Evidence map›Paper›PMID 37860536›Full record

ArticleHeliyon2023

The second near-infrared fluorescence concealed imaging for identifying smuggled baggage.

Yu Cai, Jinling Wang, Changjin Ou, Yuanyuan Zhu, Jing Fang, Ying Hong

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yu CaiCenter for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Jinling WangJinling Customs, 111 Jiangjun Road, Jiangning District, Nanjing, Jiangsu, 211100, China.
Changjin OuInstitute of Advanced Materials and Flexible Electronics (IAMFE), School of Chemistry and Materials Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Yuanyuan ZhuJinling Customs, 111 Jiangjun Road, Jiangning District, Nanjing, Jiangsu, 211100, China.
Jing FangJinling Customs, 111 Jiangjun Road, Jiangning District, Nanjing, Jiangsu, 211100, China.
Ying HongJinling Customs, 111 Jiangjun Road, Jiangning District, Nanjing, Jiangsu, 211100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smuggling methods are renovating with more diversified, complicated means, thereby developing concealed and non-sensitive luggage identification technology is of great significance. Herein, for the first time, the second near-infrared (NIR-II, 1000-2500 nm) concealed imaging for identifying smuggled baggage based on organic small molecule luminescent material was studied. A small molecule luminescent material (Y6) with the excitation in invisible near-infrared (NIR) light was used to generate fluorescence-emission in NIR-II region. The traceless Y6 sprayed on the surface of criminal luggage does not emit light under visible light irradiation, but shows hidden NIR-II signs under NIR excitation that can prevent the suspects to find abnormality and lose their luggage. The Y6 organic luminescent material has invisibility, low toxicity, long-term fluorescence stability and high transparency. This NIR-II fluorescence imaging technology can be used as a new type of concealed baggage marking, which is of great significance to help the customs combat smuggling crimes.

Indexed as

Concealed imagingCustomsIdentifying smuggled baggageSecond near-infrared fluorescenceY6

Identifiers

PMID37860536
PMCPMC10582487

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.