Machine learning‐assisted design of low‐energy, high‐efficiency nonaqueous amine solutions
Xinping Yang, Juntao Wang, Yiming Zhao, Yongchun Zhang, Anmin Liu, Qilei Liu, Chong PengAbstract
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.