Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension von Iskandarov, Javad, Ahmed, Shehzad, Fanourgakis, George S., Alameri, Waleed, Froudakis, George E., Karanikolos, Georgios N.

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Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration von Iskandarov, Javad, Fanourgakis, George S, Ahmed, Shehzad, Alameri, Waleed, Froudakis, George E, Karanikolos, Georgios N

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2D MXenes for controlled releases of therapeutic proteins von Seitak, Aibobek, Shanti, Aya, Al Adem, Kenana, Farid, Nouran, Luo, Shaohong, Iskandarov, Javad, Karanikolos, Georgios N., Liao, Kin, Chan, Vincent, Lee, Sungmun

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Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions von Iskandarov, Javad, Fanourgakis, George S., Ahmed, Shehzad, Alameri, Waleed, Froudakis, George E., Karanikolos, Georgios N.

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Data-driven prediction of in situ CO 2 foam strength for enhanced oil recovery and carbon sequestration von Iskandarov, Javad, Fanourgakis, George S, Ahmed, Shehzad, Alameri, Waleed, Froudakis, George E, Karanikolos, Georgios N

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Anionic 1-Aza-3,4-diphospholides as redox active ligands von Suter, Riccardo, Gilliard, Robert J., Iskandarov, Javad, Benkő, Zoltan, Wörle, Michael, Grützmacher, Hansjörg

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Data-driven prediction of CO foam strength for enhanced oil recovery and carbon sequestration von Iskandarov, Javad, Fanourgakis, George S, Ahmed, Shehzad, Alameri, Waleed, Froudakis, George E, Karanikolos, Georgios N

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