Latent-to-sensible heat conversion kinetics during nanoparticle coalescence.

Coagulational growth in an aerosol is a multistep process; first particles collide, and then they coalesce with one another. Coalescence kinetics have been investigated in numerous prior studies, largely through atomistic simulations of nanoclusters (102-104 atoms). However, with a few exceptions, they have either assumed the process is completely isothermal or is a constant energy process. During coalescence, there is the formation of new bonds, decreasing potential energy, and correspondingly increasing internal kinetic (thermal) energy. Internal kinetic energy evolution is dependent not only on coalescence kinetics but also on heat transfer to the surrounding gas. Here, we develop and test a model of internal kinetic energy evolution in collisionally formed nanoclusters in the presence of a background gas. We find that internal kinetic energy dynamics hinge upon a power law relationship describing latent-to-sensible heat release as well as a modified thermal accommodation coefficient. The model is tested against atomistic models of 1.5-3.0 nm embedded-atom gold nanocluster sintering in argon and helium environments. The model results are in excellent agreement with the simulation results for all tested conditions. Results show that nanocluster effective temperatures can increase by hundreds of Kelvin due to coalescence, but that the rise and re-equilibration of the internal kinetic energy is strongly dependent on the background gas environment. Interestingly, internal kinetic energy change kinetics are also found to be distinct from surface area change kinetics, suggesting that modeling coalescence heat release solely due to surface area change is inaccurate.

Prediction of temperature-dependent nucleation and growth rates from crystallization-related heat release.

We propose a method for determining the time and, therefore, temperature-dependent relative nucleation and growth rates during crystallization. We do so by linking the partial differential equation governing the time dynamics of the crystal size distribution to kinetic (Avrami) parameters describing heat release. This approach is tested in silico by nucleating and growing diffusion limited aggregates with time-varying morphology and growth rates unhindered by impingement. The associated heat release is analyzed, showing that nucleation and growth rates could be extracted with high fidelity.

Using Machine Learning to Overcome Interfering Oxygen Effects in a Graphene Volatile Organic Compound Sensor.

Discriminating between volatile organic compounds (VOCs) for applications including disease diagnosis and environmental monitoring, is often complicated by the presence of interfering compounds such as oxygen. Graphene sensors are effective at detecting VOCs; however, they are also known to be highly sensitive to oxygen. Therefore, the combined effects of each of these gases on graphene sensors must be understood. In this work, we use graphene variable capacitor (varactor) sensors to examine the cross-selectivity of oxygen at 3 concentrations and 3 VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each. The sensor responses exhibit distinct shapes dependent on the relative concentrations in mixtures of oxygen and VOCs. Because the entire response shape is therefore informative for distinguishing between each gas mixture, a classification algorithm that utilizes entire sequences of data is needed. Accordingly, a long short-term memory (LSTM) network is used to classify the mixtures and VOC concentrations. The model achieves 100% accurate classification of the VOC type, even in the presence of varying levels of oxygen. When the VOC type and VOC concentration are classified, we show that the sensors can provide VOC concentration resolution within approximately 200 ppm. Throughout this work, we also demonstrate that an effective gas mixture classification can be achieved, even while the sensors exhibit varied drift patterns typical of graphene sensors. This is made possible due to the data analysis and machine learning methods employed.

Control technologies to prevent aerosol-based disease transmission in animal agriculture production settings: a review of established and emerging approaches.

Transmission of infectious agents via aerosols is an ever-present concern in animal agriculture production settings, as the aerosol route to disease transmission can lead to difficult-to-control and costly diseases, such as porcine respiratory and reproductive syndrome virus and influenza A virus. It is increasingly necessary to implement control technologies to mitigate aerosol-based disease transmission. Here, we review currently utilized and prospective future aerosol control technologies to collect and potentially inactivate pathogens in aerosols, with an emphasis on technologies that can be incorporated into mechanically driven (forced air) ventilation systems to prevent aerosol-based disease spread from facility to facility. Broadly, we find that control technologies can be grouped into three categories: (1) currently implemented technologies; (2) scaled technologies used in industrial and medical settings; and (3) emerging technologies. Category (1) solely consists of fibrous filter media, which have been demonstrated to reduce the spread of PRRSV between swine production facilities. We review the mechanisms by which filters function and are rated (minimum efficiency reporting values). Category (2) consists of electrostatic precipitators (ESPs), used industrially to collect aerosol particles in higher flow rate systems, and ultraviolet C (UV-C) systems, used in medical settings to inactivate pathogens. Finally, category (3) consists of a variety of technologies, including ionization-based systems, microwaves, and those generating reactive oxygen species, often with the goal of pathogen inactivation in aerosols. As such technologies are typically first tested through varied means at the laboratory scale, we additionally review control technology testing techniques at various stages of development, from laboratory studies to field demonstration, and in doing so, suggest uniform testing and report standards are needed. Testing standards should consider the cost-benefit of implementing the technologies applicable to the livestock species of interest. Finally, we examine economic models for implementing aerosol control technologies, defining the collected infectious particles per unit energy demand.

Evaluation of Hydrogen-Deuterium Exchange during Transient Vapor Binding of MeOD with Model Peptide Systems Angiotensin II and Bradykinin.

The advancement of hybrid mass spectrometric tools as an indirect probe of molecular structure and dynamics relies heavily upon a clear understanding between gas-phase ion reactivity and ion structural characteristics. This work provides new insights into gas-phase ion-neutral reactions of the model peptides (i.e., angiotensin II and bradykinin) on a per-residue basis by integrating hydrogen/deuterium exchange, ion mobility, tandem mass spectrometry, selective vapor binding, and molecular dynamics simulations. By comparing fragmentation patterns with simulated probabilities of vapor uptake, a clear link between gas-phase hydrogen/deuterium exchange and the probabilities of localized vapor association is established. The observed molecular dynamics trends related to the sites and duration of vapor binding track closely with experimental observation. Additionally, the influence of additional charges and structural characteristics on exchange kinetics and ion-neutral cluster formation is examined. These data provide a foundation for the analysis of solvation dynamics of larger, native-like conformations of proteins in the gas phase.

Investigation of Zero-/High-Field Ion Mobility Orthogonal Separation Using a Hyphenated DMA-FAIMS System and Validation of the Two-Temperature Theory at Arbitrary Field for Tetraalkylammonium Salts in Nitrogen.

Toward greater separation techniques for ions, a differential mobility analyzer (DMA) has been coupled with field asymmetric waveform ion mobility spectrometry (FAIMS) to take advantage of two mobility-related but different methods of separation. The filtering effect of the DMA allows ions to be selected individually based on low-field mobility and studied in FAIMS at variable electric field, yielding mobility separations in two dimensions. Because spectra fully describe ion mobility at variable field strength, results are then compared with a two-temperature theory-predicted mobility up to the fourth-order approximation. The comparison yields excellent results up to at least 100 Td, beyond which the theory deviates from experiments. This is attributed to two effects, the enlargement of the structure due to ion heating and the inelasticity of the collisions with the nitrogen bath gas. The corrected mobility can then be used to predict the dispersion plot through a newly developed implicit equation that circumvents the possible issues related to the more elaborate Buryakov equation. Our results simultaneously show that the DMA-FAIMS coupling yields complete information on ion mobility versus the field-strength to gas-density ratio and works toward predicting such spectra from ion structures and gas properties.

A neural network parametrized coagulation rate model for <3 nm titanium dioxide nanoclusters.

Coagulation is a key factor governing the size distribution of nanoclusters during the high temperature synthesis of metal oxide nanomaterials. Population balance models are strongly influenced by the coagulation rate coefficient utilized. Although simplified coagulation models are often invoked, the coagulation process, particularly for nanoscale particles, is complex, affected by the coagulating nanocluster sizes, the surrounding temperature, and potential interactions. Toward developing improved models of nanocluster and nanoparticle growth, we have developed a neural network (NN) model to describe titanium dioxide (TiO2) nanocluster coagulation rate coefficients, trained with molecular dynamics (MD) trajectory calculations. Specifically, we first calculated TiO2 nanocluster coagulation probabilities via MD trajectory calculations varying the nanocluster diameters from 0.6 to 3.0 nm, initial relative velocity from 20 to 700 m s-1, and impact parameter from 0.0 to 8.0 nm. Calculations consider dipole-dipole interactions, dispersion interactions, and short-range repulsive interactions. We trained a NN model to predict whether a given set of nanocluster diameters, impact parameter, and initial velocity would lead to the outcome of coagulation. The accuracy between the predicted outcomes from the NN model and the MD trajectory calculation results is >95%. We subsequently utilized both the NN model and MD trajectory calculations to examine coagulation rate coefficients at 300 and 1000 K. The NN model predictions are largely within the range 0.65-1.54 of MD predictions, and importantly NN predictions capture the local minimum coagulation rate coefficients observed in MD trajectory calculations. The NN model can be directly implemented in population balances of TiO2 formation.

