Computational Optimization of Nanomaterial Sensors for Explosive Detection: A Multi-Parameter Study of Selectivity and Environmental Stability

Abstract

Trace explosive residue detection is still an important and pressing issue in security scanning, forensic science, and anti-terrorism missions. In this paper, a thorough comparative simulation analysis of four nanomaterial-based sensor technologies, namely Single-Walled Carbon Nanotubes (SWCNT), Multi-Walled Carbon Nanotubes (MWCNT), reduced Graphene Oxide (rGO), and Zinc Oxide Nanowires (ZnO NW), for the detection of three major explosive materials: 2,4,6-Trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and Triacetone Triperoxide (TATP), is conducted. Based on simulation parameters obtained from 47 peer-reviewed articles (2020-2024), the performance of the sensors was assessed for 108 experimental settings, including different temperatures (10°C, 25°C, 40°C) and humidity conditions (30%, 60%, 90%). The results showed that each nanomaterial has its own unique strengths : ZnO nanowires had the highest sensitivity (0.2 ng/mL LOD) and selectivity (95%) for TNT detection, rGO sensors outperformed in TATP detection (0.4 ng/mL LOD, 93% selectivity) and were highly resistant to environmental conditions (92% signal retention at 90% RH), MWCNT sensors had well-rounded performance for multiple types of explosives (83.3% average selectivity), and SWCNT sensors had fast response times (25 seconds) but poor humidity tolerance. The overall performance ranking revealed rGO as the best-performing material (score: 35.8/40), followed by MWCNT (score: 29.3/40). The results obtained suggest that the optimal sensor choice is application-dependent and not generalizable, thus facilitating the design of multi-sensor array systems that can harness the unique advantages of various nanomaterial platforms for improved explosive detection capabilities.