Volatile organic compounds or VOCs serve an important function as indicators in a wide range of fields from health to food spoilage and environmental monitoring. In a recent study published in Sensors and Actuators B: Chemical, researchers explored a novel approach for detecting VOCs using electronic nose or e-nose technology.
The study presented a breakthrough by using a graphene-coated single sensor to achieve high selectivity and accuracy in the precise classification of VOC isomers. The researchers proved the effectiveness of the sensor for real-life applications through food quality assessments.
Study: Facile E-nose based on single antenna and graphene oxide for sensing volatile organic compound gases with ultrahigh selectivity and accuracy. Image Credit: luchschenF/Shutterstock.com
Background
Sensing of VOCs is a non-destructive method of quality assessment used in various fields including agriculture, environmental monitoring, food quality, and health diagnostics.
These gases can indicate contamination, spoilage, or ill-health, and often share the same properties while differing significantly in their toxicity and environmental impact, making it essential to differentiate them accurately.
Traditional e-nose technology uses an array of nanomaterial-coated sensors made of semiconductor metal oxide or micro-light emitting diodes to identify the specific signatures or fingerprints of different VOCs. However, e-noses containing multiple sensors are complex, expensive, and often impractical due to their high-power requirements.
Furthermore, the entire system is vulnerable to failure if a single element in the sensor malfunctions, highlighting the need for more sustainable and reliable e-nose technologies.
About the Ant-Nose Sensor
The present study introduced the single-sensor e-nose technology called Ant-nose, which used commercial graphene oxide to detect and differentiate VOCs and VOC isomers. The Ant-nose sensor integrated communication and sensing on a compact coplanar waveguide printed circuit board of 33 mm x 33 mm dimensions.
The device was fabricated by etching a copper laminate and polishing it through sanding to eliminate oxidation on the surface.
Furthermore, the researchers conducted reflection coefficient measurements that were essential for VOC sensing using a vector network analyzer across a 1–8 gigahertz, and the radiation patterns were recorded.
A sensing layer composed of graphene oxide and a fluoropolymer-copolymer called Nafion was used for VOC sensitivity, and coated onto the Ant-nose sensor after sonication with ethanol. After the ethanol solvent evaporated, a thin membrane formed on the sensor.
A graphite-Nafion solution was also prepared and drop-coated onto the sensor after sonication for alternative test materials.
The VOC sensing set-up consisted of a 10-liter airtight container with the Ant-nose sensor connected to the vector network analyzer using a coaxial cable and a fan connected to ensure uniform gas distribution.
Precision syringes were used to inject VOC samples to minimize leakage and the volumes of the injected liquids were pre-calculated to ensure controlled concentrations.
The vector network analyzer recorded the Ant-nose sensor’s reflection coefficient data over 1–8 gigahertz every second and captured components of the signal for high-dimensional data analysis. The researchers ensured that the chamber was ventilated and refreshed after each measurement and before each test.
A range of food quality assessment experiments were conducted to test the ability of Ant-nose to detect VOCs associated with food freshness or spoilage. This included the detection of ethylene emitted during fruit ripening and distinct VOC signatures of bacterial growth emitted during meat spoilage.
Major Findings
The study showed that the novel e-nose sensor known as Ant-nose used a multi-resonant, single-microwave antenna to successfully differentiate between various VOCs and detect specific mixtures of VOCs.
The Ant-nose sensor measured six different VOCs at concentrations ranging from 200 parts per million (ppm) to 1000 ppm, and produced unique response fingerprints for each VOC, which distinguished for concentration and type.
The sensor also used principal component analysis and an eXtreme Gradient Boosting (XGBoost) model to classify four non-isomer VOCs, namely methanol, ethanol, propanol, and butanol with 100% accuracy.
The sensor also achieved 96.7% accuracy for six VOCs that included the isomers for isopropyl alcohol and 2-butanol. Furthermore, the Ant-nose sensor accurately identified and predicted the concentrations of binary VOC mixtures consisting of 1/2-propanol and 1/2-butanol.
When the accuracy of Ant-nose was tested for real-life applications using food samples, the sensor efficiently distinguished between aged, fresh, and damaged samples, confirming its utility in food quality assessment and monitoring.
However, the results indicated that higher humidity levels had a minor impact on the measurement precision of the instrument, but the sensor maintained reliable VOC detection through moderate humidity.
Conclusions
Overall, the findings indicated that the Ant-nose sensor offered reliable VOC detection and classification, even under variable humidity and for complex mixtures of VOCs.
The single-sensor design, stable communication, and accurate and sensitive concentration prediction abilities make it a promising, low-cost e-nose technology for real-world applications in food safety and environmental monitoring.
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