Comprehensive Dataset of 72 Electro-deposited ZnO Nanostructured Sensors for Acetone Detection in E-Nose Applications
| Field | Value | Language |
| dc.contributor.author | Garay-Rairan, Fabian | |
| dc.contributor.author | Wang, Qi | |
| dc.contributor.author | Tricoli, Antonio | |
| dc.contributor.author | Qian, Jing | |
| dc.contributor.author | Lensky, Artem | |
| dc.contributor.author | Murugappan, Krishnan | |
| dc.contributor.author | Suominen, Hanna | |
| dc.date.accessioned | 2026-04-29T04:50:53Z | |
| dc.date.available | 2026-04-29T04:50:53Z | |
| dc.date.issued | 2026-04-29 | |
| dc.identifier.uri | https://hdl.handle.net/2123/35148 | |
| dc.description.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. | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 | en |
| dc.subject | ZnO nanostructures | en |
| dc.subject | Electrodeposition | en |
| dc.subject | Acetone detection | en |
| dc.subject | Electronic Nose (E-Nose) | en |
| dc.subject | Chemiresistive gas sensors | en |
| dc.subject | Nanomanufacturing optimization | en |
| dc.subject | Sensor characterization | en |
| dc.subject | Time-series resistance data | en |
| dc.title | Comprehensive Dataset of 72 Electro-deposited ZnO Nanostructured Sensors for Acetone Detection in E-Nose Applications | en |
| dc.type | Dataset | en |
| dc.subject.asrc | ANZSRC FoR code::40 ENGINEERING::4018 Nanotechnology::401806 Nanomanufacturing | en |
| dc.subject.asrc | ANZSRC FoR code::40 ENGINEERING::4018 Nanotechnology::401805 Nanofabrication, growth and self assembly | en |
| dc.subject.asrc | ANZSRC FoR code::40 ENGINEERING::4018 Nanotechnology::401807 Nanomaterials | en |
| dc.subject.asrc | ANZSRC FoR code::46 INFORMATION AND COMPUTING SCIENCES::4611 Machine learning | en |
| dc.identifier.doi | 10.25910/eb82-ng65 | |
| dc.relation.arc | DP190101864 | |
| dc.relation.arc | AS008 | |
| dc.description.method | 1. Materials and Substrates: The sensing layers were deposited on interdigitated electrodes (IDEs) sourced from Micrux Technologies (platinum electrodes on a glass substrate, 3.5 mm cell diameter). Reagents included zinc chloride (ZnCl₂ ≥ 98%) and potassium chloride (KCl ≥ 99%) from Sigma-Aldrich. High-purity gases (oxygen 99.995%, nitrogen 99.999%, and acetone 10 ppm in N₂ ) were sourced from BOC Australia. 2. Electrochemical Manufacturing (Synthesis) The 72 ZnO sensors were fabricated using a three-electrode setup via a Biologic electrochemical station. Process: Chronopotentiometry was employed at constant current settings (-100 μA, -250 μA, -500 μA, -1 mA, -3 mA, -5 mA) and varying ZnCl₂ molarities (0.01 M to 0.2 M) with a constant 0.1 M KCl background. Conditions: The electrolyte was maintained at 70°C in a silicon oil bath during deposition (times: 10s, 30s, 60s). Post-Processing: Samples were washed with deionized water, dried at 70°C, and subsequently calcined in a muffle furnace at 400°C for 2 hours to enhance crystallinity. 3. Gas Sensing Experimental Setup: Tests were performed in a Linkam HFS600E-PB4 gas sensing chamber. Stabilization: Sensors were stabilized in synthetic air (0.1 L/min O₂ and 0.4 L/min N₂) at 350°C for 2 hours, followed by 2 hours at the operating temperature of 300°C to ensure a steady baseline. Gas Delivery: Acetone concentrations (0.1–1 ppm) were regulated by Bronkhorst Mass Flow Controllers (MFCs) via a custom LabVIEW program, maintaining a constant total flow rate of 0.5 L/min. Data Acquisition: Real-time resistance data was acquired using a Keithley 2700 Digital Multimeter. 4. Data Processing and Dataset Generation For each of the 72 fabrication conditions, the response was recorded for 10 acetone concentrations (0.1 to 1 ppm) and repeated for three cycles, resulting in 2,160 individual sensor responses. The data analysis (performed via Python/LabVIEW) included: Standardization: Mapping values to a [-1, 1] range. Feature Extraction: Computation of Signal-to-Noise Ratio (SNR), noise standard deviation, response time, amplitude, and baseline drift. Correction: SNR values were corrected and analyzed across the 72-sample matrix to identify optimal manufacturing parameters. | en |
| dc.relation.other | NS210100083 | |
| dc.relation.other | FT200100939 | |
| usyd.faculty | SeS faculties schools::Faculty of Engineering::School of Biomedical Engineering | en |
| usyd.department | Nanotechnology Research Laboratory | en |
| workflow.metadata.only | No | en |
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