Evidence map›Paper›PMID 41476044›Full record

ArticleLight, science & applications2026

Bioinspired phototransistor with tunable sensitivity for low-contrast target detection.

Ruyue Han, Dayu Jia, Bo Li, Shun Feng, Guoteng Zhang, Yun Sun, Zheng Han, Chi Liu, Hui-Ming Cheng, Dong-Ming Sun

Abstract read
In one paragraph

Article in Light, science & applications, 2026. 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

10 authors.

Ruyue Han *Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Dayu Jia *School of Information Institution, Liaoning University, Shenyang, China.
Bo Li *Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Shun Feng *Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China. sfeng@imr.ac.cn.
Guoteng ZhangShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Yun SunShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Zheng HanState Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, China.ORCID http://orcid.org/0000-0001-5721-6206
Chi LiuShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China. chiliu@imr.ac.cn.
Hui-Ming ChengShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Dong-Ming SunShenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China. dmsun@imr.ac.cn.ORCID http://orcid.org/0000-0003-1552-7940

Funding

National Natural Science Foundation of China (National Science Foundation of China) E311L5191R
6 · The paper itself

Abstract

Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems. Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity, which restricts their ability to capture low-contrast features and makes them susceptible to noise. Inspired by the adaptive mechanisms of the human visual system, we present a molybdenum disulfide (MoS

Identifiers

PMID41476044
PMCPMC12756283

What OpenQuestion holds

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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.