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Here, we describe the generation of dynamic vortices in micro-scale cavities at low flow rates. The system utilizes a computer-controlled audio speaker to axially oscillate the inlet tube of the microfluidic system at desired frequencies and amplitudes. The oscillation of the tube induces transiently high flow rates in the system, which facilitates the generation of dynamic vortices inside the cavity. The size of the vortices can be modulated by varying the tube oscillation frequency or amplitude. The vortices can be generated in single or serial cavities and in a wide range of cavity sizes. We demonstrate the suitability of the tube oscillation mechanism for the pulsed injection of water-based solutions or whole blood into the cavity. The injection rate can be controlled by the oscillation characteristics of the tube, enabling the injection of liquids at ultralow flow rates. The dynamic vortices facilitate the rapid mixing of the injected liquid with the main flow. The controllability and versatility of this technology allow for the development of programmable inertial microfluidic systems for performing multistep biological assays.

Here, the generation of dynamic vortices across microscale barriers using the tube oscillation mechanism is demonstrated. Using a combination of high-speed imaging and computational flow dynamics, the cyclic formation, expansion, and collapse of vortices are studied. The dynamics of vortices across circular , triangular, and blade-shape barriers are investigated at different tube oscillation frequencies. The formation of an array of synchronous vortices across parallel blade-shaped barriers is demonstrated. The transient flows caused by these dynamic vortex arrays are harnessed for the rapid and efficient mixing of blood samples . A circular barrier scribed with a narrow orifice on its shoulder is used to facilitate the injection of liquid into the microfluidic channel, and its rapid mixing with the main flow through the dynamic vortices generated across the barrier. This approach facilitates the generation of vortices with desirable configurations, sizes, and dynamics in a highly controllable, programmable, and predictable manner while operating at low static flow rates.

How endothelial cells sense and respond to dynamic changes in their biophysical surroundings as we age is not fully understood. Vascular stiffness is clearly a contributing factor not only in several cardiovascular diseases but also in physiological processes such as aging and vascular dementia. To address this gap, we utilized a microfluidic model to explore how substrate stiffness in the presence of shear stress affects endothelial morphology, senescence, proliferation, and inflammation. We also studied the role of mechanosensitive ion channel Piezo1 in endothelial responses under the combined effect of shear stress and substrate stiffness. To do so, we cultured endothelial cells inside microfluidic channels covered with fibronectin-coated elastomer with elastic moduli of 40 and 200 kPa, respectively, mimicking the stiffness of the vessel walls in young and aged arteries. The endothelial cells were exposed to atheroprotective and atherogenic shear stress levels of 10 and 2 dyn/cm2, respectively. Our findings show that substrate stiffness affects senescence under atheroprotective flow conditions and cytoskeleton remodeling, senescence, and inflammation under atherogenic flow conditions. Additionally, we found that the expression of Piezo1 plays a crucial role in endothelial adaptation to flow and regulation of inflammation under both atheroprotective and atherogenic shear stress levels. However, Piezo1 contribution to endothelial senescence was limited to the soft substrate and atheroprotective shear stress level. Overall, our study characterizes the response of endothelial cells to the combined effect of shear stress and substrate stiffness and reveals a previously unidentified role of Piezo1 in endothelial response to vessel stiffening, which potentially can be therapeutically targeted to alleviate endothelial dysfunction in aging adults.

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

2D materials with high in‐plane anisotropy are rapidly emerging as a tantalizing class of nanomaterials with promising applications in nanoelectronics and optoelectronics since they provide an additional degree of freedom that can be exploited in device design. The large‐area synthesis of such materials remains however challenging since the anisotropic crystal structure renders identifying a suitable growth substrate to be difficult, while the nanosheets are usually too fragile for the exfoliation and transfer of macroscopic sheets. This work reports the scalable synthesis of highly crystalline, large‐area 2D Bi2S3 nanosheets using a novel liquid‐metal‐based synthesis approach. Ultrathin bismuth oxide sheets are exfoliated from molten bismuth followed by tube furnace sulfurization. The strategy effectively separates the formation of layered structures from the process of anisotropic crystallization, overcoming the shortcomings of established techniques. The synthesized nanosheets feature a highly anisotropic orthorhombic crystal structure with intraplane van der Waals gaps and a direct bandgap of ≈2.3 eV. The nanosheets are found to be highly photoconductive with a photoresponsivity of 8 A W−1. Bi2S3 channel‐based field effect transistors feature a maximum hole mobility of 28 cm2 V−1 s−1, highlighting the excellent electronic properties of the isolated nanosheets.

Generation of tunable harmonic flows at low cost in microfluidic systems is a persistent and significant obstacle to this field, substantially limiting its potential to address major scientific questions and applications. This work introduces a simple and elegant way to overcome this obstacle. Harmonic flow patterns can be generated in microfluidic structures by simply oscillating the inlet tubes. Complex rib and vortex patterns can be dynamically modulated by changing the frequency and magnitude of tube oscillation and the viscosity of liquid. Highly complex rib patterns and synchronous vortices can be generated in serially connected microfluidic chambers. Similar dynamic patterns can be generated using whole or diluted blood samples without damaging the sample. This method offers unique opportunities for studying complex fluids and soft materials, chemical synthesis of various compounds, and mimicking harmonic flows in biological systems using compact, tunable, and low-cost devices.

Here, we show that long-term exposure of PDMS based microfluidic droplet generation systems to water can reverse their characteristics such that they generate oil-in-water droplets instead of water-in-oil droplets. The competition between two oil columns entering via the two side channels leads to asynchronous generation of oil droplets. We identify various modes of droplet generation, and study the size, gap and generation rate of droplets under different combinations of oil and water pressures. Oil droplets can also be generated using syringe pumps, various oil viscosities, and different combinations of immiscible liquids. We also demonstrate the ability to dynamically change the gap between the oil droplets from a few hundred microns to just a few microns in successive cycles using a latex balloon pressure pump. This method requires no special equipment or chemical treatments, and importantly can be reversed by long-term exposure of the PDMS surfaces to the ambient air.

TRPV4 is a non-selective cation channel that tunes the function of different tissues including the vascular endothelium, lung, chondrocytes, and neurons. GSK1016790A is the selective and potent agonist of TRPV4 and a pharmacological tool that is used to study the TRPV4 physiological function in vitro and in vivo. It remains unknown how the sensitivity of TRPV4 to this agonist is regulated. The spatial and temporal dynamics of receptors are the major determinants of cellular responses to stimuli. Membrane translocation has been shown to control the response of several members of the transient receptor potential (TRP) family of ion channels to different stimuli. Here, we show that TRPV4 stimulation with GSK1016790A caused an increase in [Ca2+]i that is stable for a few minutes. Single molecule analysis of TRPV4 channels showed that the density of TRPV4 at the plasma membrane is controlled through two modes of membrane trafficking, complete, and partial vesicular fusion. Further, we show that the density of TRPV4 at the plasma membrane decreased within 20 min, as they translocate to the recycling endosomes and that the surface density is dependent on the release of calcium from the intracellular stores and is controlled via a PI3K, PKC, and RhoA signaling pathway.

Components with self-propelling abilities are important building blocks of small autonomous systems and the characteristics of liquid metals are capable of fulfilling self-propulsion criteria. To date, there has been no exploration regarding the effect of electrolyte ionic content surrounding a liquid metal for symmetry breaking that generates motion. Here we show the controlled actuation of liquid metal droplets using only the ionic properties of the aqueous electrolyte. We demonstrate that pH or ionic concentration gradients across a liquid metal droplet induce both deformation and surface Marangoni flow. We show that the Lippmann dominated deformation results in maximum velocity for the self-propulsion of liquid metal droplets and illustrate several key applications, which take advantage of such electrolyte-induced motion. With this finding, it is possible to conceive the propulsion of small entities that are constructed and controlled entirely with fluids, progressing towards more advanced soft systems.

Recently, the bubble-based systems have offered a new paradigm in microfluidics. Gas bubbles are highly flexible, controllable and barely mix with liquids, and thus can be used for the creation of reconfigurable microfluidic systems. In this work, a hydrodynamically actuated bubble-based microfluidic system is introduced. This system enables the precise movement of air bubbles via axillary feeder channels to alter the geometry of the main channel and consequently the flow characteristics of the system. Mixing of neighbouring streams is demonstrated by oscillating the bubble at desired displacements and frequencies. Flow control is achieved by pushing the bubble to partially or fully close the main channel. Patterning of suspended particles is also demonstrated by creating a large bubble along the sidewalls. Rigorous analytical and numerical calculations are presented to describe the operation of the system. The examples presented in this paper highlight the versatility of the developed bubble-based actuator for a variety of applications; thus providing a vision that can be expanded for future highly reconfigurable microfluidics.

