Remote Detection of Uranium and Plutonium Using Wireless LIBS and AI

Akmal Khan, Muhammad Haroon and Kamil Jalal

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 4, 982-995

Abstract: Nuclear threat detection remains a vital global security concern, particularly in environments requiring real-time, non-contact monitoring. This study presents a remote detection system that combines laser-induced breakdown spectroscopy (LIBS), machine learning, and wireless signal optimization to detect uranium and plutonium from distances of 30 to 70 meters. The DRUP-LIBS system utilizes both multi- and single-wavelength laser pulses to generate actinide-specific plasma emissions, which are analyzed using artificial intelligence with 100% classification accuracy. Wireless modules equipped with directional antennas maintained signal quality over long distances. Plasma temperature and electron density were measured using Boltzmann plots and Stark broadening, confirming plasma consistency. Results show that the system reliably distinguishes between uranium and plutonium, with plutonium exhibiting stronger emissions and higher electron density. These findings support DRUP-LIBS as a secure, non-intrusive tool for real-time radioactive threat detection in open environments.

Keywords: Remote Nuclear Detection; Laser-Induced Breakdown Spectroscopy (LIBS); Uranium and Plutonium Identification; Artificial Intelligence (AI) Classification; Plasma Diagnostics; Wireless Signal Optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST251377 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST251377/IJSRST251377 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i4:id:1101

DOI: 10.32628/IJSRST251377

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().