Porous carbons are an important class of porous materials with many applications, including gas separation. An N₂ 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 N₂ adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N₂ 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/N₂ as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO₂/N₂ 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.

中文翻译：

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通过卷积神经网络预测77 K下N2吸附等温线对多孔碳中CO2 / N2选择性的影响。

多孔碳是一类重要的多孔材料，具有许多应用，包括气体分离。在77 K的N ₂吸附等温线是表征孔隙率最广泛使用的方法。通常，通过将其与吸附理论相适应，然后将其与气体分离性能（吸收和选择性）相关联，从77 K的N ₂吸附等温线得出诸如表面积和孔体积之类的文本属性。在这里，在77 K时的N ₂等温线直接用作输入（表示孔隙度的特征描述符），以训练卷积神经网络来预测气体分离性能（使用CO ₂ / N ₂作为测试用例）。探索了多孔碳的孔隙空间，以实现更高的CO ₂ / N ₂选择性。具有良好分离的中孔（3–7 nm）和微孔（<2 nm）的双峰孔径分布的多孔碳是最有前途的。这项工作将有助于指导具有理想孔隙率的多孔碳的实验研究，以用于气体分离和其他应用。