Hydrothermal Destruction and Defluorination of Trifluoroacetic Acid (TFA).

Per- and polyfluoroalkyl substances (PFAS) have received increased attention due to their environmental prevalence and threat to public health. Trifluoroacetic acid (TFA) is an ultrashort-chain PFAS and the simplest perfluorocarboxylic acid (PFCA). While the US EPA does not currently regulate TFA, its chemical similarity to other PFCAs and its simple molecular structure make it a suitable model compound for studying the transformation of PFAS. We show that hydrothermal processing in compressed liquid water transforms TFA at relatively mild conditions (T = 150-250 °C, P < 30 MPa), initially yielding gaseous products, such as CHF3 and CO2, that naturally aspirate from the solution. Alkali amendment (e.g., NaOH) promotes the mineralization of CHF3, yielding dissolved fluoride, formate, and carbonate species as final products. Fluorine and carbon balances are closed using Raman spectroscopy and fluoride ion selective electrode measurements for experiments performed at alkaline conditions, where gas yields are negligible. Qualitative FTIR gas analysis allows for establishing the transformation pathways; however, the F-balance could not be quantitatively closed for experiments without NaOH amendment. The kinetics of TFA transformation under hydrothermal conditions are measured, showing little to no dependency on NaOH concentration, indicating that the thermal decarboxylation is a rate-limiting step. A proposed TFA transformation mechanism motivates additional work to generalize the hydrothermal reaction pathways to other PFCAs.

Miniaturizing Wet Scrubbers for Aerosolized Droplet Capture.

Aerosols dispersed and transmitted through the air (e.g., particulate matter pollution and bioaerosols) are ubiquitous and one of the leading causes of adverse health effects and disease transmission. A variety of sampling methods (e.g., filters, cyclones, and impactors) have been developed to assess personal exposures. However, a gap still remains in the accessibility and ease-of-use of these technologies for people without experience or training in collecting airborne samples. Additionally, wet scrubbers (large non-portable industrial systems) utilize liquid sprays to remove aerosols from the air; the goal is to "scrub" (i.e., clean) the exhaust of industrial smokestacks, not collect the aerosols for analysis. Inspired by wet scrubbers, we developed a device fundamentally different from existing portable air samplers by using aerosolized microdroplets to capture aerosols in personal spaces (e.g., homes, offices, and schools). Our aerosol-sampling device is the size of a small teapot, can be operated without specialized training, and features a winding flow path in a supersaturated relative humidity environment, enabling droplet growth. The integrated open mesofluidic channels shuttle coalesced droplets to a collection chamber for subsequent sample analysis. Here, we present the experimental demonstration of aerosol capture in water droplets. An iterative study optimized the non-linear flow manipulating baffles and enabled an 83% retention of the aerosolized microdroplets in the confined volume of our device. As a proof-of-concept for aerosol capture into a liquid medium, 0.5-3 µm model particles were used to evaluate aerosol capture efficiency. Finally, we demonstrate that the device can capture and keep a bioaerosol (bacteriophage MS2) viable for downstream analysis.

Source Apportionment of Environmental Combustion Sources using Excitation Emission Matrix Fluorescence Spectroscopy and Machine Learning.

The link between particulate matter (PM) air pollution and negative health effects is well-established. Air pollution was estimated to cause 4.9 million deaths in 2017 and PM was responsible for 94% of these deaths. In order to inform effective mitigation strategies in the future, further study of PM and its health effects is important. Here, we present a method for identifying sources of combustion generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy and machine learning (ML) algorithms. PM samples were collected during a health effects exposure assessment panel study in Seattle. We use archived field samples from the exposure study and the associated positive matrix factorization (PMF) source apportionment based on X-ray fluorescence and light absorbing carbon measurements to train convolutional neural network and principal component regression algorithms. We show EEM spectra from cyclohexane extracts of the archived filter samples can be used to accurately apportion mobile and vegetative burning sources but were unable to detect crustal dust, Cl-rich, secondary sulfate and fuel oil sources. The use of this EEM-ML approach may be used to conduct PM exposure studies that include source apportionment of combustion sources.

