Electrogenic bacteria produce power in soil based terrestrial microbial fuel cells (tMFCs) by growing on electrodes and transferring electrons released from the breakdown of substrates. The direction and magnitude of voltage production is hypothesized to be dependent on the available substrates. A sensor technology was developed for compounds indicative of anthropological activity by exposing tMFCs to gasoline, petroleum, 2,4-dinitrotoluene, fertilizer, and urea. A machine learning classifier was trained to identify compounds based on the voltage patterns. After 5 to 10 days, the mean voltage stabilized (+/- 0.5 mV). After the entire incubation, voltage ranged from -59.1 mV to 631.8 mV, with the tMFCs containing urea and gasoline producing the highest (624 mV) and lowest (-9 mV) average voltage, respectively. The machine learning algorithm effectively discerned between gasoline, urea, and fertilizer with greater than 94% accuracy, demonstrating that this technology could be successfully operated as an environmental sensor for change detection.

The natural freezing and thawing of soils dramatically affects their thermal and mechanical properties. This can have destructive effects on structures built on those soils. This study developed a thermodynamic finite element model using multiple frost-susceptible soil types. It measured thermal conductivity and temperature through several freeze-thaw cycles. We identified moisture migration as likely the most significant factor in frost heave and frost penetration. Additionally, the thermal conductivity increased near the freezing front across all samples. For example, the thermal conductivity for ML (low-plasticity silt) soils rose from 301 to 357 milliBtu/(hr*ft*°F), which appeared to correspond to where the moisture concentrated and ice formation was highest. Our experimental results guided model development, where thermal parameters changed with respect to temperature, ice, and moisture during freeze-thaw cycles. Using dynamic thermal parameters improved frostdepth prediction compared to the standard Modified Berggren equation. For our tested conditions, the equation had an error of 2.2 in. for a frost depth of 8 in. while our model had an error of 1.4 in. These developments are important to airfield runway and general pavements design and maintenance in frost-affected regions. The findings will allow more accurate predictions of frost depth and deflection. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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