Evidence map›Paper›PMID 41234772›Full record

ArticleiScience2025

AI enhanced strong-field terahertz spectral detection and imaging.

Mingxuan Zhang, Shaojie Liu, Jiahui Li, Zehao Yang, Zejun Ren, Deyin Kong, Yida Wu, Lin Du, Peidi Yang, Xinxiong Chen and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. 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

13 authors.

Mingxuan ZhangHangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China.
Shaojie LiuDepartment of Physics, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China.
Jiahui LiUniversity of Chinese Academy of Sciences, Institute of Physics, Beijing National Laboratory for Condensed Matter Physics, Beijing 100190, China.
Zehao YangSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Zejun RenSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Deyin KongSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Yida WuSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Lin DuSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Peidi YangHangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China.
Xinxiong ChenSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Songbo CuiSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Caihua WanUniversity of Chinese Academy of Sciences, Institute of Physics, Beijing National Laboratory for Condensed Matter Physics, Beijing 100190, China.
Xiaojun WuHangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Terahertz (THz) waves demonstrate advantages including broad absolute bandwidth, low photon energy, and superior penetration capability, showing significant potential in nondestructive testing and spectroscopic imaging applications. However, current techniques face challenges including unknown material refractive indices, pulse signal aliasing, and time-consuming detection processes, which hinder the advancement and commercialization of THz-based nondestructive testing. This work presents a nondestructive testing system employing an enhanced 4-inch spintronic strong-field THz emitter, integrated with neural-network-assisted thickness prediction and contour detection. The system achieves micrometer-scale accuracy for ultrathin materials and rapid defect identification in large-scale samples. Experimental results demonstrate 99.8% measurement accuracy within ±8 μm for micrometer-scale samples, with submillimeter-level depth resolution for defect characterization and contour imaging. This technology demonstrates scalability and high flexibility, laying the foundation for thickness measurement of multilayer composite coatings and internal defect detection in complex materials, showing broad prospects in surface coating processing, component maintenance, and cultural heritage preservation.

Indexed as

Applied sciencesPhysics

Identifiers

PMID41234772
PMCPMC12604959

What OpenQuestion holds

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Registered trials

None linked

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.