Prof. Jiangong Cheng Profile

Prof. Jiangong Cheng

at Univ. of Chinese Academy of Sciences

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Publications (2)

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Proceedings Article | 27 November 2023 Paper

On-chip fluorescence array sensor for chemical gas sensing

Bo Wang, He Li, Chang Chen, Jiangong Cheng

Proceedings Volume 12771, 127710P (2023) https://doi.org/10.1117/12.2687339

KEYWORDS: Fluorescence, Waveguides, Biological and chemical sensing, Gases, Quenching, Detector arrays, Silicon photonics, Sensors, Molecules

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Silicon photonics technology provides an attractive solution for compact, low-cost multi-array detection for chemical sensing. An on-chip multi-channel fluorescence gas sensor has been developed on an integrated photonic platform. By the simultaneous excitation and collection with the same waveguide, online detection of illicit drug simulants Methylphenethylamine (MPEA), nerve agent simulants Diethyl Chlorophosphate (DCP), and carcinogenic gas Aniline (AN) have been achieved by one source and detector on the same chip. Thanks to the compatibility with the complementary metal–oxide semiconductor (CMOS) fabrication processes, this technology paves the way for future development of wearable fluorescence-based gas sensors with high sensitivity, low cost, and compact size.

Proceedings Article | 4 December 2020 Paper

Intelligent sensor array based on machine learning

Jinming Chen, Qingguo He, Jiangong Cheng

Proceedings Volume 11617, 116171W (2020) https://doi.org/10.1117/12.2585241

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With the development of Internet and IoT (Internet of Things), the specificity and sensitivity of traditional sensors cannot meet the multi-task requirements in complex environments. The sensor array can solve the limitation that a single sensor can only detect one target to a certain extent. However, the sensor array still cannot solve the recognition of cross-sensitive similar target. This work proposes a solution based on machine learning that can greatly improve the specificity of traditional sensors. It can potentially be used in non-contact breathing diagnosis. Firstly, carbon nanotube materials were chose as electrical carriers, taking advantage of its high specific surface area and high electron mobility characteristics. Secondly, a series of organic molecules are designed to modify carbon nanotubes. The ability of organic materials to capture amines enables response output to amine gases. In order to meet the needs of exhaled breath diagnosis, we need to further distinguish amines with high similarity (ammonia, n-propylamine, diethylamine, triethylamine). Therefore, we have adopted the method of machine learning. The data of resistance with time collected by the sensor array are input as eigenvalues into a pre-designed neural network model for training. The trained model can achieve over 80% recognition accuracy in testing data. The purpose of this work is to propose a new and better solution to the detection of targets with cross-sensitivity, thereby improving the selectivity of the sensor. It provides the possibility to use it under different requirements to achieve intelligent detection.

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