Evidence map›Paper›PMID 41481713›Full record

ArticleScience advances2026

Multienergy x-ray imaging enabled by unipolar perovskite detector for intelligent substance identification.

Yuwei Li, Xiangyu Ou, Shilin Liu, Jingda Zhao, Lu Xue, Chengjun Liu, Bingjian Zhu, Su Yan, Shuo Wang, Ziyu Wei and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2026. 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. Review
  2. Review
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

14 authors.

Yuwei LiSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.ORCID 0009-0003-2436-3025
Xiangyu OuDepartment of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden.ORCID 0000-0002-4093-6757
Shilin LiuSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.ORCID 0000-0003-3383-0425
Jingda ZhaoSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Lu XueSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Chengjun LiuSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Bingjian ZhuState Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education, Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.ORCID 0009-0006-7619-6275
Su YanSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Shuo WangSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Ziyu WeiSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Sajid HussainSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Wei LeiSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.ORCID 0000-0001-5532-7252
Feng GaoDepartment of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden.ORCID 0000-0002-2582-1740
Xiaobao XuSchool of Electronic Science and Engineering, Southeast University, Nanjing, Jiangsu, China.ORCID 0009-0009-7738-5336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A challenge with state-of-the-art projection x-ray imaging technologies is their limited ability to identify unknown substances. Here, we develop an intelligent multienergy x-ray imaging technique capable of precisely distinguishing different substances and labeling them with diverse colors. Our design uses a series of x-ray attenuation coefficient ratios under different x-ray energies as substance-specific markers. For this purpose, unipolar perovskite x-ray detectors are carefully engineered to resolve the x-ray energies into seven channels using a customized algorithm. Combining machine learning and a comprehensive x-ray attenuation ratio database of common materials enables accurate recognition of low-density biological tissues composed of light elements with similar atomic numbers. By transforming the intensity scale in conventional x-ray images into an attenuation coefficient ratio, our work presents a proof of concept for color-coded x-ray imaging, highlighting its potential for applications in energy-dispersive computed tomography, targeted drug delivery, quantum physics, and universe exploration.

Identifiers

PMID41481713
PMCPMC12758516

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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.