Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

ABSTRACT: We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH₄⁺ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH₄⁺ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH₄⁺ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent feat

SUBMITTER: Aliyana AK 

PROVIDER: S-EPMC8692315 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature