Accurate and meaningful dose metrics are a basic requirement for in vitro screening to assess potential health risks of engineered nanomaterials (ENMs). Correctly and consistently quantifying what cells “see,” during an in vitro exposure requires standardized preparation of stable ENM suspensions, accurate characterizatoin of agglomerate sizes and effective densities, and predictive modeling of mass transport. Earlier transport models provided a marked improvement over administered concentration or total mass, but included assumptions that could produce sizable inaccuracies, most notably that all particles at the bottom of the well are adsorbed or taken up by cells, which would drive transport downward, resulting in overestimation of deposition. Here we present development, validation and results of two robust computational transport models. Both three-dimensional computational fluid dynamics (CFD) and a newly-developed one-dimensional Distorted Grid (DG) model were used to estimate delivered dose metrics for industry-relevant metal oxide ENMs suspended in culture media. Both models allow simultaneous modeling of full size distributions for polydisperse ENM suspensions, and provide deposition metrics as well as concentration metrics over the extent of the well. The DG model also emulates the biokinetics at the particle-cell interface using a Langmuir isotherm, governed by a user-defined dissociation constant, K D, and allows modeling of ENM dissolution over time. Dose metrics predicted by the two models were in remarkably close agreement. The DG model was also validated by quantitative analysis of flash-frozen, cryosectioned columns of ENM suspensions. Results of simulations based on agglomerate size distributions differed substantially from those obtained using mean sizes. The effect of cellular adsorption on delivered dose was negligible for K D values consistent with non-specific binding (> 1 nM), whereas smaller values (≤ 1 nM) typical of specific high-affinity binding resulted in faster and eventual complete deposition of material. The advanced models presented provide practical and robust tools for obtaining accurate dose metrics and concentration profiles across the well, for high-throughput screening of ENMs. The DG model allows rapid modeling that accommodates polydispersity, dissolution, and adsorption. Result of adsorption studies suggest that a reflective lower boundary condition is appropriate for modeling most in vitro ENM exposures.

Computational fluid dynamics (CFD) is an effective analysis method of personalized ventilation (PV) in indoor built environments. As an increasingly important supplement to experimental and theoretical methods, the quality of CFD simulations must be maintained through an adequately controlled numerical modeling process. CFD numerical data can explain PV performance in terms of inhaled air quality, occupants’ thermal comfort, and building energy savings. Therefore, this paper presents state-of-the-art CFD analyses of PV systems in indoor built environments. The results emphasize the importance of accurate thermal boundary conditions for computational thermal manikins (CTMs) to properly analyze the heat exchange between human body and the microenvironment, including both convective and radiative heat exchange. CFD modeling performance is examined in terms of effectiveness of computational grids, convergence criteria, and validation methods. Additionally, indices of PV performance are suggested as system-performance evaluation criteria. A specific utilization of realistic PV air supply diffuser configurations remains a challenging task for further study. Overall, the adaptable airflow characteristics of a PV air supply provide an opportunity to achieve better thermal comfort with lower energy use based on CFD numerical analyses.