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Steven N. Rogak

Publications and source records attributed to Steven N. Rogak.

2 recordsLinked to original sources

The effect of air purifiers and curtains on aerosol dispersion and removal in multi-patient hospital rooms

Airborne transmission of disease is of concern in many indoor spaces. Here, aerosol dispersion and removal in an unoccupied 4-bed hospital room was characterized using a transient aerosol tracer experiment for 38 experiments covering 4 configurations of air purifiers and 3 configurations of curtains. NaCl particle (mass mean aerodynamic diameter $\sim 3 μm$) concentrations were measured around the room following an aerosol release. Particle transport across the room was 1.5 - 4 minutes which overlaps with the characteristic times for significant viral deactivation and gravitational settling of larger particles. Concentrations were close to spatially uniform except very near the source. Short curtains had no consistent effects on concentrations at the non-source patient locations while floor-length curtains reduced concentrations slightly depending on the purifier configuration. The aerosol decay rate was in most cases higher than expected from the clean air delivery rate, but the reduction in steady-state concentrations resulting from air purifiers was less than suggested by the decay rates. Apparently a substantial (and configuration-dependent) fraction of the aerosol is removed immediately and this effect is not captured by the decay rate. Overall, the combination of curtains and purifiers is likely to reduce disease transmission in multipatient hospital rooms.

physics.med-ph

Characterizing soot in TEM images using a convolutional neural network

Soot is an important material with impacts that depend on particle morphology. Transmission electron microscopy (TEM) represents one of the most direct routes to qualitatively assess particle characteristics. However, producing quantitative information requires robust image processing tools, which is complicated by the low image contrast and complex aggregated morphologies characteristic of soot. The current work presents a new convolutional neural network explicitly trained to characterize soot, using pre-classified images of particles from a natural gas engine; a laboratory gas flare; and a marine engine. The results are compared against other existing classifiers before considering the effect that the classifiers have on automated primary particle size methods. Estimates of the overall uncertainties between fully automated approaches of aggregate characterization range from 25% in d_{p,100} to 85% in D_{TEM}. A consistent correlation is observed between projected-area equivalent diameter and primary particle size across all of the techniques.

physics.app-ph