Search

The rheological characterization of complex liquids is of great importance in many applications. Among the properties that can be measured, the relaxation time has great relevance, as it provides a measure of fluid elasticity. In this work, we propose a novel method to estimate the longest relaxation time of viscoelastic fluids by applying machine learning to microfluidics. Specifically, we train a long-short term memory (LSTM) neural network to identify the Weissenberg number that characterizes the dynamics of trains of rigid particles suspended in a viscoelastic liquid flowing in a cylindrical microchannel. We first study the effect of the Weissenberg number on the evolution of the microstructure through numerical simulations. An in silico dataset consisting of the distributions of the interparticle distances at different channel sections is built and used to train the network. The performance of the LSTM model is tested on both classification and regression problems. The proposed method is nonintrusive, requires a simple setup, and can in principle be used to measure other properties of the fluid. [ABSTRACT FROM AUTHOR]

When cells in a suspension flow through a microfluidic channel and rotate within the field of view (FOV) of a holographic microscope, they become accessible to a light beam from various angles. This allows the retrieval of a three-dimensional refractive index map for each flowing cell, essentially a 3D phase-contrast tomogram. Understanding the effects of hydrodynamic interactions among cells on their rotational behaviour during flow is crucial for designing microfluidic devices for holographic imaging. In this study, we employ direct numerical simulations to investigate the dynamics of cell clusters suspended in a Newtonian liquid under pressure-driven flow within a microfluidic channel, with the aim of clarifying the influence of hydrodynamic interactions on cell rotation. [ABSTRACT FROM AUTHOR]

The manipulation and control of particles in microfluidic devices through non-intrusive methods is pivotal in many application fields, e.g., cell focusing and sorting. Inertial microfluidics is rapidly gaining attention in the scientific community because of the considerable advantages in terms of throughput. In addition to inertia, other factors can trigger the cross-stream migration of particles in liquids undergoing pressure-driven channel flows, such as the deformability of the particles themselves and/or the viscoelasticity of the carrier fluid. For this reason, the dynamics of an initially spherical elastic particle suspended in a viscoelastic liquid subjected to pressure-driven flow in a cylindrical channel at non-negligible inertia is studied through three-dimensional arbitrary Lagrangian–Eulerian finite-element numerical simulations. The mechanical behavior of the particle is described through the neo-Hookean hyper-elastic constitutive equation, whereas the rheological behavior of the carrier liquid is described through the Giesekus model. The Reynolds number Re, measuring the relative importance of inertial and viscous forces in the tube, the elastic capillary number Ca e , measuring the relative importance of liquid viscous stress and solid elastic stress, and the Deborah number De, measuring the ratio of the liquid relaxation time and the flow characteristic time, are varied. The particle migrates transversally to the flow direction until reaching a radial equilibrium position depending on Re, Ca e , and De. Different dynamics are observed depending on the interplay among inertia and elasticity of both the liquid and the solid phase: one, two, or even three stable equilibrium positions can be found along the tube radius. [ABSTRACT FROM AUTHOR]

The possibility to investigate cells in microfluidic flow by using a full 3D morphometry analysis is highly demanded to achieve information about their healthiness. Recently, the tomographic flow cytometry by digital holography has been demonstrated to monitor red blood cells in microfluidics environment by simply applying flux pressure to induce random self-rotation of flowing cells. Here, we provide a microfluidic solution to engineer the flow with the aim to ensure the full 360 degree of angle rotation of all cells in the field of view. We test the proposed methods for circulating tumour cells.

Suspensions carrying deformable inclusions are ubiquitous in nature and applications. Hence, high-throughput characterization of the mechanical properties of soft particles is of great interest. Recently, a non-invasive optofluidic technique has been developed for the measurement of the interfacial tension between two immiscible liquids [8]. We have adapted such technique to the case of soft solid beads, thus designing a non-invasive optofluidic device for the measurement of the mechanical properties of deformable particles from real-time optical imaging of their deformation. The device consists of a cylindrical microfluidic channel with a cross-section reduction in which we make initially spherical soft beads flow suspended in a Newtonian carrier. By imaging the deformation of a particle in real time while it goes through the constriction, it is possible to get a measure of its elastic modulus through a theoretically derived-correlation. We provide both experimental and numerical validation of our device.