This proposal addresses the SEC-2011.3.4-2 Artificial Sniffer call. The research will develop a universal gas sensor using modular technologies to function as an artificial sniffer that will detect a range of substances. The technology will complement trained sniffer dogs that are currently used. The technology proposed is based on linear ion trap (LIT) mass spectrometry (MS).

MS techniques have been increasingly deployed in security sniffing applications. MS is a non-intrusive high-resolution technique able to detect single atoms and complex molecules through their charged species (ions) or fragmentation pattern. The technique is capable of detecting an extremely wide range of substances rapidly, with high accuracy and with a stand-off capability - critically it is able to detect trace levels below parts per million. Once the MS fingerprint of an unknown substance is measured it can be compared online with a database of known substances and rapid identification can be made on the spot in real time.

Sniffles will develop a LIT MS based device that has a mass range larger than other comparable MS techniques. Additionally, methods for miniaturisation and modularisation will be applied to allow reduced vacuum demand and upgradeability. Miniaturisation will be made possible through improved designs based on results from modelling, implementation of novel manufacturing techniques and improvements in the MS drive electronics and vacuum system. These advances will bring benefits including reduced acquisition/operating costs, greater mobility, user friendliness and flexibility.

Sniffles has the potential to have a significant impact on National security and border control and enable exploitation of International markets. A successful project outcome will demonstrate an automated portable MS-based sniffer instrument, tested and evaluated for a range of security applications and markets by end-users.

Keywords: Mass spectrometry, linear on trap, electronic nose, mass spectra, Sniffer dogs, border security, border control.