Super-Sensing of Human and Environmental Odors

Abstract

The goal of our research program is to construct an electronic olfactory system (enose) that has the sensitivity, intelligence and discriminative capability of the mammalian olfactory system, as exemplified by a trained sniffer dog. We aim to create both the sensor array front end and the pattern analysis and learning back end. Our criteria for success include rapid and reliable detection of trace gas signals from both humans and ordinance. We have developed a novel sensor technology based on DNA-decorated single-walled semiconducting carbon nanotubes arrayed as the semiconductor elements in field effect transistors (Gelperin and Johnson, 2008; Johnson et al., 2009). A diagram of our sensor is shown in Figure 1. Our strategy is to broaden and deepen our understanding of the ability of this new sensor technology to respond to and discriminate between odorants while simultaneously developing the pattern recognition and machine learning techniques to process sensor array data. Our progress in these areas will be presented in three parts dealing with sensor development, odor sampling with a commercial sensor array, and algorithm development.

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Document Details

Document Type
Technical Report
Publication Date
Jun 08, 2010
Accession Number
ADA534341

Entities

People

  • A. T. Johnson
  • Alan Gelperin
  • Dan D. Lee
  • Pamela Dalton

Organizations

  • Monell Chemical Senses Center

Tags

Communities of Interest

  • Advanced Electronics
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Carbon Nanotubes
  • Chemical Synthesis
  • Chemistry
  • Detection
  • Electronic Components
  • Field Effect Transistors
  • Fullerenes
  • Graphene
  • Measurement
  • Organic Compounds
  • Pattern Recognition
  • Raman Spectra
  • Semiconductors
  • Signal Processing
  • Systems Engineering
  • Trace Gases
  • Volatile Organic Compounds

Readers

  • Canine Service Warrior Training Program for Wounded Warriors in the Veterinary Industry, Supported by Donors.
  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems