Citation

Ferdous, A.H.M. Iftekharul and Islam, Md. Safiul and Mamun, Abdullah Al and Reza, Md. Hanif and Hossen, Md. Jakir and Anower, Md. Shamim (2025) Terahertz PCF sensor for explosive detection: A machine learning approach to nitroglycerine and royal demolition analysis. Journal of Hazardous Materials Advances, 20. p. 100886. ISSN 2772-4166

Text Terahertz PCF sensor for explosive detection_ A machine learning approach to nitroglycerine and royal demolition analysis.pdf - Published Version Restricted to Repository staff only Download (8MB)

Abstract

This article presents a square hollow core Photonic Crystal Fiber (PCF) sensor developed for high relative sensitivity detection of explosives (Nitroglycerin and Royal Demolition Explosive (RDX)) in the terahertz region (1 THz to 2.8 THz). The numerical sensing capabilities are assessed utilizing the finite element technique(FEM). We have attained enhanced relative sensitivity with negligible loss for detecting Nitroglycerine and RDX through the optimization of structural factors. The maximum relative sensitivity achieved is 98.09 % for Nitroglycerine and 88.25 % for RDX at 2 THz. Additionally, we have achieved little effective material loss (EML) and an extensive effective area. The proposed sensor design is compatible with current fabrication technologies, ensuring practical feasibility. Furthermore, the prediction was conducted with the Random Forest Regressor. We have attained optimal accuracy of prediction with a unity R2 score, and this model may be utilized for predicting much behaviour, including relative sensitivity and EML for frequency

Item Type:	Article
Uncontrolled Keywords:	Nitroglycerine
Subjects:	T Technology > TP Chemical technology > TP267.5-301 Explosives and pyrotechnics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 08:56
Last Modified:	05 Oct 2025 16:38
URII:	http://shdl.mmu.edu.my/id/eprint/14622

Downloads

Edit (login required)