Evidence map›Paper›PMID 37177087›Full record

ArticleNanomaterials (Basel, Switzerland)2023

Polarization-Dependent Metasurface Enables Near-Infrared Dual-Modal Single-Pixel Sensing.

Rong Yan, Wenli Wang, Yao Hu, Qun Hao, Liheng Bian

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 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

5 authors.

Rong YanMIIT Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology, Beijing 100081, China.
Wenli WangBeijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Yao HuBeijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Qun HaoBeijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Liheng BianMIIT Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology, Beijing 100081, China.

Funding

National Natural Science Foundation of China 61971045, 62131003, 61991451, 62275022
6 · The paper itself

Abstract

Infrared single-pixel sensing with the two most representative modes, bright-field imaging and edge-enhanced imaging, has great application potential in biomedical diagnosis and defect inspection. Building a multifunctional and miniature optical computing device for infrared single-pixel sensing is extremely intriguing. Here, we propose and validate a dual-modal device based on a well-designed metasurface, which enables near-infrared bright-field and edge-enhanced single-pixel imaging. By changing the polarization of the incident beam, these two different modes can be switched. Simulations validate that our device can achieve high-fidelity dual-modal single-pixel sensing at 0.9 μm with certain noise robustness. We also investigate the generalization of our metasurface-based device and validate that different illumination patterns are applied to our device. Moreover, these output images by our device can be efficiently utilized for biomedical image segmentation. We envision this novel device may open a vista in dual-modal infrared single-pixel sensing.

Indexed as

biomedical image segmentationdual modesinfrared single-pixelmetasurfaceoptical computing

Identifiers

PMID37177087
PMCPMC10180816

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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.