Evidence map›Paper›PMID 38067898›Full record

ArticleSensors (Basel, Switzerland)2023

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

Zhongyuan Yang, Fumihiro Sassa, Kenshi Hayashi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhongyuan YangGraduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.ORCID 0000-0003-4760-1965
Fumihiro SassaGraduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
Kenshi HayashiGraduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.

Funding

Japan Society for the Promotion of Science JP22H04952 JP19H05680
6 · The paper itself

Abstract

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.

Indexed as

chemical imaginggas sensorlocalized surface plasmon resonanceodor trace visualizationspatial distribution

Identifiers

PMID38067898
PMCPMC10708793

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.