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Mining Contract: A Wearable Multivariable Sensor for NO2 and CO Detection

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Contract #200-2014-59395
Start Date9/1/2014
End Date8/31/2015
Research Concept

Reliable and unobtrusive detection of nitrogen dioxide and carbon monoxide released from diesel equipment in mines is critical to prevent overexposure and adverse health effects on workers in these locations.

Topic Areas

Contract Status & Impact

This contract is complete. To receive a copy of the final report, send a request to OMSHR@cdc.gov.

In underground and surface mines, particularly in poorly ventilated and confined spaces, accumulation of carbon monoxide (CO) and nitrogen dioxide (NO2) released from diesel-based mining equipment can occur, resulting in an overexposure of these gases to miners working in these locations. Several adverse effects including asthma, nausea, headache, nasal irritation, and, in certain instances, severe toxicity have been linked to exposure to elevated diesel exhaust concentrations. The Threshold Limit Value (TLV) over an 8-hr time-weighted average period enforced by Mine Safety and Health Administration (MSHA) for NO2 and CO are 5 and 50 ppm, respectively, while concentrations Immediately Dangerous to Life or Health (IDLH) are 20 and 1,200 ppm, respectively.

One of the lower-cost methods for detecting gases is using metal oxides. While these sensors show very high sensitivity and are able to detect carbon monoxide and nitrogen dioxide across wide concentration ranges, they also suffer from certain limitations. These include (1) cross-reactivity toward other gases; (2) sensitivity toward fluctuations at ambient temperature; and (3) significant power requirements.

Under this contract, General Electric designed composite sensing materials using metal catalysts, metal oxide semiconductors, and mixed metal oxides with varying structural properties to attempt to develop a sensor that is sensitive to CO and NO2 at the desired concentrations but is not affected by the interferences present in mines. These materials were laboratory tested for sensitivity to CO and NO2 and the effects of interferences. Unfortunately, the effect of NO as a possible interferent was not reliably tested.

As a result of this research, based on the combination of the sensing materials developed as well as the data analytics tools and algorithms used, a sensor was developed that could detect NO2 and CO. The sensor showed promise in detecting these gases in the presence of interferences. Relative humidity had an effect on detection, and a correction method was developed, but not adequately verified.


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