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A Summary of NASA’s E-Nose Technology

PI: Ami Hannon

Exhaled breath analysis has gained significant attention as a noninvasive and portable method for health diagnosis, offering advantages such as convenience, safety, simplicity, and minimal discomfort. This approach is particularly promising for detecting a wide range of diseases by analyzing the associated breathprints resulting from diseases specific volatile organic biomarkers. In specialized applications like the Environmental Control and Life Support System (ECLSS), exhaled breath analysis is crucial for monitoring astronauts’ health in space, where traditional diagnostic methods are not feasible. The electronic nose (E-Nose) technology, with its high sensitivity, rapid response, real-time monitoring, ease of use, autonomous operation, and portability, is especially suited for such critical environments, providing real-time health data, and enabling early detection of potential issues to ensure astronaut safety and well-being. In addition to health diagnosis, the E-Nose technology is also proven to be useful for cabin air monitoring, food safety, and process control.

Key Features:

  1. Sensor Array Technology:
    • Uses 64 nanosensors made from materials like carbon nanotubes.
    • Detects volatile organic compounds (VOCs) and biomarkers in human breath at parts-per-billion (ppb) levels.
    • High sensitivity and repeatability (0.02%), comparable to laboratory-grade instruments.
  2. Portable Design:
    • Handheld device integrates sensors, data acquisition, and smartphone/tablet controls.
    • Results are available within minutes, enabling rapid screening.
  3. Wide Applications:
    • Designed for early disease detection in healthcare, public settings, and space exploration.
    • Provides insights into respiratory conditions, metabolic disorders, and infectious diseases.

COVID-19 Detection:

  • Preliminary tests at Stanford Medicine on 63 participants demonstrated 79% accuracy in distinguishing COVID-19-positive and negative cases.
  • Combines sensor data with machine learning models for improved classification.
  • Breath sampling leverages single-use bags, processed with built-in humidity controls to maintain consistency.

Advancements and Future Work:

  • Incorporating machine learning for higher accuracy and integration with symptom screening.
  • Planned deployment for mass screening in clinics, airports, and other public venues.
  • Ongoing clinical trials aim to refine the technology and reduce costs through optimized sensor arrays.

This innovative device represents a breakthrough in non-invasive diagnostics, leveraging spaceflight-proven technology for real-time health monitoring in diverse environments.

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