Microfluidics and Nanofluidics最新文献

Despite several decades of development, microfluidics lacks a sealing material that can be readily fabricated, leak-tight under high liquid water pressure, stable over a long time, and vacuum compatible. In this paper, we report the performances of a micro-scale processable sealing material for nanofluidic/microfluidics chip fabrication, which enables us to achieve all these requirements. We observed that micrometric walls made of SU-8 photoresist, whose thickness range from 35 to 135 µm, are at least leak-tight to 1.5 bars and up to 5.5 bars, exhibit no water porosity even after 2 months of aging, and are able to sustain under (10^{-5}) mbar vacuum. This sealing material is therefore reliable and versatile for building microchips, part of which must be isolated from liquid water under pressure or vacuum. Moreover, the fabrication process we propose does not require the use of either aggressive chemicals or high-temperature or high-energy plasma treatment. It thus opens a new perspective to seal microchips with sensitive surfaces containing nanomaterials.

Snap-off events are one of the most common and essential phenomena in two-phase flow in porous media. This paper uses the scanning results of a siltstone slice to construct a two-dimensional heterogeneous pore network structure to visualise microscopic snap-off phenomena and displacement processes accurately. The relationship between snap-off events and the non-wetting phase saturation was studied using two-phase flow displacement experiments. Results show that although the non-wetting phase snap-off events benefit freeing the trapped non-wetting phase in the microchannels, high-frequency snap-off events are the main reason for trapping the non-wetting phase during the displacement process, eventually leading to residuals. The frequency of non-wetting phase snap-off events in the pore network structure can be reduced to lower the non-wetting phase saturation and reduce the non-wetting phase residuals by increasing the displacement fluid viscosity, reducing the surface tension coefficient between the phases and increasing the flow rate.

In droplet microfluidic devices with suction-based flow control, the microchannel geometry and suction pressure at the outlet govern the dynamic properties of the two phases that influence the droplet generation. Therefore, it is critical to understand the role of geometry along with suction pressure in the dynamics of droplet generation to develop a predictive model. We conducted a comprehensive characterization of droplet generation in a flow focusing device with varying control parameters. We used these results to formulate a scaling argument and propose a governing parameter, called as modified capillary number (Ca_L), that combines normalized droplet volume with geometrical parameters (length of dispersed and continuous phase channels) and flow parameters (interfacial tension, phase viscosity and velocity) in a power law relationship. Ca_L effectively captures the transition from squeezing to dripping regimes of droplet generation, providing essential insights into the design requirements for suction-driven droplet generation. These findings are key to standardize microfluidic flow-focusing devices that can achieve the desired droplet generation behavior with optimal pressure consumption.

In rarefied gas dynamics scattering kernels deserve special attention since they contain all the essential information about the effects of physical and chemical properties of the gas–solid surface interface on the gas scattering process. However, to study the impact of the gas–surface interactions on the large-scale behavior of fluid flows, these scattering kernels need to be integrated in larger-scale models like Direct Simulation Monte Carlo (DSMC). In this work, the Gaussian mixture (GM) model, an unsupervised machine learning approach, is utilized to establish a scattering kernel for monoatomic (Ar) and diatomic ((hbox {H}_{2})) gases directly from Molecular Dynamics (MD) simulations data. The GM scattering kernel is coupled to a pure DSMC solver to study isothermal and non-isothermal rarefied gas flows in a system with two parallel walls. To fully examine the coupling mechanism between the GM scattering kernel and the DSMC approach, a one-to-one correspondence between MD and DSMC particles is considered here. Benchmarked by MD results, the performance of the GM-DSMC is assessed against the Cercignani–Lampis–Lord (CLL) kernel incorporated into DSMC simulation (CLL-DSMC). The comparison of various physical and stochastic parameters shows the better performance of the GM-DSMC approach. Especially for the diatomic system, the GM-DSMC outperforms the CLL-DSMC approach. The fundamental superiority of the GM-DSMC approach confirms its potential as a multi-scale simulation approach for accurately measuring flow field properties in systems with highly nonequilibrium conditions.

The microfluidic system is capable of recapitulating key attributes of in vivo circumstances and, therefore, becomes a valuable platform for better understanding tumor growth dynamics and evaluating drug efficiency. While numerical simulations have been envisioned as powerful tools for validating versatile performance of advanced microfluidic platforms, cell growth within these microchannels has not yet been theoretically modeled. In this paper, we developed an experimental data-driven cellular automaton model, which was adopted for simulating cell behaviors and drug responses in a microfluidic system. The boundaries of the cellular automata lattices and prohibited zones for simulation were directly converted from microscopic images of cell morphology and the microchamber configuration. The dynamic progression of tumor growth at the avascular stage was predicted by incorporating the biophysical and molecular characteristics of cells and their interactions with surrounding environment. The simulated proliferation rate of tumor cells over time demonstrated its dependency on nutrient delivery, aligning well with experimental observations in the microfluidic culture. The spatiotemporal efficacy of the chemotherapeutic compound doxorubicin (DOX) on the microfluidic culture was also simulated. The similarity between in silico simulations and in vitro tumor response upon drug interaction highlighted the potential of the computational models as complementary tools for predicting the drug treatment efficacy with acceptable accuracy before practical applications.