Characterization of Inkjet-Printed Digital Microfluidics Devices.

Digital microfluidics (DMF) devices enable precise manipulation of small liquid volumes in point-of-care testing. A printed circuit board (PCB) substrate is commonly utilized to build DMF devices. However, inkjet printing can be used to fabricate DMF circuits, providing a less expensive alternative to PCB-based DMF designs while enabling more rapid design iteration cycles. We demonstrate the cleanroom-free fabrication process of a low-cost inkjet-printed DMF circuit. We compare Kapton and polymethyl methacrylate (PMMA) as dielectric coatings by measuring the minimal droplet actuation voltage for a range of actuation frequencies. A minimum actuation voltage of 5.6 V was required for droplet movement with the PMMA layer thickness of 0.2 µm and a hydrophobic layer of 0.17 µm. Significant issues with PMMA dielectric breakdown were observed at actuation voltages above 10 V. In comparison, devices that utilized Kapton were found to be more robust, even at an actuation voltage up to 100 V.

Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network.

Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.

Excitation-Emission Matrix Spectroscopy for Analysis of Chemical Composition of Combustion Generated Particulate Matter.

Analysis of particulate matter (PM) is important for the assessment of human exposures to potentially harmful agents, notably combustion-generated PM. Specifically, polycyclic aromatic hydrocarbons (PAHs) found in ultrafine PM have been linked to cardiovascular diseases and carcinogenic and mutagenic effects. In this study, we quantify the presence and concentrations of PAHs with lower molecular weight (LMW, 126 < MW < 202) and higher molecular weight (HMW, 226 < MW < 302), i.e., smaller and larger than Pyrene, in combustion-generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy. Laboratory combustion PM samples were generated in a laminar diffusion inverted gravity flame reactor (IGFR) operated on ethylene and ethane. Fuel dilution by Ar in 0% to 90% range controlled the flame temperature. The colder flames result in lower PM yields however, the PM PAH content increases significantly. Temperature thresholds for PM transition from low to high organic carbon content were characterized based on the maximum flame temperature (Tmax,c ∼ 1791 to 1857 K) and the highest soot luminosity region temperature (T*c ∼ 1600 to 1650K). Principal component regression (PCR) analysis of the EEM spectra of IGFR samples correlates to GCMS data with R2 = 0.988 for LMW and 0.998 for HMW PAHs. PCR-EEM analysis trained on the IGFR samples was applied to PM samples from woodsmoke and diesel exhaust, the model accurately predicts HMW PAH concentrations with R2 = 0.976 and overestimates LMW PAHs.

Hydrolysis of Dimethyl Methylphosphonate (DMMP) in Hot-Compressed Water.

Dimethyl methylphosphonate (DMMP) is often used as a chemical surrogate for organophosphate nerve agents, as it exhibits similar physiochemical properties while having significantly lower toxicity. Continuous hydrolysis of DMMP in hot-compressed water is performed at temperatures from 200 to 300 °C, pressures of 20 and 30 MPa, and residence times from 30 to 80 s to evaluate the effects of pressure and temperature on reaction kinetics. DMMP hydrolysis is observed to follow pseudo-first-order reaction behavior, producing methylphosphonic acid and methanol as the only detectable reaction products. This is significant for the practical implementation of a continuous hydrothermal reactor for chemical warfare agent neutralization, as the process only yields stable, less-toxic compounds. Pressure has no discernible effect on the hydrolysis rate in compressed liquid water. Pseudo-first-order Arrhenius parameters are determined, with an activation energy of 90.17 ± 5.68 kJ/mol and a pre-exponential factor of 107.51±0.58 s-1.

Nanostructure Transition of Young Soot Aggregates to Mature Soot Aggregates in Diluted Diffusion Flames.

In this study, the structural properties of soot produced in diffusion flames are analyzed to elucidate the formation of mature aggregates from large young particles. Soot samples are generated in a laminar diffusion inverted gravity flame reactor (IGFR) operated on methane, ethane, and ethylene with Ar dilution to reduce the flame temperature. Soot produced in temperature ranges from 1495K-1568 K contains 100nm-300nm particles with (i) isotropic or (ii) multiple core structures, supporting a soot maturation pathway where one young soot particle evolves into a mature fractal aggregate via an internal nucleation route. During the process, these large amorphous particles can form internal voids as the particle loses mass due to pyrolysis or oxidation. Transmission electron microscopy (TEM) shows that young soot aggregates contain a higher fraction of shorter fringes and highly curved aromatics (11% vs. 23%), which is in agreement with their higher organic carbon content (3.3%-5.4% vs. 12.1%-28.8% wt.). Increasing the flame temperature reduces the curvature of polycyclic aromatic hydrocarbons (PAHs) and allows for more efficient layer stacking as indicated by a higher percent of stacked fringes. For these gaseous fuels, carbonization appears to be primarily a function of the flame temperature and independent of the fuel composition.

Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification.

The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disease and exacerbation of respiratory diseases such as asthma and pneumonia. PM originates from natural and anthropogenic sources including combustion engines, cigarettes, agricultural burning, and forest fires. Identifying the source of PM can inform effective mitigation strategies and policies, but this is difficult to do using current techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm. We collected combustion generated PM2.5 from wood burning, diesel exhaust, and cigarettes using filters. Filters were weighted to determine mass concentration followed by extraction into cyclohexane and analysis by EEM fluorescence spectroscopy. Spectra obtained from each source served as training data for a convolutional neural network (CNN) used for source identification in mixed samples. This method can predict the presence or absence of the three laboratory sources with an overall accuracy of 89% when the threshold for classifying a source as present is 1.1 µg/m3 in air over a 24-hour sampling time. The limit of detection for cigarette, diesel and wood are 0.7, 2.6, 0.9 µg/m3, respectively, in air assuming a 24-hour sampling time at an air sampling rate of 1.8 liters per minute. We applied the CNN algorithm developed using the laboratory training data to a small set of field samples and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable.

Behavior of Ultrafine Particles in Electro-Hydrodynamic Flow Induced by Corona Discharge.

Ultrafine particle behavior in electro-hydrodynamic (EHD) flow induced by corona discharge is studied experimentally and numerically. The EHD flow serves as a primary particle aspiration/sampling mechanism, the collector does not require any additional flow generation. Multiphysics numerical model couples the ion transport equation and the Navier-Stokes equations (NSE) to solve for the spatiotemporal distribution of electric field, charge density, and flow field, the results are compared with experimental velocity profiles at the exit. The computed velocity and flow rate data are in good agreement with the experimental data; the maximum velocity is located at the axis and ranges from 1 m/s to 4 m/s as a function of corona voltage. Experimentally evaluated particle transmission trends for ambient and NaCl nanoparticles particles in the 20 nm - 150 nm range are in good agreement with the theoretical models. However, for particles in the 10 nm - 20 nm size range, the transmission is lower due to the increased particle charging resulted from their exposure to the high-intensity electric field and high charge density in the EHD driven flow. These conditions yield a high probability of particles below 20 nm to acquire and hold a unit charge. The transmission is lower for smaller particle (10 nm) due to their high charge to mass ratio, and it increases as the single-charged particles grow in mass up to 20 nm, resulting in their lower electrical mobility. For particles larger than 20 nm, the electrical mobility increases again as they can acquire multiple charges. The results shed insight into interaction of nanoparticle and ions in high electrical field environment, that occur in primary EHD driven flows and in the secondary flows generated by corona discharge.

DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients.

Diabetes is a global epidemic that impacts millions of people every year. Enhanced dietary assessment techniques are critical for maintaining a healthy life for a diabetic patient. Moreover, hospitals must monitor their diabetic patients' food intake to prescribe a certain amount of insulin. Malnutrition significantly increases patient mortality, the duration of the hospital stay, and, ultimately, medical costs. Currently, hospitals are not fully equipped to measure and track a patient's nutritional intake, and the existing solutions require an extensive user input, which introduces a lot of human errors causing endocrinologists to overlook the measurement. This paper presents DietSensor, a wearable three-dimensional (3D) measurement system, which uses an over the counter 3D camera to assist the hospital personnel with measuring a patient's nutritional intake. The structured environment of the hospital provides the opportunity to have access to the total nutritional data of any meal prepared in the kitchen as a cloud database. DietSensor uses the 3D scans and correlates them with the hospital kitchen database to calculate the exact consumed nutrition by the patient. The system was tested on twelve volunteers with no prior background or familiarity with the system. The overall calculated nutrition from the DietSensor phone application was compared with the outputs from the 24-h dietary recall (24HR) web application and MyFitnessPal phone application. The average absolute error on the collected data was 73%, 51%, and 33% for the 24HR, MyFitnessPal, and DietSensor systems, respectively.

Soot Morphology and Nanostructure in Complex Flame Flow Patterns via Secondary Particle Surface Growth.

While the majority of studies explore soot formation in relatively simple, one-dimensional flames, most real-world flames consist of complex flows defined by large-scale turbulent eddies, recirculating flow patterns, and buoyancy effects. The effects of complex flow on soot physicochemical properties are poorly understood. This work employs an inverted gravity flame reactor (IGFR) to compare differences in soot growth between a one-dimensional laminar diffusion flame and a recirculating flame. Computational fluid dynamics (CFD) and experimental observations show particle oscillations between (i) a rich region with a high concentration of surface growth species, and (ii) a high-temperature oxidation region. Transmission electron microscopy (TEM) shows a significant difference in final primary particle diameter, where the one-dimensional flame produces primary particles 10 to 25 nm in diameter and the recirculating flame produces primary particles 25 to 75 nm in diameter. Additionally, larger primary particles from the recirculating flame contain both single and multiple cores. We propose that due to the spheroidal shape of the large primary particles, the secondary surface growth is primarily a result of polyaromatic hydrocarbon (PAH) condensation during re-entrainment of mature soot into the fuel-rich region followed by subsequent liquid layer carbonization in the high-temperature environment of the flame front. The recirculating flow patterns in the IGFR and repeated particle growth/oxidation cycle can serve as a model for soot formation in the large-scale flames with complex flow patterns, such as forest fires, coal fire plants, and other sources.

Nanoink bridge-induced capillary pen printing for chemical sensors.

Single-walled carbon nanotubes (SWCNTs) are used as a key component for chemical sensors. For miniature scale design, a continuous printing method is preferred for electrical conductance without damaging the substrate. In this paper, a non-contact capillary pen printing method is presented by the formation of a nanoink bridge between the nib of a capillary pen and a polyethylene terephthalate film. A critical parameter for stable printing is the advancing contact angle at the bridge meniscus, which is a function of substrate temperature and printing speed. The printed pattern including dots, lines, and films of SWCNTs are characterized by morphology, optical transparency, and electrical properties. Gas and pH sensors fabricated using the non-contact printing method are demonstrated as applications.

DESIGN AND OPTIMIZATION OF A COMPACT LOW-COST OPTICAL PARTICLE SIZER.

