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The Science of the Total Environment|November 12, 2025
Compositional machine learning frameworks for predicting mercury solubility in natural gas: Bridging predictive accuracy with environmental and operational integritySaad Alatefi, Menad Nait Amar, Ahmad AlkouhJournal of Contaminant Hydrology|December 30, 2025
Explainable advanced modelling of interfacial tension in H2 - CO2 - CH4 - brine systems for sustainable subsurface storage in saline aquiferSaad Alatefi, Okorie Ekwe Agwu, Menad Nait Amar, et al.Journal of Contaminant Hydrology|July 2, 2025
Toward explicit learning frameworks for predicting the solubility of CO2 - N2 gas mixtures in brine: Implication for impure CO2 storage in saline aquifersSaad Alatefi, Menad Nait Amar, Okorie Ekwe Agwu, et al.Scientific Reports|August 2, 2023
On the evaluation of the carbon dioxide solubility in polymers using gene expression programmingBehnam Amiri-Ramsheh, Menad Nait Amar, Mohammadhadi Shateri, et al.The Journal of Physical Chemistry. B|June 17, 2020
Prediction of Lattice Constant of A2XY6 Cubic Crystals Using Gene Expression ProgrammingMenad Nait Amar, Mohammed Abdelfetah Ghriga, Mohamed El Amine Ben Seghier, et al.Molecules (Basel, Switzerland)|January 5, 2021
Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent SystemSeyed Pezhman Mousavi, Saeid Atashrouz, Menad Nait Amar, et al.Journal of Hazardous Materials|April 26, 2025
A reliable model to predict mercury solubility in natural gas components: A robust machine learning framework and data assessmentMenad Nait Amar, Noureddine Zeraibi, Hakim Djema, et al.Pageof 1