Impedance Sensor for Real-Time Ammonium Detection Based on MWCNT/ZnO Nanocomposites.

This work reports on the development of an impedance sensor-based real-time-field specific system to monitor aqueous Ammonium (NH4+). The sensing element was fabricated by modifying screen-printed interdigitated electrodes (IDEs) with a hybrid nanocomposite of Multi-Wall Carbon Nanotube (MWCNT) with Zinc Oxide (ZnO) nanocrystals. The NH4+ of the water was monitored, and it exhibited a sensitivity of 67.13 Ω /mM with average correlation coefficients of 0.80. The impedance magnitude ( Ω ) of the NH4+ sensor was unaffected by the presence of Fe2+, Ni2+, K+ and P+ interfering cations. The developed sensor was interfaced with an IoT-enabled NodeMCU microcontroller, enabling a direct method for continuous monitoring of NH4+ concentrations. This integrated system is interconnected to the field-deployed sensor nodes, which provide real-time NH4+ levels to the remote user through web applications.

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

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 features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ ion levels. The proposed NH4+ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.