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Laser-printed early warning sensors: Quantum Detection of Chemical and Biological Agents

  • Bogdanowicz, Robert (PI)
  • Rathner, Adriana (PI)
  • Bockrath, Marc (Co-PI)
  • Goddard, William A. (Co-PI)
  • Wojtas, Jacek (Co-PI)
  • Stranak, Vitezslav (Co-PI)
  • California Institute of Technology

Project: Funded researchOther sponsors

Project Details

Description

The LiGAlert project aims to design and study the selective laser-induced phase transformation of carbonaceous substrates (polyimide and textiles) for rapid detection of chemical weapons agents and high energy materials. The interdisciplinary research covers materials engineering, soft matter, nanotechnology, and electrochemistry. The project intends to develop a new generation of flexible sensors by creating laser-induced graphene patterns on polyimide and analyzing their mass and charge transport, as well as their mechanical and electrical properties under stress and aging. We propose using tuneable peptide sequences modification for the selective detection at LiG platform, which are easy to produce and have optimized sensitivity. The project a plan to detect chemical and biological agents using peptides derived from olfactory receptors grafted at LiG surfaces. These recognition peptides have a high specificity and sensitivity for various explosives like TNT and DNT. The molecular recognition of explosives by impedimetric analysis is the scientific novelty of this task and will greatly contribute to the development of this technology. The LIG sensors will be tested with various high- energy, military important chemicals and the impedance characteristics will be recorded and matched to a database of chemical substances.
Artificial intelligence algorithms will be implemented to recognize chemical compounds and determine their parameters based on impedance characteristics. A neural network will be the basis of the intelligent algorithm and its dimensions and parameters will be adapted to the properties of the LIG sensors. A previously prepared measurement database of LIG sensor responses to dangerous chemical particles will be used for network learning. Genetic algorithms will be used to speed up the learning process. Field tests will be carried out in conditions similar to operational scenarios used by military and police specialists in all phases of pre- and post-blast investigations, such as prevention, detection, mitigation, and reaction.
Short titleLiGAlert
StatusActive
Effective start/end date01.02.202431.01.2027

Collaborative partners

  • Johannes Kepler University Linz
  • Gdańsk University of Technology (lead)
  • California Institute of Technology
  • The Ohio State University
  • Military University of Technology
  • University of South Bohemia

Fields of science

  • 104026 Spectroscopy
  • 106006 Biophysics
  • 106057 Metabolomics
  • 106005 Bioinformatics
  • 106041 Structural biology
  • 104002 Analytical chemistry
  • 210002 Nanobiotechnology
  • 104021 Structural chemistry
  • 106023 Molecular biology
  • 106002 Biochemistry

JKU Focus areas

  • Sustainable Development: Responsible Technologies and Management