Direct measurements of time- and size-resolved particulate matter (PM) concentrations are of major importance in air quality studies and pollution monitoring. Low-cost, compact optical particle counters (OPCs), which provide accurate PM measurements independent of the particle complex index of refraction (CRI), can be useful in personal exposure monitoring and distributed sensor network studies applications. A methodology is presented for the optimization of the sensor design and operation parameter space aimed at reducing the effect of the CRI on particle sizing errors. The Monte Carlo numerical simulation, which utilizes Mie scattering calculations, is used to determine the optimal detector angle for the specific set of constraints described by the weighting coefficients. The optimized detector position (θ = 48°) has the lowest dependency on CRI over the entire particle size range of 0.5-10 microns. The near-forward, optimized, and perpendicular detector angles are compared experimentally using monodisperse 2 µm and 4 µm particles of silica, PSL, and alumina; the light collection cone angle is set at α = 20° in all experiments. The data agree well with the numerical results for all tested scenarios. Overall, the perpendicular detector location has the best precision and worst accuracy related to the CRI variations. The optimized detector position has the best accuracy for both silica and alumina particles. The use of low-cost components, such as laser diodes, photodiodes, miniaturized integrated electronics, and simple component layouts allows for the development of compact OPCs capable of accurately sizing PM. The number of sizing bins, sizing accuracy and precision, and other parameters of interest can be used as an input to an optimization algorithm.

Development of an elution device for ViroCap virus filters.

Environmental surveillance of waterborne pathogens is vital for monitoring the spread of diseases, and electropositive filters are frequently used for sampling wastewater and wastewater-impacted surface water. Viruses adsorbed to electropositive filters require elution prior to detection or quantification. Elution is typically facilitated by a peristaltic pump, although this requires a significant startup cost and does not include biosafety or cross-contamination considerations. These factors may pose a barrier for low-resource laboratories that aim to conduct environmental surveillance of viruses. The objective of this study was to develop a biologically enclosed, manually powered, low-cost device for effectively eluting from electropositive ViroCap™ virus filters. The elution device described here utilizes a non-electric bilge pump, instead of an electric peristaltic pump or a positive pressure vessel. The elution device also fully encloses liquids and aerosols that could contain biological organisms, thereby increasing biosafety. Moreover, all elution device components that are used in the biosafety cabinet are autoclavable, reducing cross-contamination potential. This device reduces costs of materials while maintaining convenience in terms of size and weight. With this new device, there is little sample volume loss due to device inefficiency, similar virus yields were demonstrated during seeded studies with poliovirus type 1, and the time to elute filters is similar to that required with the peristaltic pump. The efforts described here resulted in a novel, low-cost, manually powered elution device that can facilitate environmental surveillance of pathogens through effective virus recovery from ViroCap filters while maintaining the potential for adaptability to other cartridge filters.

Standoff trace explosives vapor detection at meter distances.

Vapor detection is a noncontact sampling method, which is a less invasive means of explosives screening than physical swiping. Explosive vapor detection is a challenge due to the low levels of vapors available for detection. This study demonstrates that the parts-per-quadrillion sensitivity of atmospheric flow tube-mass spectrometry (AFT-MS) combined with a high-volume air sampler enables standoff detection of trace explosives vapor at distances of centimeters to meters. Standoff detection of explosives vapor was possible both upstream and downstream of the vapor source relative to room air currents. RDX vapor from a saturated source was detected at up to 2.5 m. Vapors from RDX residue and nitroglycerin residue were detected at distances up to 0.5 m. The sampling can be optimized by accounting for air movement in the room or environment, which could further extend standoff detection distances. Using AFT-MS with a high-volume sampler could also be effective for standoff vapor detection of drugs and additional chemical threats and could be useful for security screening applications such as at mail facilities, border crossings, and security checkpoints.

Destruction of PFAS in AFFF-impacted fire training pit water, with a continuous hydrothermal alkaline treatment reactor.

