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

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.

本数据集针对72支用于电子鼻（electronic nose, E-Nose）应用、旨在实现高灵敏度丙酮检测的独立氧化锌（ZnO）纳米结构传感器，开展了系统性实验研究。该批次传感器采用优化后的电沉积工艺制备，实验过程中系统调控了三项核心制备参数：氯化锌（ZnCl₂）摩尔浓度（0.01M至0.2M）、电流密度（-100µA至-5mA）以及沉积时长（10s至60s）。

数据集内容分为三大核心类别：
1. 动态气体传感记录：包含针对0.1ppm至1.0ppm梯度丙酮浓度的三轮循环暴露传感序列；
2. 热特性表征曲线：提供全部72支传感器的基线电阻-温度响应特性；
3. 统计性能指标：包含信噪比（Signal-to-Noise Ratio, SNR）计算与噪声尺度分析。

该多参数数据集（包含超过2000次传感循环）为基于机器学习的气体识别任务，以及面向高灵敏度、低成本气体传感器的纳米制备工艺优化提供了关键的研究基础。