ArticleMicrosystems & nanoengineering2020
An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol.
Article in Microsystems & nanoengineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 158 citations in OpenAlex.
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- Rapid on-site methanol screening in distilled spirits via BME688 MOX sensor array and machine learning.Scientific reports · 2026Article
- Multifunctional, energy-autonomous textile sensors enabled by spray-coated two-dimensional heterostructures.Npj flexible electronics · 2026Article
- Automated Assessment of Green Infrastructure Using E-nose, Integrated Visible-Thermal Cameras and Computer Vision Algorithms.Sensors (Basel, Switzerland) · 2025Article
- AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems.Microsystems & nanoengineering · 2025Review
- Optically Programmable Smart WSeACS applied materials & interfaces · 2025Article
- The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition.Nano-micro letters · 2025Review
- Synthesizing Conductive Metal-Organic Framework Nanosheets for High-Performing Chemiresistive Sensors.ACS applied materials & interfaces · 2025Article
- Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.ACS sensors · 2025Article
- Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments.Microsystems & nanoengineering · 2024Article
- Optimizing Low-Cost Gas Analysis with a 3D Printed Column and MiCS-6814 Sensor for Volatile Compound Detection.Sensors (Basel, Switzerland) · 2024Article
- Ultra-sensitive nitrate-ion detection via transconductance-enhanced graphene ion-sensitive field-effect transistors.Microsystems & nanoengineering · 2024Article
- Rationalizing Graphene-ZnO Composites for Gas Sensing via Functionalization with Amines.Nanomaterials (Basel, Switzerland) · 2024Article
- Enantioselective Detection of Gaseous Odorants with Peptide-Graphene Sensors Operating in Humid Environments.ACS applied materials & interfaces · 2024Article
- Calibration of a Low-Cost Methane Sensor Using Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing.Sensors (Basel, Switzerland) · 2024Article
- Smart mid-infrared metasurface microspectrometer gas sensing system.Microsystems & nanoengineering · 2024Article
- Detection of Water Contaminants by Organic Transistors as Gas Sensors in a Bottom-Gate/Bottom-Contact Cross-Linked Structure.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The poor gas selectivity problem has been a long-standing issue for miniaturized chemical-resistor gas sensors. The electronic nose (e-nose) was proposed in the 1980s to tackle the selectivity issue, but it required top-down chemical functionalization processes to deposit multiple functional materials. Here, we report a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to realize gas selectivity under particular conditions by combining the unique properties of the GFET and e-nose concept. Instead of using multiple functional materials, the gas-sensing conductivity profiles of a GFET are recorded and decoupled into four distinctive physical properties and projected onto a feature space as 4D output vectors and classified to differentiated target gases by using machine-learning analyses. Our single-GFET approach coupled with trained pattern recognition algorithms was able to classify water, methanol, and ethanol vapors with high accuracy quantitatively when they were tested individually. Furthermore, the gas-sensing patterns of methanol were qualitatively distinguished from those of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas from the realistic scenario of an ambient environment with background humidity. As such, this work offers a new class of gas-sensing schemes using a single GFET without multiple functional materials toward miniaturized e-noses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.