Visualization of Spatial Distribution of on Ground Gas Sources with LSPR based 1D/2D High Speed Gas Sensor Robot System

Yang, Zhongyuan; Soeda, Takaaki; Sassa, Fumihiro; Hayashi, Kenshi

doi:10.1109/icsens.2018.8589909

Cited by 4 publications

(1 citation statement)

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“…We are presently conducting research to visualize the mixture of volatile substances on the ground using, for example, the one-dimensional robot system mentioned above and obtaining gas trace information from 2D information. We have successfully visualized ethanol gas flow (using a transmissive type of detector) [41] and odor traces (using a reflective type of detector) [42] using a 2D-LSPR substrate on a fixed experimental platform. However, the transmissive type of detector cannot detect odor traces on the ground, and the reflective type of detector captures the camera lens in the image and the strong reflection of its substrate interferes with subsequent image processing.…”

Section: Introductionmentioning

confidence: 99%

An Odor Trace Visualization System Using a Two-Dimensional Backside Scattering Localized Surface Plasmon Resonance Gas Sensor

Yang,

Sassa,

Hayashi

2023

Sensors

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Odor information fills every corner of our lives yet obtaining its spatiotemporal distribution is a difficult challenge. Localized surface plasmon resonance has shown good sensitivity and a high response/recovery speed in odor sensing and converts chemical information such as odor information into optical information, which can be captured by charge-coupled device cameras. This suggests that the utilization of localized surface plasmon resonance has great potential in two-dimensional odor trace visualization. In this study, we developed a two-dimensional imaging system based on backside scattering from a localized surface plasmon resonance substrate to visualize odor traces, providing an intuitive representation of the spatiotemporal distribution of odor, and evaluated the performance of the system. In comparative experiments, we observed distinct differences between odor traces and disturbances caused by environmental factors in differential images. In addition, we noted changes in intensity at positions corresponding to the odor traces. Furthermore, for indoor experiments, we developed a method of finding the optimal capture time by comparing changes in differential images relative to the shape of the original odor trace. This method is expected to assist in the collection of spatial information of unknown odor traces in future research.

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Section: Introductionmentioning

confidence: 99%