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Microdialysis is a well-established method for in vivo neurochemical measurements of small molecules, with implanted concentric-design probes offering minimized tissue damage and good temporal and spatial resolution. However, the large…
Micro/nanoporous membranes have been used extensively in biological and medical applications associated with filtration, particle sorting, cell separation, and real-time sensing. Expanding its applications to microfluidics offers several…
Microfluidic channels are integral to biomedical technology and process engineering, offering versatility in handling fluids with complex properties, often a combination of viscous and elastic attributes. Despite significant advancements in…
This paper presents a new boundary integral equation (BIE) method for simulating particulate and multiphase flows through periodic channels of arbitrary smooth shape in two dimensions. The authors consider a particular system---multiple…
Computational fluid dynamics (CFD) is increasingly used to study blood flows in patient-specific arteries for understanding certain cardiovascular diseases. The techniques work quite well for relatively simple problems, but need…
We reveal an unreported physical mechanism that enables the pre-concentration, sorting and characterization of charged polystyrene nanobeads and liposomes dispersed in a continuous flow within a straight micron-sized channel. Initially, a…
Novel experimental modalities acquire spatially resolved velocity measurements for steady state and transient flows which are of interest for engineering and biological applications. One of the drawbacks of such high resolution velocity…
Small volume fluid handling in single and multiphase microfluidics provides a promising strategy for efficient bio-chemical assays, low-cost point-of-care diagnostics and new approaches to scientific discoveries. However multiple barriers…
Dielectrophoresis is an electric field-based technique for moving neutral particles through a fluid. When used for particle separation, dielectrophoresis has many advantages compared to other methods, providing label-free operation with…
This work describes a new microfluidic device developed for rapid screening of solubility diagrams. In several parallel channels, hundreds of nanoliter-volume droplets of a given solution are first stored with a gradual variation in the…
All-electronic interrogation of biofluid flow velocity by sensors incorporated in ultra-low-power or self-sustained systems offers the promise of enabling multifarious emerging research and applications. Electrical sensors based on…
We reconstruct the velocity field of incompressible flows given a finite set of measurements. For the spatial approximation, we introduce the Sparse Fourier divergence-free (SFdf) approximation based on a discrete $L^2$ projection. Within…
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully…
Decomposing a flow on a Directed Acyclic Graph (DAG) into a weighted sum of a small number of paths is an essential task in operations research and bioinformatics. This problem, referred to as Sparse Flow Decomposition (SFD), has gained…
With the aim to parallelize and monitor biological or biochemical phenomena, trapping and immobilization of objects such as particles, droplets or cells in microfluidic devices has been an intense area of research and engineering so far.…
Diffusion models achieve strong generative performance but often rely on large datasets that may include sensitive content. This challenge is compounded by the models' tendency to memorize training data, raising privacy concerns. SFBD (Lu…
Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications. In recent years deep learning (DL) has been used to construct data-driven fluid-dynamic…
Preconditioning is a key component of MCMC algorithms that improves sampling efficiency by facilitating exploration of geometrically complex target distributions through an invertible map. While linear preconditioners are often sufficient…
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to…
We present a 2D parallel implementation of the modified Cahn-Hilliard equation for the simulation of a biofilm in an aqueous enviroment. Biofilms are attached microbial communities made of many different components and can have both…