Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning.

ABSTRACT: Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different li

SUBMITTER: Muneer R 

PROVIDER: S-EPMC10096557 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature