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Open access Sep 2026

Data-driven modeling of supercritical CO₂ processing with optimized machine learning: prediction of clobetasol propionate solubility

Machine learning can be used to support data-driven modeling of supercritical CO₂ processing. The method of machine learning modeling is applied in this work for evaluation of small-molecule processing under supercritical conditions. As a necessary step, the solute solubility in the solvent is evaluated via different m...

N. Abu-Hamdeh, M. Ajour · 0 citations
Open access Sep 2026

Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems

Metal–organic frameworks (MOFs) are highly tunable porous materials whose performance is governed by complex interactions among structural, chemical, material, and operating variables. This study develops a leakage-aware, data-driven framework for predicting two distinct MOF performance endpoints: loading capacity an...

N. Abu-Hamdeh, M. Ajour, A. Milyani et al. · 0 citations

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