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This study presents numerical simulations of the convective heat transfer on wavy microchannels to investigate heat transfer enhancement in these systems. The objective is to propose a methodology based on local and global energy balances in the device, instead of the commonly used Nusselt number, as an alternative for the thermal analysis. This investigation is carried out on a single-wave microchannel model of size 0.5 mm by 0.5 mm by 20 mm length, with water flowing inside the channel, exposed to a heat influx of 47 W/cm2 at the bottom. The governing equations for an incompressible laminar flow and conjugate heat transfer are first built, and then solved, for representative models, with copper as the solid-block material under a number of operating conditions (cold-water flowrates of 𝑅𝑒=50, 100, and 150), by the finite element technique. From computed velocity, pressure and temperature fields, local and global energy balances based on cross-section-averaged velocities and temperatures enable calculating the heat rate at each section of the corresponding device. Results from this study for two different designs, namely, serpentine and divergent-convergent layouts, show that this so-called averaged energy-balance methodology enables higher accuracy than that based on Nusselt numbers since neither transfer coefficients nor characteristic temperatures are needed. 

Numerous studies have linked a wide range of diseases including respiratory illnesses to harmful particulate matter (PM) emissions indoors and outdoors, such as incense PM and industrial PM. Because of their ability to penetrate the lower respiratory tract and the circulatory system, fine particles with diameters of 2.5 µm or less (PM2.5) are believed to be more hazardous than larger PMs. Despite the enormous number of studies focusing on the intracellular processes associated with PM2.5 exposure, there have been limited reports studying the biophysical properties of cell membranes, such as nanoscale morphological changes induced by PM2.5. Our study assesses the membrane topographical and structural effects of PM2.5 from incense PM2.5 exposure in real time on A549 lung carcinoma epithelial cells and SH-SY5Y neuroblastoma cells that had been fixed to preclude adaptive cell responses. The size distribution and mechanical properties of the PM2.5 sample were characterized with atomic force microscopy (AFM). Nanoscale morphological monitoring of the cell membranes utilizing scanning ion conductance microscopy (SICM) indicated statistically significant increasing membrane roughness at A549 cells at half an hour of exposure and visible damage at 4 h of exposure. In contrast, no significant increase in roughness was observed on SH-SY5Y cells after half an hour of PM2.5 exposure, although continued exposure to PM2.5 for up to 4 h affected an expansion of lesions already present before exposure commenced. These findings suggest that A549 cell membranes are more susceptible to structural damage by PM2.5 compared to SH-SY5Y cell membranes, corroborating more enhanced susceptibility of airway epithelial cells to exposure to PM2.5 than neuronal cells. SICM · Particulate matter · Membrane topography · Single-cell imaging 

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.