Immobilisation of cells is an important feature of many cellular assays, as it enables the physical/chemical stimulation of cells; whilst, monitoring cellular processes using microscopic techniques. Current approaches for immobilising cells, however, are hampered by time-consuming processes, the need for specific antibodies or coatings, and adverse effects on cell integrity. Here, we present a dielectrophoresis-based approach for the robust immobilisation of cells, and analysis of their responses under high shear flows. This approach is quick and label-free, and more importantly, minimises the adverse effects of electric field on the cell integrity, by activating the field for a short duration of 120 s, just long enough to immobilise the cells, after which cell culture media (such as HEPES) is flushed through the platform. In optimal conditions, at least 90% of the cells remained stably immobilised, when exposed to a shear stress of 63 dyn/cm(2). This approach was used to examine the shear-induced calcium signalling of HEK-293 cells expressing a mechanosensitive ion channel, transient receptor potential vaniloid type 4 (TRPV4), when exposed to the full physiological range of shear stress.

We present a tuneable optical waveguide using dielectrophoretically controlled nanoparticles in microfluidics. Silicon dioxide nanoparticles of different sizes in de-ionized water are channelled through a microfluidic system. An array of microelectrodes generates the dielectrophoretic force to funnel nanoparticles, forming narrowbands at the center of the microfluidics at different applied voltages and frequencies. It is observed that these narrowbands either scatter or guide the coupled light under selected conditions. The realization of such a system offers exciting possibilities for the development of a new class of optofluidics, which are tuned by the positioning of nanoparticles on demand.

This paper presents the design and numerical simulation of a T-shape microfluidic electroosmotic micromixer. It is equipped with six microelectrodes that are embedded in the side surfaces of the microchannel. The electrode array consists of two sets of three 20 ¿m and 60 ¿m microelectrodes arranged in the form of two opposing triangles. Numerical analysis of electric potential and frequency effects on mixing efficiency of the micromixer is carried out by means of two sets of simulations. First, the electric potential is kept at 2 V while the frequency is varied within 10-50 Hz. The highest achieved mixing efficiency is 96% at 22 Hz. Next, the frequency is kept at 30 Hz whilst the electric potential is varied within 1-5 V. The best achieved mixing efficiency is 97% at 3 V.

This paper describes the design, simulation, fabrication and experimental analysis of a passive micromixer for the mixing of biological solvents. The mixer consists of a T-junction, followed by a serpentine microchannel. the serpentine has three arcs, each equipped with circular barriers that are patterned as two opposing triangles. >The barriers are engineered to induce periodic perturbations in the flow field and enhance the mixing. CFD (Computational Fluid Dynamics) method is applied to optimise the geometric variables of the mixer before fabrication. The mixer is made from PDMS (Polydimethylsiloxane) using photo- and soft-lithography techniques. Experimental measurements are performed using yellow and blue food dyes as the mixing fluids. The mixing is measured by analysing the composition of the flow's colour across the outlet channel. The performance of the mixer is examined in a wide range of flow rates from 0.5 to 10 µl/min. Mixing efficiencies of higher than 99.4% are obtained in the experiments confirming the results of numerical simulations. The proposed mixer can be employed as a part of lab-on-a-chip for biomedical applications.

This paper presents an analytical model of fuel consumption (AMFC) to coordinate the driving power and manage the overall fuel consumption for an internal combustion engine vehicle. The model calculates the different loads applied on the vehicle including road-slope, road-friction, wind-drag, accessories, and mechanical losses. Also, it solves the combustion equation of the engine under different working conditions including various fuel compositions, excess airs and air inlet temperatures. Then it determines the contribution of each load to signify the energy distribution and power flows of the vehicle. Unlike the conventional models in which the vehicle speed needs to be given as an input, the developed model can predict the vehicle speed and acceleration under different working conditions by allowing the speed to vary within a predefined range only. Furthermore, the model indicates the ways to minimises the vehicles' fuel consumption under various driving conditions. The results show that the model has the potential to assist in the vehicle energy management.

Reduction of fuel consumption in glass melting furnaces can cut the cost of production, and tackle the global warming. This paper presents three independent solutions to reduce the fuel consumption in industrial glass melting furnaces. The solutions include air preheating, raw material preheating, and improving the insulation of combustion space refractory. Energy balance equations are derived and used to identify the effects of each solution. The results indicate that the three solutions reduce the fuel consumption by 9.5%, 17%, and 34%, respectively.

This paper presents a random forest-based face image classification method. The random forest is an ensemble learning method that grows many classification trees. Each tree gives a classification. The forest selects the classification that has the most votes. Three experiments are performed. The random forest-based method together with several existing approaches are trained and evaluated. The experimental results are presented and discussed.