Terahertz PCF sensor for explosive detection: A machine learning approach to nitroglycerine and royal demolition analysis

IF 7.7 Q2 ENGINEERING, ENVIRONMENTAL
A.H.M. Iftekharul Ferdous , Md. Safiul Islam , Abdullah Al Mamun , Md. Hanif Reza , Md. Jakir Hossen , Md. Shamim Anower
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引用次数: 0

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.
用于爆炸探测的太赫兹PCF传感器:硝化甘油和皇家爆破分析的机器学习方法
本文介绍了一种用于高相对灵敏度探测太赫兹(1太赫兹~ 2.8太赫兹)炸药(硝化甘油和皇家爆破炸药)的方形空心芯光子晶体光纤(PCF)传感器。数值传感能力的评估利用有限元技术(FEM)。通过对结构因素的优化,在检测硝酸甘油和RDX的相对灵敏度提高的同时,损失可以忽略不计。在2太赫兹下,硝酸甘油和RDX的最大相对灵敏度分别为98.09%和88.25%。此外,我们实现了很少的有效材料损失(EML)和广泛的有效面积。所提出的传感器设计与当前的制造技术兼容,确保了实际的可行性。采用随机森林回归模型进行预测。我们获得了统一R2评分的最佳预测精度,该模型可用于预测许多行为,包括相对灵敏度和频率的EML。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of hazardous materials advances
Journal of hazardous materials advances Environmental Engineering
CiteScore
4.80
自引率
0.00%
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审稿时长
50 days
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