Surveillance and Reconnaissance Techniques for Chemical and Biological Threats
- Project Code: TeChBioT
- Start Date: December 1, 2022
- End Date: November 30, 2025
- Reference: 101103176
- Funding: European Commission (EC), European Defense Fund (EDF)
- Royal Military Academy Involvement: Coordinator
- Quad Chart
Team
- Coordinator: Leticia Fernandez Velasco (CHCH)
- Coordinator Affiliation: Royal Military Academy, Department of Chemistry (CHCH)
- Project Co-promotor: Skralan Hosteaux
- RMA Researchers: Oscar Olarte Rodriguez, Darius Couchard
- Project Partners (RMA): Department of Mathematics (MWMW), DLD Bio
- Project Partners (BE-DEF): N/A
- Project Partners (Other): Leibniz Universität Hannover, T4I Engineering, The Centre for Research & Technology - Hellas, Tallinn University of Technology, Bundesministerium der Verteidigung, EXUS AI Labs, Interscience
Context
In the wake of the COVID-19 pandemic and the ensuing effects on society and the economy, there has been a significant increase in concerns about the possibility that malicious actors could return to using hazardous agents in future plots. These concerns are legitimate in Europe, where there are still technological gaps in several aspects of the CBRN Security Cycle and specifically in the devices for rapid detection, identification and monitoring of low-volatile chemical warfare agents (CWAs) and non-volatile biological warfare agents (BWAs), mainly in complex natural environments.
Objectives
Develop new highly selective and sensitive detectors with detection limits in the pptV range, operated at elevated temperatures (> 200 °C) to prevent condensation of low volatile constituents, high 2D resolving power and robust analytical methods.
Methodology
Development of a universal detection technology based on high-temperature (HT) ion mobility spectrometry (IMS) with optional gas chromotographic pre-separation (GC) and pyrolysis (Py) for enabling fast detection and identification of nonvolatile biological and low-volatile chemical agents. Artificial Intelligence (AI) and Deep Learning (DL) models are developed to reduce the dimensionality of the 2D spectral data and enable distinguishing of bacteria, fungi, viruses, low volatile chemical warfare agents, and toxic industrial compounds at pptV concentration levels based on their unique fingerprint within a complex environment.