Evidence map›Paper›PMID 42288515›Full record

ArticleNature communications2026

Mid-infrared snapshot spectral imaging via nonlinear radial dispersion.

Jianan Fang, Kun Huang, Ruiyang Qin, Jixi Zhang, Heping Zeng

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.

Jianan FangState Key Laboratory of Precision Spectroscopy, and Hainan Institute, East China Normal University, Shanghai, China.
Kun HuangState Key Laboratory of Precision Spectroscopy, and Hainan Institute, East China Normal University, Shanghai, China. khuang@lps.ecnu.edu.cn.ORCID http://orcid.org/0000-0002-1899-7924
Ruiyang QinState Key Laboratory of Precision Spectroscopy, and Hainan Institute, East China Normal University, Shanghai, China.
Jixi ZhangState Key Laboratory of Precision Spectroscopy, and Hainan Institute, East China Normal University, Shanghai, China.
Heping ZengState Key Laboratory of Precision Spectroscopy, and Hainan Institute, East China Normal University, Shanghai, China. hpzeng@phy.ecnu.edu.cn.ORCID http://orcid.org/0000-0002-2357-4440

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62505088, 62235019, 125B2088
6 · The paper itself

Abstract

Mid-infrared spectral imaging provides chemically specific contrast through molecular vibrational fingerprints, yet snapshot acquisition remains severely limited by the lack of high-sensitivity detectors and efficient spectral encoding mechanisms. Here we introduce snapshot MIR spectral imaging based on intrinsic nonlinear radial dispersion, in which wavelength-dependent phase matching simultaneously enables frequency upconversion and spectral multiplexing. Different spectral components are mapped to distinct output angles within a 4f imaging architecture, enabling single-shot spectral encoding without external dispersive elements. In combination with speckle illumination encoding, spectral information is compressed and recovered without additional coding components. Leveraging nonlinear upconversion to the visible, the approach achieves room-temperature MIR spectral imaging with sensitivity approaching 1 photon/pixel/pulse across a broad spectral range from 2.5 to 4.0 μm. This work transforms spectral encoding from an external optical function into an inherent property of the nonlinear imaging process, providing a general route to high-sensitivity snapshot MIR spectral imaging.

Identifiers

PMID42288515
PMCPMC13408172

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.