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Creators/Authors contains: "Vikesland, Peter J."

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  1. Many outbreaks of emerging disease ( e.g. , avian influenza, SARS, MERS, Ebola, COVID-19) are caused by viruses. In addition to direct person-to-person transfer, the movement of these viruses through environmental matrices (water, air, and food) can further disease transmission. There is a pressing need for rapid and sensitive virus detection in environmental matrices. Nanomaterial-based sensors (nanosensors), which take advantage of the unique optical, electrical, or magnetic properties of nanomaterials, exhibit significant potential for environmental virus detection. Interactions between viruses and nanomaterials (or recognition agents on the nanomaterials) can induce detectable signals and provide rapid response times, high sensitivity, and high specificity. Facile and field-deployable operations can be envisioned due to the small size of the sensing elements. In this frontier review, we summarize virus transmission via environmental pathways and then comprehensively discuss recent applications of nanosensors to detect various viruses. This review provides guidelines for virus detection in the environment through the use of nanosensors as a tool to decrease environmental transmission of current and emerging diseases. 
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  2. Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles ( i.e. , ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications. 
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  3. We introduce the facile one-step biosynthesis of a bilayer structured hydrogel composite of reduced-graphene oxide (rGO) and bacterial nanocellulose (BNC) for multiple photothermal water treatment applications. One-step in situ biosynthesis of a bilayered hydrogel was achieved via modification of BNC growth medium supplemented with an optimized concentration of corn steep liquor as a growth enhancer. A two-stage, growth rate-controlled formation mechanism for the bilayer structure was revealed. The final cleaned and freeze-dried reduced-GO embedded BNC bilayer membrane enables versatile applications such as filtration (tested using gold nanoparticles, Escherichia coli cells, and plasmid DNA), photothermal disinfection of entrapped E. coli , and solar water evaporation. Comparable particle rejection (up to ≈4 nm) and water flux (146 L h −1 m −2 ) to ultrafiltration were observed. Entrapment and photothermal inactivation of E. coli cells were accomplished within 10 min of solar exposure (one sun). Such treatment can potentially suppress membrane biofouling. The steam generation capacity was 1.96 kg m −2 h −1 . Our simple and scalable approach opens a new path for biosynthesis of nanostructured materials for environmental and biomedical applications. 
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  4. To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales. 
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