2011 MRes projects
- The development of a backscatter X-ray system for cargo & vehicle screening
- Self-organisational behaviour patterns in crowds within the context of crime at bus stops
- Hippo Foraging and Poaching Using Agent Based Modelling
- Obstructions and Requirements for Coercion Resistance
- Using Semiconducting Metal Oxide Gas Sensors to Detect Explosives - A Feasibility Study
- Speed Up Effects in Security Procedure on Delhi Metro Rail : Implications for Queuing Theory and Rail Security
- 'Have Gun - Will Travel’: The Movement and Re-use of Firearms in England and Wales
- Time-of-Flight X-Ray Compton Scatter Imaging for Cargo Security: A Preliminary Study
- Is High Performance Liquid Chromatography Analysis a Useful Addition to Current Geo-Forensic Analytical Techniques?
- A Comparison of the Spread of Extreme Protest Behaviours Through Two Activist Networks
- On the Feasibility of Using Probably Approximately Correct Search Over BitTorrent Tracking Information
Using Semiconducting Metal Oxide Gas Sensors to Detect Explosives - A Feasibility Study
22 March 2013
Explosives present a very real and present threat in the world today, and detection of hidden explosive devices is a key priority for security and defence practitioners. Vapour detection is one useful tool currently in development, with much focus on emulating sniffer-dogs by developing an electronic nose. Electronic noses based on semiconducting metal oxides (SMOs) are an inexpensive, portable and sensitive technology that show great promise. They do however suffer from a lack of selectivity. This project set out to explore whether or not an SMO array could differentiate between a set of explosive marker vapours. An array of ten SMO gas sensors was fabricated, based on WO3 and In2O3 inks. Production was by a commercial screen printing technique onto substrates containing gold electrodes and a platinum heater track. Nine of the sensors were tested against seven gases, including 5 explosive markers, such as nitromethane and ammonia. The testing rig was re-engineered to allow headspace sampling from both solids and liquids. Sensitivity was improved by overlaying or admixing the oxides with two zeolites, H-ZSM5 and TS-1. Each improved responses to R−NO2 and R−OH moieties respectively. The enhancement properties of gold nanoparticles were also investigated. Finally machine learning techniques were applied, testing the selectivity of the array to four of the gases used with a support vector machine (SVM). Two implementations of an SVM algorithm were run concurrently and data classification was optimised using parameter searching and normalisation. It was shown that the algorithm was capable of good classification of the data even when information on concentration was absent - a promising start for the creation of a detection device. Dstl acted as an external consultant on some aspects of the project, and initial results were presented at Fort Halstead in February 2012.