Abstract
Developing electrochemical sensors capable of detecting multiple analytes at distinct potentials is vital for applications in environmental, biomedical, and quality monitoring. Here, we explore nanostructured, nonenzymatic α-Ni(OH)2 as a versatile sensing material for the selective detection of phenol, catechol, and p-nitrophenol using two platforms: a standard three-electrode system and a portable strip sensor. α-Ni(OH)2 was synthesized via a wet-chemical method and coated onto glassy carbon and screen-printed carbon electrodes for the respective configurations. Electron microscopy confirmed semicrystalline nanoscale morphology (nanoparticulate films), and cyclic voltammetry revealed clear redox signatures for each analyte, enabling selective detection with distinct peak positions across both systems. The three-electrode setup reached limits of detection of 0.003 μM (phenol), 0.1 μM (catechol), and 1 μM (p-nitrophenol), whereas the portable sensor achieved 0.3, 1, and 2 μM, respectively. Amperometric measurements confirmed sensor performance at target potentials. Additionally, machine learning algorithms were applied to model signal behavior and support analyte classification. This combined approach demonstrates a robust strategy for sensitive, selective, and portable detection of multiple phenolic compounds.
| Original language | English |
|---|---|
| Pages (from-to) | 20463-20476 |
| Number of pages | 14 |
| Journal | ACS Applied Nano Materials |
| Volume | 8 |
| Issue number | 42 |
| Early online date | 10 Oct 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 10 Oct 2025 |
Keywords
- phenolic compounds
- electrochemical sensors
- cyclic voltammetry
- α-Ni(OH)2
- machine learning