Ion-neutral clustering alters gas-phase hydrogen-deuterium exchange rates.

The rates and mechanisms of chemical reactions that occur at a phase boundary often differ considerably from chemical behavior in bulk solution, but remain difficult to quantify. Ion-neutral interactions are one such class of chemical reactions whose behavior during the nascent stages of solvation differs from bulk solution while occupying critical roles in aerosol formation, atmospheric chemistry, and gas-phase ion separations. Through a gas-phase ion separation technique utilizing a counter-current flow of deuterated vapor, we quantify the degree of hydrogen-deuterium exchange (HDX) and ion-neutral clustering on a series of model chemical systems (i.e. amino acids). By simultaneously quantifying the degree of vapor association and HDX, the effects of cluster formation on reaction kinetics are realized. These results imply that cluster formation cannot be ignored when modeling complex nucleation processes and biopolymer structural dynamics.

The Radial Synergy Test: An Aid to Diagnose de Quervain's Tenosynovitis.

BACKGROUND: Diagnosis of de Quervain's tenosynovitis is made clinically. Finkelstein's and Eichoff's tests are commonly utilized examination maneuvers. Their specificity has been questioned due to a propensity to provoke pain in asymptomatic patients. Using the principle of synergism, the novel radial synergy test takes advantage of isometric contraction of the first dorsal compartment with resisted abduction of the small finger. METHODS: Electromyography was performed on 3 authors and the first dorsal compartment sampled during the maneuver. Sensitivity evaluation was performed via retrospective chart review for patients diagnosed with de Quervain's from 2013 to 2018. Inclusion criteria were documented radial synergy test, Eichoff's test, and ≥90% pain relief after lidocaine/corticosteroid injection. We enrolled 222 patients with 254 affected extremities. Specificity evaluation was performed via a prospective cohort of volunteers undergoing radial synergy and Eichoff's tests. Inclusion criterion was lack of preexisting wrist pain. Score > 0 on Visual Analog Scale was considered positive. We enrolled 48 volunteers with 93 tested extremities. RESULTS: Electromyography revealed positive recruitment of the first dorsal compartment. Sensitivity of the radial synergy test was inferior to Eichoff's test (97% vs 91%, relative risk [RR] = 0.93 [95% confidence interval [CI] = 0.89-0.97], P < .01). Specificity of the radial synergy test was superior to Eichoff's test (99% vs 74%, RR = 1.33 [95% CI = 1.18-1.51], P < .001). CONCLUSIONS: We describe and evaluate the radial synergy test, a novel examination maneuver to aid the diagnosis of de Quervain's. This serves as an adjunct for future diagnostic evaluations with its high specificity. LEVEL OF EVIDENCE: Level II, diagnostic study.

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose.

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

Effect of (3-aminopropyl)triethoxysilane on dissolution of silica nanoparticles synthesized via reverse micro emulsion.

Silica nanomaterials have been studied based on their potential applications in a variety of fields, including biomedicine and agriculture. A number of different molecules have been condensed onto silica nanoparticles' surfaces to present the surface chemistry needed for a given application. Among those molecules, (3-aminopropyl)triethoxysilane (APS) is one of the most commonly applied silanes used for nanoparticle surface functionalization to achieve charge reversal as well as to enable cargo loading. However, the colloidal stability of APS-functionalized silica nanoparticles has not been thoroughly studied, which can be problematic when the high reactivity of amine groups is considered. In this study, four different types of silica nanoparticles with varied location of added APS have been prepared via a reverse micro emulsion process, and their colloidal stability and dissolution behavior have been investigated. Systematic characterization has been accomplished using transmission electron microscopy (TEM), silicomolybdic acid (SMA) spectrophotometric assay, nitrogen adsorption-desorption surface area measurement, and aerosol ion mobility-mass spectrometry to track the nanoparticles' physical and chemical changes during dissolution. We find that when APS is on the interior of the silica nanoparticle, it facilitates dissolution, but when APS is condensed both on the interior and exterior, only the exterior siloxane bonds experience catalytic hydrolysis, and the interior dissolution is dramatically suppressed. The observation and analyses that silica nanoparticles show different hydrolysis behaviors dependent on the location of the functional group will be important in future design of silica nanoparticles for specific biomedical and agricultural applications.

