Evidence map›Paper›PMID 42901896›Full record

ArticleNature communications2026

Single-fiber hyperspectral imaging via nanophotonic disordered dispersion.

Yuchen Ma, Jiachen Wu, Chang-Yin Ji, Liangcai Cao, Guangwei Hu

Abstract read
In one paragraph

Article in Nature communications, 2026. 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

5 authors.

Yuchen MaState Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China.
Jiachen WuState Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China.
Chang-Yin JiSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Liangcai CaoState Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, China. clc@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-8099-2948
Guangwei HuSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore. guangwei.hu@ntu.edu.sg.ORCID http://orcid.org/0000-0002-3023-9632

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62235009
6 · The paper itself

Abstract

Optical fibers are widely used to transmit optical signals for communications and sensing. Extending this capability to hyperspectral imaging is attractive for compact imaging systems, but is fundamentally constrained by limited channel capacity and transmission distortion of fibers. Here, we demonstrate single-fiber hyperspectral imaging via nanophotonic disordered dispersion. A disordered-dispersion encoder at the distal fiber end can map high-dimensional spectral-angular information into a one-dimensional spectral measurement, which is then computationally decoded using sparsity-constrained reconstruction. Our disordered dispersion is achieved using nonlocal nanophotonic structures that support high mode density and multimode coupling, thereby enabling low-correlation compressive encoding. The system features angular reconstruction over a field of view of ±60°, spectral detection from 400 to 700 nm, and robustness against fiber distortion. This work circumvents the need for real-space pixelated encoders or detectors used in traditional hyperspectral cameras, offering a route toward ultra-compact high-dimensional imagers compatible with flexible single-fiber probes.

Identifiers

PMID42901896
PMCPMC13683409

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.