This is the web page of PoLA project.

PoLA (Porosity Local Analysis) is a computational tool to analyze the inner structure of nanoporous materials: it can be applied either to atomistic models, or to real samples. PoLA is developed and maintained at the University of Eastern Piedmont (Italy): please cite it as indicated below.

To describe the local porosity of atomistic models, please follow the link to download the code and the instructions: PoLA can be aplied to models of any material, like amorphous carbons, zeolites, silica, MOF and others, only providing the atomic coordinates.

For real samples, PoLA uses a Neural Network machine learning algorithm (presently trained only for amorphous carbons): it requires the N2 excess adsorption isotherm at  77 K and outputs the analysis of the porous volume, along with a prediction of the H2  excess uptake at 77 K.

Porosity Local Analysis

the pore space seen from the inside
The porous volume inside the material is divided in small elements ("blocks") characterized by their MinD (the minimum distance from opposite walls)

Traditional Description

(PSD / DFT)
PoLA combined with Neural Network machine learning provides the volume analysis and the H2 isotherm prediction starting from experimental N2 isotherm

Citations: 

1) M. Cossi, A. Zoccante, V. Palumbo, M. D’Amore, F. Vallejos-Burgos, R. Kukobat, A. Moreo, F. Sebastiani, S. Brandani, E. Mangano, F. Begni, G. Celoria, L. Marchese
“Porosity Local Analysis (PoLA) for nanoporous carbons. Porous volume characterization and prediction of gas adsorption isotherms” Carbon 2026, DOI:10.1016/j.carbon.2026.121713

2) A. Zoccante, M. D’Amore, C. A. Guido, A. Fortunelli, G. Conter, L. Marchese, M. Cossi “Porosity Local Analysis (PoLA): A New Approach to Describe the Porous Volume Distribution in Amorphous Carbons” ACS Omega 2025, 10, 31623−31637.