Condensable Vapor Sorption by Low Charge State Protein Ions.

Measurement of the gas-phase ion mobility of proteins provides a means to quantitatively assess the relative sizes of charged proteins. However, protein ion mobility measurements are typically singular values. Here, we apply tandem mobility analysis to low charge state protein ions (+1 and +2 ions) introduced into the gas phase by nanodroplet nebulization. We first determine protein ion mobilities in dry air and subsequently examine shifts in mobilities brought about by the clustering of vapor molecules. Tandem mobility analysis yields mobility-vapor concentration curves for each protein ion, expanding the information obtained from mobility analysis. This experimental procedure and analysis is extended to bovine serum albumin, transferrin, immunoglobulin G, and apoferritin with water, 1-butanol, and nonane. All protein ions appear to adsorb vapor molecules, with mobility "diameter" shifts of up to 6-7% at conditions just below vapor saturation. We parametrize results using κ-Köhler theory, where the term κ quantifies the extent of uptake beyond Köhler model expectations. For 1-butanol and nonane, κ decreases with increasing protein ion size, while it increases with increasing protein ion size for water. For the systems probed, the extent of mobility shift for the organic vapors is unaffected by the nebulized solution pH, while shifts with water are sensitive to pH.

Utility of COVID-19 antigen testing in the emergency department.

BACKGROUND: The BinaxNOW coronavirus disease 2019 (COVID-19) Ag Card test (Abbott Diagnostics Scarborough, Inc.) is a lateral flow immunochromatographic point-of-care test for the qualitative detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein antigen. It provides results from nasal swabs in 15 minutes. Our purpose was to determine its sensitivity and specificity for a COVID-19 diagnosis. METHODS: Eligible patients had symptoms of COVID-19 or suspected exposure. After consent, 2 nasal swabs were collected; 1 was tested using the Abbott RealTime SARS-CoV-2 (ie, the gold standard polymerase chain reaction test) and the second run on the BinaxNOW point of care platform by emergency department staff. RESULTS: From July 20 to October 28, 2020, 767 patients were enrolled, of which 735 had evaluable samples. Their mean (SD) age was 46.8 (16.6) years, and 422 (57.4%) were women. A total of 623 (84.8%) patients had COVID-19 symptoms, most commonly shortness of breath (n = 404; 55.0%), cough (n = 314; 42.7%), and fever (n = 253; 34.4%). Although 460 (62.6%) had symptoms ≤7 days, the mean (SD) time since symptom onset was 8.1 (14.0) days. Positive tests occurred in 173 (23.5%) and 141 (19.2%) with the gold standard versus BinaxNOW test, respectively. Those with symptoms >2 weeks had a positive test rate roughly half of those with earlier presentations. In patients with symptoms ≤7 days, the sensitivity, specificity, and negative and positive predictive values for the BinaxNOW test were 84.6%, 98.5%, 94.9%, and 95.2%, respectively. CONCLUSIONS: The BinaxNOW point-of-care test has good sensitivity and excellent specificity for the detection of COVID-19. We recommend using the BinasNOW for patients with symptoms up to 2 weeks.

Localized and Whole-Room Effects of Portable Air Filtration Units on Aerosol Particle Deposition and Concentration in a Classroom Environment.

In indoor environments with limited ventilation, recirculating portable air filtration (PAF) units may reduce COVID-19 infection risk via not only the direct aerosol route (i.e., inhalation) but also via an indirect aerosol route (i.e., contact with the surface where aerosol particles deposited). We systematically investigated the impact of PAF units in a mock classroom, as a supplement to background ventilation, on localized and whole-room surface deposition and particle concentration. Fluorescently tagged particles with a volumetric mean diameter near 2 µm were continuously introduced into the classroom environment via a breathing simulator with a prescribed inhalation-exhalation waveform. Deposition velocities were inferred on >50 horizontal and vertical surfaces throughout the classroom, while aerosol concentrations were spatially monitored via optical particle spectrometry. Results revealed a particle decay rate consistent with expectations based upon the reported clean air delivery rates of the PAF units. Additionally, the PAF units reduced peak concentrations by a factor of around 2.5 compared to the highest concentrations observed and led to a statistically significant reduction in deposition velocities for horizontal surfaces >2.5 m from the aerosol source. Our results not only confirm that PAF units can reduce particle concentrations but also demonstrate that they may lead to reduced particle deposition throughout an indoor environment when properly positioned with respect to the location of the particle source(s) within the room (e.g., where the largest group of students sit) and the predominant air distribution profile of the room.