As regulations are being established to limit the levels of per- and polyfluoroalkyl substances (PFAS) in drinking water and wastewater, effective treatment technologies are needed to remove or destroy PFAS in contaminated liquid matrices. Many military installations and airports have fire training ponds (FTPs) where PFAS-containing firefighting foams are discharged during training drills. FTP water disposal is expensive and challenging due to the high PFAS levels. Hydrothermal alkaline treatment (HALT) has previously been shown to destroy a wide range of PFAS compounds with a high degree of destruction and defluorination. In this study, we investigate the performance of a continuous flow HALT reactor for destroying PFAS in contaminated FTP water samples. Processing with 5 M-NaOH and 1.6 min of continuous processing results in >99% total PFAS destruction, and 10 min processing time yields >99% destruction of every measured PFAS species. Operating with 0.1 M-NaOH or 1 M-NaOH shows little effect on the destruction of measured perfluorosulfonic acids, while all measured perfluorocarboxylic acids and fluorotelomer sulfonates are reduced to levels below the method detection limits. Continuous HALT processing with sufficient NaOH loading appears to destroy parent PFAS compounds significantly faster than batch HALT processing, a positive indicator for scaling up HALT technology for practical applications in environmental site remediation activities.

Destruction and defluorination of PFAS matrix in continuous-flow supercritical water oxidation reactor: Effect of operating temperature.

Cleanup and disposal of stockpiles and waste streams containing per- and polyfluoroalkyl substances (PFAS) require effective end-of-life destruction/mineralization technologies. Two classes of PFAS, perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkyl sulfonic acids (PFSAs), are commonly found in legacy stockpiles, industrial waste streams, and as environmental pollutants. Continuous flow supercritical water oxidation (SCWO) reactors have been shown to destroy several PFAS and aqueous film-forming foams. However, a direct comparison of the SCWO efficacy for PFSAs and PFCAs has not been reported. We show the effectiveness of continuous flow SCWO treatment for a matrix of model PFCAs and PFSAs as a function of operating temperature. PFSAs appear to be significantly more recalcitrant than PFCAs in the SCWO environment. The SCWO treatment results in a destruction and removal efficiency of 99.999% at a T > 610 °C and at a residence time of ∼30 s. Fluoride recovery lags destruction PFAS at 510 °C and reaches >100% above 610 °C, confirming the formation of liquid and gaseous phase intermediate product during lower temperature oxidation. This paper establishes the threshold for destroying PFAS-containing liquids under SCWO conditions.

Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings.

Over four thousand portable air cleaners (PACs) with high-efficiency particulate air (HEPA) filters were distributed by Public Health - Seattle & King County to homeless shelters during the COVID-19 pandemic. This study aimed to evaluate the real-world effectiveness of these HEPA PACs in reducing indoor particles and understand the factors that affect their use in homeless shelters. Four rooms across three homeless shelters with varying geographic locations and operating conditions were enrolled in this study. At each shelter, multiple PACs were deployed based on the room volume and PAC's clean air delivery rate rating. The energy consumption of these PACs was measured using energy data loggers at 1-min intervals to allow tracking of their use and fan speed for three two-week sampling rounds, separated by single-week gaps, between February and April 2022. Total optical particle number concentration (OPNC) was measured at 2-min intervals at multiple indoor locations and an outdoor ambient location. The empirical indoor and outdoor total OPNC were compared for each site. Additionally, linear mixed-effects regression models (LMERs) were used to assess the relationship between PAC use time and indoor/outdoor total OPNC ratios (I/OOPNC). Based on the LMER models, a 10 % increase in the hourly, daily, and total time PACs were used significantly reduced I/OOPNC by 0.034 [95 % CI: 0.028, 0.040; p < 0.001], 0.051 [95 % CI: 0.020, 0.078; p < 0.001], and 0.252 [95 % CI: 0.150, 0.328; p < 0.001], respectively, indicating that keeping PACs on resulted in significantly lower I/OOPNC. The survey suggested that keeping PACs on and running was the main challenge when operating them in shelters. These findings suggested that HEPA PACs were an effective short-term strategy to reduce indoor particle levels in community congregate living settings during non-wildfire seasons and the need for formulating practical guidance for using them in such an environment.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19.

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 a 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 exposure assessment.