A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks

A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks

Abstract This study employed some machine learning (ML) techniques with Python programming to forecast the adsorption capacity of MOF adsorbents for thiophenic compounds namely benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyl dibenzothiophene (4,6-DMDBT). Five ML models were developed w...

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Bibliographic Details
Main Authors: Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Adsorptive desulfurization
Thiophenic compounds removal
MOFs adsorbents
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-86689-2
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https://doi.org/10.1038/s41598-025-86689-2

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