Prediction by Convolutional Neural Networks of CO2/N2Selectivity in Porous Carbons from N2Adsorption Isotherm at 77 K

被引:38
作者
Wang, Song [1 ]
Li, Yi [2 ]
Dai, Sheng [3 ,4 ]
Jiang, De-en [1 ]
机构
[1] Univ Calif Riverside, Dept Chem, Riverside, CA 92521 USA
[2] Jilin Univ, Coll Chem, State Key Lab Inorgan Synth & Preparat Chem, Changchun 130012, Jilin, Peoples R China
[3] Oak Ridge Natl Lab, Div Chem Sci, Oak Ridge, TN 37831 USA
[4] Univ Tennessee, Dept Chem, Knoxville, TN 37996 USA
关键词
adsorption; neural networks; machine learning; materials science; porous materials; OXYGEN REDUCTION; CO2; CAPTURE; ADSORPTION;
D O I
10.1002/anie.202005931
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Porous carbons are an important class of porous materials with many applications, including gas separation. An N(2)adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N(2)adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N(2)isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO2/N(2)as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO2/N(2)selectivity. Porous carbons with a bimodal pore-size distribution of well-separated mesopores (3-7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.
引用
收藏
页码:19645 / 19648
页数:4
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