Rahimi, M. Et Al. 2022. Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , vol.61, no.30 , 10670-10688.

Rahimi, M., Abbaspour-Fard, M. H., Rohani, A., YÜKSEL ORHAN, Ö., & Li, X., (2022). Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , vol.61, no.30, 10670-10688.

Rahimi, Mohammad Et Al. "Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches," INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , vol.61, no.30, 10670-10688, 2022

Rahimi, Mohammad Et Al. "Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches." INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , vol.61, no.30, pp.10670-10688, 2022

Rahimi, M. Et Al. (2022) . "Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches." INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , vol.61, no.30, pp.10670-10688.

@article{article, author={Mohammad Rahimi Et Al. }, title={Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches}, journal={INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH}, year=2022, pages={10670-10688} }