A mixture theory model of fluid and solute transport in the microvasculature of normal and malignant tissues. I. Theory.

In order to better understand the mechanisms governing transport of drugs, nanoparticle-based treatments, and therapeutic biomolecules, and the role of the various physiological parameters, a number of mathematical models have previously been proposed. The limitations of the existing transport models indicate the need for a comprehensive model that includes transport in the vessel lumen, the vessel wall, and the interstitial space and considers the effects of the solute concentration on fluid flow. In this study, a general model to describe the transient distribution of fluid and multiple solutes at the microvascular level was developed using mixture theory. The model captures the experimentally observed dependence of the hydraulic permeability coefficient of the capillary wall on the concentration of solutes present in the capillary wall and the surrounding tissue. Additionally, the model demonstrates that transport phenomena across the capillary wall and in the interstitium are related to the solute concentration as well as the hydrostatic pressure. The model is used in a companion paper to examine fluid and solute transport for the simplified case of an axisymmetric geometry with no solid deformation or interconversion of mass.

A mixture theory model of fluid and solute transport in the microvasculature of normal and malignant tissues. II: Factor sensitivity analysis, calibration, and validation.

The treatment of cancerous tumors is dependent upon the delivery of therapeutics through the blood by means of the microcirculation. Differences in the vasculature of normal and malignant tissues have been recognized, but it is not fully understood how these differences affect transport and the applicability of existing mathematical models has been questioned at the microscale due to the complex rheology of blood and fluid exchange with the tissue. In addition to determining an appropriate set of governing equations it is necessary to specify appropriate model parameters based on physiological data. To this end, a two stage sensitivity analysis is described which makes it possible to determine the set of parameters most important to the model's calibration. In the first stage, the fluid flow equations are examined and a sensitivity analysis is used to evaluate the importance of 11 different model parameters. Of these, only four substantially influence the intravascular axial flow providing a tractable set that could be calibrated using red blood cell velocity data from the literature. The second stage also utilizes a sensitivity analysis to evaluate the importance of 14 model parameters on extravascular flux. Of these, six exhibit high sensitivity and are integrated into the model calibration using a response surface methodology and experimental intra- and extravascular accumulation data from the literature (Dreher et al. in J Natl Cancer Inst 98(5):335-344, 2006). The model exhibits good agreement with the experimental results for both the mean extravascular concentration and the penetration depth as a function of time for inert dextran over a wide range of molecular weights.

Effects of feed gas composition and catalyst thickness on carbon nanotube and nanofiber synthesis by plasma enhanced chemical vapor deposition.

Many engineering applications require carbon nanotubes with specific characteristics such as wall structure, chirality and alignment. However, precise control of nanotube properties grown to application specifications remains a significant challenge. Plasma-enhanced chemical vapor deposition (PECVD) offers a variety of advantages in the synthesis of carbon nanotubes in that several important synthesis parameters can be controlled independently. This paper reports an experimental study of the effects of reacting gas composition (percentage methane in hydrogen) and catalyst film thickness on carbon nanotube (CNT) growth and a computational study of gas-phase composition for the inlet conditions of experimentally observed carbon nanotube growth using different chemical reaction mechanisms. The simulations seek to explain the observed effects of reacting gas composition and to identify the precursors for CNT formation. The experimental results indicate that gas-phase composition significantly affects the synthesized material, which is shown to be randomly aligned nanotube and nanofiber mats for relatively methane-rich inlet gas mixtures and non-tubular carbon for methane-lean incoming mixtures. The simulation results suggest that inlet methane-hydrogen mixture coverts to an acetylene-methane-hydrogen mixture with minor amounts of ethylene, hydrogen atom, and methyl radical. Acetylene appears to be the indicator species for solid carbon formation. The simulations also show that inlet methane-hydrogen mixture does not produce enough gas-phase precursors needed to form quality CNTs below 5% CH4 concentrations in the inlet stream.

Detection of Escherichia coli O157:H7 and Salmonella typhimurium using filtration followed by Fourier-transform infrared spectroscopy.

Fourier-transform infrared spectroscopy has been successfully used as a nondestructive method for identifying, distinguishing, and classifying pathogens. In this study, a less time-consuming Fourier-transform infrared procedure was developed to identify Escherichia coli O157:H7 and Salmonella Typhimurium. Samples containing 10(9) CFU/ml were prepared in tryptic soy broth and then serially diluted (up to eight times) to obtain bacterial solutions of 10(9) to 10 CFU/ml. These dilutions were incubated at 37 degrees C for 6 h, samples were filtered through a Metricel filter hourly (for 0 to 6 h), and spectra were obtained using a ZnSe contact attenuated total reflectance accessory on a Continu mum infrared microscope. Midinfrared spectra (4,000 to 700 cm(-1)) of Salmonella Typhimurium and E. coli O157:H7 were generated, and peak areas in the region of 1,589 to 1,493 cm(-1) were used to detect the pathogens. Initially, detection limits were between 10(6) and 10(7) CFU/ml without preenrichment, and samples starting with 500 CFU/ml were detectable following incubation for 6 h, when counts reached at least 10(6) CFU/ ml. Compared with results of previously published studies in which Fourier-transform infrared spectroscopy was used to identify select pathogens, this method is more rapid and less expensive for practical large-scale sample analysis.