Kinetics of nonisothermal phase change with arbitrary temperature-time history and initial transformed phase distributions.

This paper describes the extension of the classic Avrami equation to nonisothermal systems with arbitrary temperature-time history and arbitrary initial distributions of transformed phase. We start by showing that through examination of phase change in Fourier space, we can decouple the nucleation rate, growth rate, and transformed fraction, leading to the derivation of a nonlinear differential equation relating these three properties. We then consider a population balance partial differential equation (PDE) on the phase size distribution and solve it analytically. Then, by relating this PDE solution to the transformed fraction of phase, we are able to derive initial conditions to the differential equation relating nucleation rate, growth rate, and transformed fraction.

Improvement in Kansas City Cardiomyopathy Questionnaire Scores After a Self-Care Intervention in Patients With Acute Heart Failure Discharged From the Emergency Department.

BACKGROUND: We conducted a secondary analysis of changes in the Kansas City Cardiomyopathy Questionnaire (KCCQ)-12 over 30 days in a randomized trial of self-care coaching versus structured usual care in patients with acute heart failure who were discharged from the emergency department. METHODS: Patients in 15 emergency departments completed the KCCQ-12 at emergency department discharge and at 30 days. We compared change in KCCQ-12 scores between the intervention and usual care arms, adjusted for enrollment KCCQ-12 and demographic characteristics. We used linear regression to describe changes in KCCQ-12 summary scores and logistic regression to characterize clinically meaningful KCCQ-12 subdomain changes at 30 days. RESULTS: There were 350 patients with both enrollment and 30-day KCCQ summary scores available; 166 allocated to usual care and 184 to the intervention arm. Median age was 64 years (interquartile range, 55-70), 37% were female participants, 63% were Black, median KCCQ-12 summary score at enrollment was 47 (interquartile range, 33-64). Self-care coaching resulted in significantly greater improvement in health status compared with structured usual care (5.4-point greater improvement, 95% CI, 1.12-9.68; P=0.01). Improvements in health status in the intervention arm were driven by improvements within the symptom frequency (adjusted odds ratio, 1.62 [95% CI, 1.01-2.59]) and quality of life (adjusted odds ratio, 2.39 [95% CI, 1.46-3.90]) subdomains. CONCLUSIONS: In this secondary analysis, patients with acute heart failure who received a tailored, self-care intervention after emergency department discharge had clinically significant improvements in health status at 30 days compared with structured usual care largely due to improvements within the symptom frequency and quality of life subdomains of the KCCQ-12. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02519283.

Evidence-based aerosol clearance times in a healthcare environment.

BACKGROUND: As researchers race to understand the nature of COVID-19 transmission, healthcare institutions must treat COVID-19 patients while also safeguarding the health of staff and other patients. One aspect of this process involves mitigating aerosol transmission of the SARS-CoV2 virus. The U.S. Centers for Disease Control and Prevention (CDC) provides general guidance on airborne contaminant removal, but directly measuring aerosol clearance in clinical rooms provides empirical evidence to guide clinical procedure. AIM: We present a risk-assessment approach to empirically measuring and certifying the aerosol clearance time (ACT) in operating and procedure rooms to improve hospital efficiency while also mitigating the risk of nosocomial infection. METHODS: Rooms were clustered based on physical and procedural parameters. Sample rooms from each cluster were randomly selected and tested by challenging the room with aerosol and monitoring aerosolized particle concentration until 99.9% clearance was achieved. Data quality was analysed and aerosol clearance times for each cluster were determined. FINDINGS: Of the 521 operating and procedure rooms considered, 449 (86%) were issued a decrease in clearance time relative to CDC guidance, 32 (6%) had their clearance times increased, and 40 (8%) remained at guidance. The average clearance time change of all rooms assessed was a net reduction of 27.8%. CONCLUSION: The process described here balances the need for high-quality, repeatable data with the burden of testing in a functioning clinical setting. Implementation of this approach resulted in a reduction in clearance times for most clinical rooms, thereby improving hospital efficiency while also safeguarding patients and staff.

