Comprehensive Dataset of 72 Electro-deposited ZnO Nanostructured Sensors for Acetone Detection in E-Nose Applications

Abstract

This dataset presents a comprehensive experimental study of 72 individual zinc oxide (ZnO) nanostructured sensors designed for electronic nose (E-Nose) applications, specifically targeting high-sensitivity acetone detection. The sensors were fabricated using an optimized electrodeposition process, where three key manufacturing parameters were systematically varied: ZnCl₂ molarity (0.01M to 0.2M), current density (-100µA to -5mA), and deposition time (10s to 60s).

The data is organized into three primary categories: (1) Dynamic Gas Sensing Records, featuring a 3-loop exposure sequence to varying acetone concentrations (0.1 ppm to 1.0 ppm); (2) Thermal Characterization Profiles, providing baseline resistance-temperature behavior for all 72 samples; and (3) Statistical Performance Metrics, including Signal-to-Noise Ratio (SNR) calculations and noise scaling analysis. This multi-parametric matrix (comprising over 2,000 sensing cycles) provides a critical foundation for machine learning-based gas identification and the optimization of nanomanufacturing protocols for highly sensitive, low-cost gas sensors.