Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Xinghe Jiang; Yuhang Li; Yuzhu Li; Che-Yung Shen; Aydogan Ozcan; Mona Jarrahi

doi:10.1038/s41377-026-02190-z

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Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Original Articles

- Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning
- Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning
- Light: Science & Applications 2026年15卷第3期页码：903-915
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
  3.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
  Mona Jarrahi (mjarrahi@ucla.edu)
- Funds：
- DOI：10.1038/s41377-026-02190-z
  中图分类号：
- 收稿：2025-05-27，
  
  修回：2025-12-26，
  
  录用：2026-01-09，
  
  网络首发：2026-01-22，
  
  纸质出版：2026-03-31
- Accepted：
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Xinghe Jiang, Yuhang Li, Yuzhu Li, 等. Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning[J]. Light: Science & Applications, 2026,15(3):903-915.

Jiang, X. H. et al. Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning. Light: Science & Applications, 15, 903-915 (2026).
Xinghe Jiang, Yuhang Li, Yuzhu Li, 等. Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning[J]. Light: Science & Applications, 2026,15(3):903-915. DOI： 10.1038/s41377-026-02190-z.

Jiang, X. H. et al. Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning. Light: Science & Applications, 15, 903-915 (2026). DOI： 10.1038/s41377-026-02190-z.

摘要

Abstract

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range

conventional terahertz-based detection methods often struggle in real-world environments

where variations in sample geometry

thickness

and packaging can lead to inconsistent spectral responses. In this study

we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays

our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz

supporting practical

stand-off operation. By analyzing individual time-domain pulses with deep neural networks

the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably

the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings

demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

关键词

Keywords

references

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