Nanoscale Devices for Fast and Reversible Chemical Sensors
Abstract
We propose a research program to investigate the limits of nanofabricated sensors for fast, reversible chemical sensing of explosives, decomposition products, and related vapor signatures. There are four main areas of emphasis: 1) improving sensor response speeds, 2) expanding a test set to include explosives and related chemicals, 3) increasing sensor materials diversity, and 4) rational design of sensor materials. The rate of response and recovery for chemical sensors is limited by mass transport and stray capacitance that can be minimized with nanoscale sensors. Our objective is to achieve time constants of 1-10 msec for device turn-on using nanofabrication and dielectrophoresis to control the placement and packing of nanoparticle composite sensing materials. The technical approach is to scale devices to the 1-10 nm limit and determine the physical factors that limit response speed for sorption based chemical sensors. Sensor devices will be fabricated as large arrays on integrated chips that provide multivariate data for pattern recognition to detect and identify vapor signatures. We have previously demonstrated the ability to detect and classify test sets of 20 vapors with > 95% accuracy using multivariate and machine learning algorithms. In the proposed work, we will expand the test set to include chemicals relevant to explosives detection including explosive materials, decomposition products, and related vapor signatures. We hypothesize that identification of unknown vapor signatures will be greatly enhanced by increasing the chemical diversity of sensor materials without penalty for response speed. Moreover, machine learning algorithms benefit from large data streams. We propose to investigate nano inkjet printing to create sensor arrays with 10-20 different materials. We propose to use principles of chemical thermodynamics to rationally design sensor materials for optimal sensitivity for particular analytes as well as enhanced performance of sensor arrays.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 25, 2019
- Source ID
- N000141912246
Entities
People
- Brian G. Willis
Organizations
- Office of Naval Research
- United States Navy
- University of Connecticut