Combined Effects of Masking and Distance on Aerosol Exposure Potential.

OBJECTIVE: To quantify the efficacy of masking and "social distancing" on the transmission of airborne particles from a phantom aerosol source (simulating an infected individual) to a nearby target (simulating a healthy bystander) in a well-controlled setting. METHODS: An aerosol was created using monodisperse polystyrene latex beads in place of infectious respiratory secretions. Detection was by aerodynamic particle spectrometry. Both reusable cloth masks and disposable paper masks were studied. Transmission was simulated indoors during a 3-minute interval to eliminate the effect of variable ventilation rate on aerosol exposure. The study commenced on September 16, 2020, and concluded on December 15, 2020. RESULTS: Compared with a baseline of 1-foot separation with no masks employed, particle count was reduced by 84% at 3 feet of separation and 97% at 6 feet. A modest decrease in particle count was observed when only the receiver was masked. The most substantial exposure reduction occurred when the aerosol source was masked (or both parties were masked). When both the source and target were masked, particle count was reduced by more than 99.5% of baseline, regardless of separation distance or which type of mask was employed. CONCLUSION: These results support the principle of layered protection to mitigate transmission of SARS-CoV-2, the virus causing COVID-19, and other respiratory viruses and emphasize the importance of controlling the spread of aerosol at its source. The combination of masking and distancing reduced the exposure to exhaled particulates more than any individual measure. Combined measures remain the most effective way to combat the spread of respiratory infection.

Wind tunnel-based testing of a photoelectrochemical oxidative filter-based air purification unit in coronavirus and influenza aerosol removal and inactivation.

Recirculating air purification technologies are employed as potential means of reducing exposure to aerosol particles and airborne viruses. Toward improved testing of recirculating air purification units, we developed and applied a medium-scale single-pass wind tunnel test to examine the size-dependent collection of particles and the collection and inactivation of viable bovine coronavirus (BCoV, a betacoronavirus), porcine respiratory coronavirus (PRCV, an alphacoronavirus), and influenza A virus (IAV), by a commercial air purification unit. The tested unit, the Molekule Air Mini, incorporates a MERV 16 filter as well as a photoelectrochemical oxidating layer. It was found to have a collection efficiency above 95.8% for all tested particle diameters and flow rates, with collection efficiencies above 99% for supermicrometer particles with the minimum collection efficiency for particles smaller than 100 nm. For all three tested viruses, the physical tracer-based log reduction was near 2.0 (99% removal). Conversely, the viable virus log reductions were found to be near 4.0 for IAV, 3.0 for BCoV, and 2.5 for PRCV, suggesting additional inactivation in a virus family- and genus-specific manner. In total, this work describes a suite of test methods which can be used to rigorously evaluate the efficacy of recirculating air purification technologies.

Aggregation affects optical properties and photothermal heating of gold nanospheres.

Laser heating of gold nanospheres (GNS) is increasingly prevalent in biomedical applications due to tunable optical properties that determine heating efficiency. Although many geometric parameters (i.e. size, morphology) can affect optical properties of individual GNS and their heating, no specific studies of how GNS aggregation affects heating have been carried out. We posit here that aggregation, which can occur within some biological systems, will significantly impact the optical and therefore heating properties of GNS. To address this, we employed discrete dipole approximation (DDA) simulations, Ultraviolet-Visible spectroscopy (UV-Vis) and laser calorimetry on GNS primary particles with diameters (5, 16, 30 nm) and their aggregates that contain 2 to 30 GNS particles. DDA shows that aggregation can reduce the extinction cross-section on a per particle basis by 17-28%. Experimental measurement by UV-Vis and laser calorimetry on aggregates also show up to a 25% reduction in extinction coefficient and significantly lower heating (~ 10%) compared to dispersed GNS. In addition, comparison of select aggregates shows even larger extinction cross section drops in sparse vs. dense aggregates. This work shows that GNS aggregation can change optical properties and reduce heating and provides a new framework for exploring this effect during laser heating of nanomaterial solutions.