Zoccante Alberto, D'Amore Maddalena, Guido Ciro Achille, Fortunelli Alessandro, Conter Giorgio, Marchese Leonardo, Cossi Maurizio
Dipartimento di Scienze e Innovazione Tecnologica (DISIT), Università del Piemonte Orientale, viale T. Michel 11, I-15121 Alessandria, Italy.
Centro di Ricerca e Sviluppo per il Risanamento e la Protezione Ambientale (Centro RiSPA), Joint-Lab DISIT/Syensqo, viale T. Michel 11, I-15121 Alessandria, Italy.
ACS Omega. 2025 Jul 15;10(29):31623-31637. doi: 10.1021/acsomega.5c02479. eCollection 2025 Jul 29.
A new procedure, named PoLA (Porous Local Analysis), is presented to describe the porosity of amorphous carbons accurately. Unlike models based on predefined geometrical pores, PoLA is based on a point-by-point description of the inner void, and it is particularly suitable for amorphous materials. The porous volume is partitioned into small elements (blocks) of user-defined size, and each block is assigned a micro-, meso-, or macroporous nature according to its minimum distance from the material walls. This method is very fast and characterizes any porous volume uniquely: most importantly, this distribution of volume allows one to predict the gas adsorption behavior of the material. To show this, a number of carbon models have been defined, spanning a large range of porosities, and the adsorption isotherm of nitrogen at 77 K has been accurately simulated with Grand Canonical Monte Carlo in each model. We show that PoLA porous volume distributions and adsorption isotherms are strongly correlated so that N isotherms at 77 K can be accurately predicted by a machine learning procedure on the basis of PoLA results. We expect that this approach will be of great help in the design of new adsorbents and in the interpretation of experimental gas adsorption.
本文提出了一种名为PoLA(多孔局部分析)的新方法,用于精确描述非晶碳的孔隙率。与基于预定义几何孔隙的模型不同,PoLA基于对内部空隙的逐点描述,特别适用于非晶材料。将多孔体积划分为用户定义大小的小单元(块),并根据每个块与材料壁的最小距离为其赋予微孔、介孔或大孔性质。该方法速度非常快,能唯一地表征任何多孔体积:最重要的是,这种体积分布使人们能够预测材料的气体吸附行为。为了证明这一点,定义了许多涵盖广泛孔隙率范围的碳模型,并在每个模型中用巨正则蒙特卡罗方法精确模拟了77K下氮气的吸附等温线。我们表明,PoLA多孔体积分布与吸附等温线密切相关,因此基于PoLA结果的机器学习程序可以准确预测77K下的N等温线。我们期望这种方法将对新型吸附剂的设计以及实验气体吸附的解释有很大帮助。