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Artificial sniffer using linear ion trap technology

Cel

The research will develop a universal gas sensor using modular technologies to function as an artificial sniffer. It will detect a range of substances, including but not limited to people, drugs, explosives (including weapons) and CBRNe. The technology will complement trained sniffer dogs. The technology proposed is based on linear ion trap (LIT) mass spectrometry (MS). MS techniques have been increasingly deployed in security sniffing applications in the USA. 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 a 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 a unknown substance is measured it can be compared online with a database of known substances enabling real-time rapid identification. Sniffles will develop a LIT MS based device with a larger mass range than other comparable MS techniques. 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, 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 significantly impact on National Security and border control and enable exploitation of International Markets. Performance will be benchmarked against a state-of-the-art conventional MS system and security sniffer dogs – within the context of a border security checkpoint. Sniffles aims to demonstrate an automated portable MS-based sniffer device, tested and evaluated for a range of security applications and markets by end-users.

Zaproszenie do składania wniosków

FP7-SEC-2011-1
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TWI LIMITED
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Debbie Mcconnell (Ms.)

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THE UNIVERSITY OF LIVERPOOL
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DSM R & D SOLUTIONS BV

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Q TECHNOLOGIES LTD
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UNIVERSITE DE PROVENCE

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