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VOC Sensor Uses Micro-Flowers to Track Rice Quality

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What rice smells like can reveal its quality - and a new sensor array shows how those aromas evolve over time.

Caucasian Technician Uses Advanced Gas Detecting Device to Inspect Natural Gas Systems at Facility Image Credit: Virrage Images/Shutterstock.com

Researchers have developed a compact, laboratory-scale gas sensor array capable of distinguishing subtle changes in cooked rice quality by tracking volatile organic compounds (VOCs) released during storage.

Reported in the Journal of Advanced Ceramics, the system combines copper oxide-decorated bismuth subcarbonate micro-flowers with pattern-recognition analytics to deliver highly selective, room-temperature sensing.

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Aroma is one of the earliest indicators of food quality; yet, monitoring it in real-time remains an issue. Cooked rice releases large-molecule VOCs with low volatility, which overlap strongly with background compounds and are difficult to separate selectively. 

Among these, nonanal, benzaldehyde, and 1-octen-3-ol are widely recognized key aroma markers.

Their concentrations evolve during storage and correlate closely with perceived freshness, making them useful indicators of rice quality - if they can be reliably detected.

Engineering Selectivity at the Material Level

To address this challenge, the researchers designed a series of CuO-decorated Bi2O2CO3 materials, referred to as Cux-BC (x = 10, 20, 30, and 40), synthesized via a solvothermal route followed by mild calcination.

Structural and chemical analysis using XRD, SEM, TEM, XPS, BET, and electron paramagnetic resonance (EPR) confirmed the formation of p–n heterostructures with controllable copper oxide loading and abundant surface oxygen vacancies.

CuO decoration reshaped the Bi2O2CO3 into semi-hollow micro-flower architectures with greatly increased surface area and interconnected diffusion pathways. These features enhanced gas access to active sites while improving charge transport across the CuO/Bi2O2CO3 interface.

Room-Temperature Sensing With an Optimal Balance

Gas-sensing tests at room temperature (25 ± 2 °C) showed that all Cux-BC sensors responded strongly to nonanal, benzaldehyde, and 1-octen-3-ol, with linear response trends across relevant concentration ranges. Among them, Cu20-BC consistently delivered the highest response.

The study shows that sensor performance does not improve indefinitely with increasing CuO content. Instead, moderate CuO loading optimizes electrical conductivity, interface contact, and depletion-layer modulation at the p-n junction.

Excessive CuO introduces too many junctions, disrupting charge transport and reducing sensitivity.

The enhanced performance of Cu20-BC was further linked to a higher concentration of oxygen vacancies and a narrowed bandgap, both of which lower the energy barrier for charge carrier excitation and amplify resistance changes upon gas exposure.

Single Sensors to Pattern Recognition

Rather than relying on a single sensor, the team integrated four Cux-BC sensors into a compact, laboratory-based array using a printed circuit board, microcontroller unit, and voltage-to-resistance conversion circuitry. This enabled real-time signal acquisition across multiple sensing channels.

When exposed to individual VOCs, the combined sensor responses formed distinct patterns that were clearly separated using principal component analysis (PCA), confirming high selectivity among nonanal, benzaldehyde, and 1-octen-3-ol.

Importantly, the system was designed to recognize response patterns rather than measure absolute concentrations of single compounds.

Tracking Rice Quality Over Time

To evaluate practical relevance, the array was used to analyze VOCs emitted by cooked rice prepared from grains stored for up to six weeks. Headspace GC-MS confirmed that freshly stored rice primarily released nonanal and benzaldehyde, while rice made from longer-stored grains also contained 1-octen-3-ol.

Using PCA and linear discriminant analysis (LDA), the sensor array successfully grouped samples into three distinct quality categories: fresh (up to two weeks), mild (three to four weeks), and moderate (five to six weeks).

The results demonstrate the system’s ability to resolve gradual aroma evolution in complex VOC mixtures, rather than simply detecting the presence or absence of individual gases.

Molecular dynamics simulations were used to probe how VOC molecules and oxygen interact with the material surfaces.

While the simulations did not directly predict sensor output, they supported the experimental findings by revealing how adsorption strength, diffusion behavior, and interface effects influence charge transfer and modulation of the depletion layer in the CuO/Bi2O2CO3 heterostructure.

Tracking VOCs in the Future

The researchers emphasize that the system remains at a laboratory scale, but its performance highlights the potential of interface-engineered metal oxide heterostructures for intelligent food quality monitoring. 

Beyond cooked rice, the approach could be extended to grain storage, food freshness assessment, and other agricultural applications where aldehydes and alcohols serve as key indicators.

Future work will need to address long-term stability, humidity tolerance, and performance under variable environmental conditions before these sensor arrays can be deployed in the field.

Journal Reference

Zichen Z., et al. (2025). CuO-decorated bismuth subcarbonate p-n heterostructured micro-flowers for high-selectivity VOC gas sensor arrays and cooked rice quality assessment, Journal of Advanced Ceramics, 15: 9221233. DOI: 10.26599/JAC.2025.9221233.

Dr. Noopur Jain

Written by

Dr. Noopur Jain

Dr. Noopur Jain is an accomplished Scientific Writer based in the city of New Delhi, India. With a Ph.D. in Materials Science, she brings a depth of knowledge and experience in electron microscopy, catalysis, and soft materials. Her scientific publishing record is a testament to her dedication and expertise in the field. Additionally, she has hands-on experience in the field of chemical formulations, microscopy technique development and statistical analysis.    

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