Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes.
View Article and Find Full Text PDFBackground: Bronchoscopy is one of the most accurate procedures to diagnose airway stenosis which is an invasive procedure. However, a quick and noninvasive estimation of the percent area of obstruction (%AO) of the lumen is helpful in decision-making before performing a bronchoscopy procedure. We hypothesized that there is a relationship between %AO and tracheal resistance against fluid flow.
View Article and Find Full Text PDFWith a mortality rate over 580,000 per year, cancer is still one of the leading causes of death worldwide. However, the emerging field of microfluidics can potentially shed light on this puzzling disease. Unique characteristics of microfluidic chips (also known as micro-total analysis system) make them excellent candidates for biological applications.
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