Environ Sci Pollut Res Int
May 2024
Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability.
View Article and Find Full Text PDFMicrofluidic microparticle manipulation is currently widely used in environmental, bio-chemical, and medical applications. Previously we proposed a straight microchannel with additional triangular cavity arrays to manipulate microparticles with inertial microfluidic forces, and experimentally explored the performances within different viscoelastic fluids. However, the mechanism remained poorly understood, which limited the exploration of the optimal design and standard operation strategies.
View Article and Find Full Text PDFTraditionally, comprehensive laboratorial experiments on newly proposed microfluidic devices are necessary for theoretical validation, technological design, methodological calibration and optimization. Multiple parameters and characteristics, such as the flow rate, particle size, microchannel dimensions, , should be studied by controlled trials, which could inevitably result in extensive experiments and a heavy burden on researchers. In this work, a novel numerical model was introduced to simulate particle migration within a complicated double-layered microchannel.
View Article and Find Full Text PDFBiomicroparticles such as proteins, bacterium, and cells are known to be viscoelastic, which significantly affects their performance in microfluidic applications. However, the exact effects and the quantitative study of cellular viscoelastic creep within different applications remain unclear. In this study, the cellular-deforming evolution within a filter unit was studied using a multiphysics numerical model.
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