journal article·2025-03-26Open access

Quantification of lead through rod-shaped silver-doped zinc oxide nanoparticles using an electrochemical approach

Ravinder Lamba, Gaurav Bhanjana, Neeraj Dilbaghi, Vivek Gupta, Sandeep Kumar

Abstract

Special features of zinc oxide nanoparticles have drawn a lot of interest due to their wide bandgap, high surface area, photocatalytic activity, antimicrobial activity, and semiconductor properties. By doping ZnO nanoparticles with transition metals, we can alter their electrical, optical, and magnetic properties by introducing new electronic states into the band structure. Herein, Ag is added to ZnO nanostructures to improve their optical properties to detect heavy metal lead ions. The prepared lead sensor with ultrahigh sensitivity, based on silver-doped ZnO nanorods (Ag@ZnO NRs), was fabricated and characterized. The morphological, structural, compositional, and optical characteristics of the Ag@ZnO NRs were investigated using a variety of methods after they were fabricated using a low-temperature co-precipitation method. The resulting Ag@ZnO NRs had good optical properties, nanorod morphologies, and high crystallinity with no impurities. Technological advancements are leading people to use lightweight electronics and affordable sensors. Electrochemical techniques comparatively offer quick, portable, sensitive, and inexpensive basic equipment for heavy metal detection. The interactions between Ag@ZnO NRs and lead were studied using electrochemical methods. The prepared lead sensor using Ag@ZnO NRs show a very low detection limit and a very high sensitivity toward lead. The lead chemical sensor that was developed had a detection limit of 3 ppm with a sensitivity of 16 µA·ppm −1 ·cm −2 . The recorded reaction time of lead sensor was less than two seconds.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Electrochemical Analysis and Applications · Analytical Chemistry and Sensors · Gas Sensing Nanomaterials and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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