Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks
Tarek Lemaoui(University Ferhat Abbas of Setif), Inas M. AlNashef(Khalifa University of Science and Technology), Abir Boublia(University Ferhat Abbas of Setif), Manawwer Alam(King Saud University), Ahmad S. Darwish(Khalifa University of Science and Technology), Fawzi Banat(Khalifa University of Science and Technology), Byong‐Hun Jeon(Hanyang University), Sung‐Min Park(Hanyang University), Yacine Benguerba(University Ferhat Abbas of Setif)
Cited by 102
Related Papers
Schiff bases and their metal Complexes: A review on the history, synthesis, and applications
|Inorganic Chemistry Communications|2023|387
Densities of ammonium and phosphonium based deep eutectic solvents: Prediction using artificial intelligence and group contribution techniques
|Thermochimica Acta|2011|356
Prediction of Electrical Conductivity of Deep Eutectic Solvents Using COSMO-RS Sigma Profiles as Molecular Descriptors: A Quantitative Structure–Property Relationship Study
|Industrial & Engineering Chemistry Research|2020|153
Predicting the density and viscosity of hydrophobic eutectic solvents: towards the development of sustainable solvents
|Green Chemistry|2020|134
Molecular insights into plant–microbe interactions for sustainable remediation of contaminated environment
|Bioresource Technology|2021|107