Use of Nanoparticles for Environmental Remediation

Gulzar, Abu Barkat Md; Mazumder, Pranab Behari

doi:10.4018/978-1-7998-8936-6.ch010

Cited by 1 publication

(1 citation statement)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nanoparticles have shown great potential in medical applications such as drug delivery, imaging, and cancer therapy, as they can be designed to target specific cells and tissues [ 2 , 3 , 4 , 5 ]. In addition, they are being explored for their use in the oil and gas industry [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ], energy storage and conversion [ 16 , 17 , 18 , 19 ], environmental remediation [ 20 , 21 ], and information and communication technology, including the manufacture of smaller and faster electronic devices and data storage systems [ 22 , 23 ]. Overall, nanoparticles hold great potential for a wide range of applications and their unique properties make them a promising area of research.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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 literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

show abstract