TY - JOUR
T1 - Porosity Local Analysis (PoLA): A New Approach to Describe the Porous Volume Distribution in Amorphous Carbons
AU - Zoccante, Alberto
AU - D'AMORE, MADDALENA
AU - GUIDO, CIRO ACHILLE
AU - Fortunelli, Alessandro
AU - Conter, Giorgio
AU - MARCHESE, Leonardo
AU - COSSI, Maurizio
PY - 2025
Y1 - 2025
N2 - 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 N2 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
AB - 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 N2 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
UR - https://iris.uniupo.it/handle/11579/212902
U2 - 10.1021/acsomega.5c02479
DO - 10.1021/acsomega.5c02479
M3 - Article
SN - 2470-1343
JO - ACS Omega
JF - ACS Omega
ER -