Evidence map›Paper›PMID 36903792›Full record

ArticleNanomaterials (Basel, Switzerland)2023

All-Silicon Photoelectric Biosensor on Chip Based on Silicon Nitride Waveguide with Low Loss.

Yu Tang, Qian Luo, Yuxing Chen, Kaikai Xu

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Article in Nanomaterials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. The Effect of MoSMicromachines · 2025
    Article
  2. 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

4 authors.

Yu TangState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.
Qian LuoState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yuxing ChenState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.
Kaikai XuState Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.

Funding

the National Key Research and Development Program of China under Contract 2018YFE0181500the National Natural science Foundation of China under Grant 62174018the Sichuan Province's Science Fund for Distinguished Young Scholars under Contract 2020JDJQ0022
6 · The paper itself

Abstract

Compared to the widely used compound semiconductor photoelectric sensors, all-silicon photoelectric sensors have the advantage of easy mass production because they are compatible with the complementary metal-oxide-semiconductor (CMOS) fabrication technique. In this paper, we propose an all-silicon photoelectric biosensor with a simple process and that is integrated, miniature, and with low loss. This biosensor is based on monolithic integration technology, and its light source is a PN junction cascaded polysilicon nanostructure. The detection device utilizes a simple refractive index sensing method. According to our simulation, when the refractive index of the detected material is more than 1.52, evanescent wave intensity decreases with the growth of the refractive index. Thus, refractive index sensing can be achieved. Moreover, it was also shown that, compared to a slab waveguide, the embedded waveguide designed in this paper has a lower loss. With these features, our all-silicon photoelectric biosensor (ASPB) demonstrates its potential in the application of handheld biosensors.

Indexed as

evanescent wavemonolithic integratedPN junction cascade light sourcesilicon nitride waveguide

Identifiers

PMID36903792
PMCPMC10005702

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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.