ABSTRACT
Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect the characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposures assessment.
ABSTRACT
The COVID-19 pandemic heightened public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via aerosols. The persistence of potentially infectious aerosol in public spaces, particularly medical settings, deserves close investigation; however, approaches for rapidly parameterizing the temporospatial distribution of particles released by an infected individual have not been reported in literature. This paper presents a methodology for mapping the movement of aerosol plumes using a network of low-cost PM sensors in ICUs. Mimicking aerosol generation by a patient, we tracked aerosolized NaCl particles functioning as tracers for potentially infectious aerosols. In positive (closed door) and neutral-pressure (open door) ICUs, an aerosol spike was detected outside the room, with up to 6% or 19% of all PM escaping through the door gaps, respectively. The outside sensors registered no aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial data suggests three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) immediately outside the room. These zones inform two-phase aerosol plume behavior: dispersion of the original aerosol spike throughout the room, and evacuation phase where "well-mixed" PM decayed uniformly. Decay rates were calculated for 4 ICUs in positive, neutral, and negative mode, with negative modes decaying the fastest. This research demonstrates the methodology for aerosol persistence monitoring in medical settings; however, it is limited by a relatively small data set. Future studies need to evaluate medical settings with high risks of infectious disease, assess risks of airborne disease transmission, and optimize hospital infrastructure.