# README: Dataset for Optimization of 72 ZnO Nanostructured Sensors for E-Nose Applications

AUTHORS: Fabian Steven Garay-Rairan, Qi Wang, Qian Jing, Artem Lensky, Krishan Murugappan, Hanna Suominen, Antonio Tricoli
SUPERVISOR: Antonio Tricoli
AFFILIATION: School of Biomedical Engineering, The University of Sydney
PROJECT: Electronic Nose (E-Nose) for Early Cancer Detection
DATE: April 2026

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1. PROJECT DESCRIPTION
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This dataset contains the complete experimental performance and thermal 
characterization of 72 Zinc Oxide (ZnO) nanostructured sensors. These sensors 
were fabricated via electrodeposition to optimize their sensitivity and 
Signal-to-Noise Ratio (SNR) for the detection of Acetone at low concentrations 
(0.1 - 1 ppm), which is a key biomarker for cancer and diabetes diagnostics.

The fabrication parameters varied across the 72 samples include:
- Molarity (ZnCl2): 0.01M, 0.05M, 0.1M, and 0.2M.
- Current Density: -100µA to -3mA.
- Deposition Time: 10s, 30s, and 60s.

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2. FILE INVENTORY AND STRUCTURE
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A. RAW SENSING DATA
- Sensing_Gas_72_sensors.csv: 
  Dynamic resistance response of the 72 sensors exposed to three cycles 
  (loops) of acetone concentrations.

B. THERMAL CHARACTERIZATION DATA
- Thermal_Characterization_Part1_Sensors_S01_to_S36.csv: 
  Resistance-temperature profiles for the first half of the sensors (S01-S36).
- Thermal_Characterization_Part2_Sensors_S37_to_S72.csv: 
  Resistance-temperature profiles for the second half of the sensors (S37-S72).

C. ANALYTICAL AND STATISTICAL DATA
- 72_sensors_mean_snr.csv: 
  Consolidated file with mean performance metrics (SNR, Base R, Delta R) 
  per sensor and per cycle.
- snr_corrected_72_sensors.csv: 
  Advanced analytical data including noise scaling (sigma) and corrected 
  SNR values used for final optimization models.

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3. DATA DICTIONARY (HEADERS)
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Header              | Description                                | Units
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sensor_id           | Unique ID for each sensor (S01 - S72)      | -
sol_con             | Concentration of the ZnCl2 solution        | M
current             | Applied electrodeposition current          | µA
deposition_time     | Duration of the nanostructure growth       | s
loop_number         | Gas exposure cycle (1, 2, 3)               | -
cycle_no            | Index of the gas exposure cycle            | -
time_s              | Relative timestamp of the measurement      | s
resistance_ohm      | Measured electrical resistance             | Ω
o2_l_min            | Oxygen flow rate                           | L/min
n2_l_min            | Nitrogen flow rate                         | L/min
acetone_ppm         | Concentration of Acetone gas               | ppm
temp_c              | Operating temperature of the sensor        | °C
base_r              | Baseline resistance in synthetic air       | Ω
response_r          | Resistance peak during gas exposure        | Ω
delta_r             | Absolute resistance change (|Base-Resp|)   | Ω
sigma_base          | Standard deviation of the baseline noise   | -
snr_lin             | Linear Signal-to-Noise Ratio               | -

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4. METHODOLOGY NOTES
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- Synthesis: Electrochemical deposition (Chronoamperometry/Chronopotentiometry) 
  followed by calcination at 400°C       for 2 hours.
- Gas Exposure: Each "Loop" consists of a baseline period (Synthetic Air), 
  an exposure period (Acetone), and a recovery period.
- Data Sampling: High-frequency sampling was used to capture the fast 
  kinetics of the ZnO nanostructures.
- Thermal Baseline: Measurements were performed to ensure stability and 
  understand the semiconducting behavior under specific heating profiles.
- Equipment: Data acquired via a Keithley 2700 digital multimeter.
- Target Gas: Acetone (0.1 ppm - 1 ppm range).
- Operating Temperature: 300\'b0C (stabilized).

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5. FUNDING AND ACKNOWLEDGMENTS
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This work was conducted at the University of Sydney and supported by:
- National Intelligence and Security Discovery Research Grants (NISDRG) NS210100083.
- The Australian Research Council Future Fellowship FT200100939.
- The Australian Research Council Discovery Project DP190101864.
- The Australian Renewable Energy Agency – Research and Development Program, 
  Round 4: Renewable Hydrogen for Export AS008.
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6. TERMS OF USE
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This dataset is provided under the CC BY-NC 4.0 license (Attribution-
NonCommercial 4.0 International). Please cite this dataset using the DOI 
provided in the Sydney eScholarship Repository record.