ArticleMicrosystems & nanoengineering2024
A Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments.
Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Temperature-Modulated MEMS Gas Sensor Array Enabled by Physics-Informed Deep Learning for Thermal-Runaway Characteristic Gas Discrimination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tailoring Chemiresistive Nanomaterials Toward Integrated Circuit Compatible Fabrication of Gas Sensors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Sensitive and wafer-scale olfactory sensory neurons.Microsystems & nanoengineering · 2026Article
- Recent progress of gas sensors toward olfactory display development.Nano convergence · 2025Review
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Authors and funding
11 authors.
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Abstract
The olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, with the aim to determine the noxious gas information in challenging environments such as high humidity scenarios and partially damaged sensor units. As a result, our electronic olfactory sensing system is capable of achieving comprehensive gas recognition by qualitatively identifying 7 types of gases with an accuracy of 98.5%, reducing the number of parameters and the difficulty of calculation, and quantitatively predicting each gas of 3-5 concentration gradients with an accuracy of 93.2%; thus, these results show superiority of our system in supporting alarm systems in emergency rescue scenarios.
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Registered trials
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