst can determine the primary product. Precise selection of the active metal center influences the adsorption of reactants, intermediates, and product selectivity and ultimately affects the efficiency of the process. Here, we predict a potent catalyst, namely, Fe@C6N6, with e...
Moumita Mukherjee, Arko Mohari, S. Dutta et al.· ACS Applied Energy Materials· 0 citations
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