Real-time release of pharmaceuticals (small molecules and biologics) requires the ability to use in-process data to evaluate and ensure the quality of the final product (Shintani, 2016). Within biologics, determining sterility and measuring microbial contamination are especially important (Jiang et al., 2017). Traditional United States Pharmacopeia microbial testing methods depend primarily on the culturing of microorganisms to determine bioburden and sterility (England et al., 2019; Shintani, 2016). Since culturing and culture-dependent methods are slow (1-21 days), they cannot be used for real-time release testing. Nucleic acid-based technologies (polymerase

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In this paper, we study the dynamics of deformable cells in a channel flow of Newtonian and polymeric fluids and unravel the effects of deformability, elasticity, inertia, and size on the cell motion. We investigate the role of polymeric fluids on the cell migration behavior and the performance of inertial microfluidic devices. Our results show that the equilibrium position of the cell is on the channel diagonal, in contrast to that of rigid particles, which is on the center of the channel faces for the same range of Reynolds number. A constant-viscosity polymeric fluid, modeled using an Oldroyd-B constitutive equation, drives the cells toward the channel centerline, while a shear-thinning polymeric fluid, modeled using a Giesekus constitutive equation, pushes the cells toward the channel wall. The findings of this paper suggest that the addition of polymers in microfluidic devices can be used to enhance the throughput of cell focusing and separation devices at a low cost. This study provides an insight on the role of rheological properties of the fluid and the ways that they can be tuned to control the focal position of the cells.

Thermal performance of pulsating heat pipes (PHPs) is dependent to several factors. Inner and outer diameter of tube, filling ratio, thermal conductivity, heat input, inclination angle, and length of each section are the most influential factors in the design process of PHPs. Since water is a conventional working fluid for PHPs, thermal resistance and effective thermal conductivity of PHPs filled with water are modeled by applying a GMDH (group method of data handling) neural network. The input data of the GMDH model are collected from other experimental investigations to predict the physical properties including thermal resistance and effective thermal conductivity of PHPs filled with water as working fluid. The accuracy of the introduced models are examined through the R 2 tests and resulted in 0.9779 and 0.9906 for thermal resistance and effective thermal conductivity, respectively.

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