To enhance focusing performance, we proposed an integrated microchannel with expansion–contraction arrays (ECA) on the inner wall of the curved microchannel (CIECA) and compared it with a straight microchannel with ECA (SECA) as well as the traditional integrated microchannel of ECA on the outer wall of the curved channel (COECA). We investigated the particle-focusing mechanisms in these microchannels through a combination of experiments and numerical simulations. The proposed integrated microchannel demonstrates significant improvements in focusing performance compared to SECA and COECA, which is attributed to its consistent Dean flow. In contrast, COECA shows the poorest performance because of inconsistent Dean flow. The focusing width in the proposed integrated microchannel is reduced to 1/3 of that in COECA and 1/2 of that in SECA. Furthermore, the focusing performance of CIECA improves as the Reynolds number increases, eventually forming a single trajectory when the Reynolds number (at contraction) reaches 83.33. Finally, the impact of particle size on focusing performance was investigated through numerical simulations. The focusing performance of the CIECA is the best in these three microchannels. In CIECA, as the particle size increases, the focusing width initially decreases and then increases. Among them, 8 and 10 μm particles can achieve complete focusing. This study serves as a crucial reference for comprehending and enhancing particle focusing through the synergy of multi-Dean flow.

Pulsed thermal energy causes piecewise actuation of a nitinol cantilever providing the mechanical force required to evacuate a chamber constructed of parylene C. This proof-of-principle micropump demonstrates an alternative to typical evacuation and rectification methods utilized in most micropumps. The chamber and normally closed channels that serve as valves are all of parylene C construction, leading to the flexibility of the device. The nitinol cantilever functions as an actuator capable of yielding successive partial chamber evacuations until achieving complete evacuation. Piecewise shape recovery of the actuator was made viable by implementing a Peltier device, providing the means for supplying responsive and controlled thermal energy. Experiments delivered measurements of consecutive advancement of shape recovery using a laser displacement sensor while monitoring the temperature with fiber-optic sensors. The release of a saturated lithium chloride solution from the pump was monitored by observing conductivity changes in the experimental area. Theoretically predicting a release amount used calculations for the expected recovery of the actuator based on displacement characterization via a logistic curve fit against actuator temperature data. The measured release amounts correlated well with the theoretically predicted values made using the temperature values obtained near the device during the release. These works provide novel approaches to micropump fabrication and implementation and new strategies for predicting the recovery of shape memory alloys. The micropump concepts are viable in many fields, such as biomedical applications: in vivo drug delivery, organ-on-chip, and lab-on-chip devices, to name a few. Likewise, a simple prediction for nitinol recovery has vast potential.

This study introduces a practical approach utilizing microfluidic trap and mixer modules fabricated with polydimethylsiloxane (PDMS) microfluidic devices. These modules were employed to capture and fluorescently label various randomly shaped microplastics (MPs) like polyethylene (PE), polypropylene (PP), and polystyrene (PS). Within the MPs trap module, grooves were incorporated into a straight-lined channel using SU-8 photolithography. This design induced turbulence effectively trapping and gathering the MPs within aqueous phases at 15 groove spaces, which achieved a trapping efficiency of up to 69% for PS MPs sized at a flow rate of 2 mL/min. Additionally, a mixer module featuring two flow inlets was designed to create a serpentine microfluidic channel, whose design significantly reduced sample and reagent (Nile Red) consumption during MP fluorescence staining at 80 °C. Furthermore, 2 nm gold nanoparticles (Au NPs), conjugated with a PS binding peptide (PSBP), were examined as an alternative fluorescent agent at room temperature. Photoluminescence (PL) and Fourier transform infrared (FT-IR) showcased efficiency of mixer module in labeling 30 mL MP solutions within a short time of 15 min. Moreover, a combined platform integrating trap and mixer devices was devised, incorporating a disposable heating pad and filter paper unit, which offers a simplified and compact MPs staining tool including spherical PE nanoplastics (200 nm–99 μm). This study aims to propose a preliminary concept for a lab-on-a-chip, facilitating the simultaneous collection and fluorescent labeling, which can be instrumentally implemented in future MPs monitoring.

Sharp edge structures have been demonstrated as an efficient way of generating acoustic streaming in microfluidic devices, which finds numerous applications in fluid mixing, pumping, particle actuation, and cell lysis. A sharp tip capillary is widely available means of generating sharp structures without the need of microfabrication, which has been used for studying enzyme kinetics, droplet digital PCR, and mass spectrometry analysis. In this work, we studied the influence of liquid inside the vibrating glass capillary on its efficiency of generating acoustic streaming. Using fluorescence microscopy and fluorescent particles, we observed that adding liquid to the inside of the vibrating glass capillary changed the streaming patterns as well as led to increased streaming velocity. Based on the observed streaming patterns, we hypothesized the liquid present in the capillary changed vibration mode of the capillary, which is matched with COMSOL simulations. Finally, the utility of the liquid filled vibrating capillary was demonstrated for higher energy efficiency for fluid mixing and mass spectrometry experiments. This study will provide useful guidance when optimizing the efficiency of vibrating sharp tip capillary systems.

Kinetic equations are crucial for modeling non-equilibrium phenomena, but their computational complexity is a challenge. This paper presents a data-driven approach using reduced order models (ROM) to efficiently model non-equilibrium flows in kinetic equations by comparing two ROM approaches: proper orthogonal decomposition (POD) and autoencoder neural networks (AE). While AE initially demonstrate higher accuracy, POD’s precision improves as more modes are considered. Notably, our work recognizes that the classical POD model order reduction approach, although capable of accurately representing the non-linear solution manifold of the kinetic equation, may not provide a parsimonious model of the data due to the inherently non-linear nature of the data manifold. We demonstrate how AEs are used in finding the intrinsic dimension of a system and to allow correlating the intrinsic quantities with macroscopic quantities that have a physical interpretation.