Attoliter protein nanogels from droplet nanofluidics for intracellular delivery.

Microscale hydrogels consisting of macromolecular networks in aqueous continuous phases have received increasing attention because of their potential use in tissue engineering, cell encapsulation and for the storage and release of cargo molecules. However, for applications targeting intracellular delivery, their micrometer-scale size is unsuitable for effective cellular uptake. Nanoscale analogs of such materials are thus required for this key area. Here, we describe a microfluidics/nanofluidics-based strategy for generating monodisperse nanosized water-in-oil emulsions with controllable sizes ranging from 2500 ± 110 nm down to 51 ± 6 nm. We demonstrate that these nanoemulsions can act as templates to form protein nanogels stabilized by supramolecular fibrils from three different proteins. We further show that these nanoparticles have the ability to penetrate mammalian cell membranes and deliver intracellular cargo. Due to their biocompatibility and lack of toxicity, natural protein-based nanoparticles present advantageous characteristics as vehicles for cargo molecules in the context of pharmaceutical and biomedical applications

Low-Cost Microfabrication Tool Box.

Microsystems are key enabling technologies, with applications found in almost every industrial field, including in vitro diagnostic, energy harvesting, automotive, telecommunication, drug screening, etc. Microsystems, such as microsensors and actuators, are typically made up of components below 1000 microns in size that can be manufactured at low unit cost through mass-production. Yet, their development for commercial or educational purposes has typically been limited to specialized laboratories in upper-income countries due to the initial investment costs associated with the microfabrication equipment and processes. However, recent technological advances have enabled the development of low-cost microfabrication tools. In this paper, we describe a range of low-cost approaches and equipment (below £1000), developed or adapted and implemented in our laboratories. We describe processes including photolithography, micromilling, 3D printing, xurography and screen-printing used for the microfabrication of structural and functional materials. The processes that can be used to shape a range of materials with sub-millimetre feature sizes are demonstrated here in the context of lab-on-chips, but they can be adapted for other applications. We anticipate that this paper, which will enable researchers to build a low-cost microfabrication toolbox in a wide range of settings, will spark a new interest in microsystems

Collective dynamics in dispersions of anisometric pigment particles

© 2018 Elsevier B.V. Dynamics of suspensions of solid rodlike pigment particles in a non-polar solvent were studied in a concentration range from the isotropic up to the orientationally ordered nematic-like phase. Using dynamic light scattering and gradient recovery measurements, we studied the rotational and translational diffusion coefficients. We demonstrate that the translational diffusion coefficient in this system is increasing with increasing concentration of the pigment particles in the vicinity of the transition into an ordered phase. This unexpected behaviour can be attributed to the collective interactions between the particles and the alignment effects

Enhanced Quality Factor Label-free Biosensing with Micro-Cantilevers Integrated into Microfluidic Systems.

Microelectromechanical systems (MEMS) have enabled the development of a new generation of sensor platforms. Acoustic sensor operation in liquid, the native environment of biomolecules, causes, however, significant degradation of sensing performance due to viscous drag and relies on the availability of capture molecules to bind analytes of interest to the sensor surface. Here, we describe a strategy to interface MEMS sensors with microfluidic platforms through an aerosol spray. Our sensing platform comprises a microfluidic spray nozzle and a microcantilever array operated in dynamic mode within a closed loop oscillator. A solution containing the analyte is sprayed uniformly through picoliter droplets onto the microcantilever surface; the micrometer-scale drops evaporate rapidly and leave the solutes behind, adding to the mass of the cantilever. This sensing scheme results in a 50-fold increase in the quality factor compared to operation in liquid, yet allows the analytes to be introduced into the sensing system from a solution phase. It achieves a 370 femtogram limit of detection, and we demonstrate quantitative label-free analysis of inorganic salts and model proteins. These results demonstrate that the standard resolution limits of cantilever sensing in dynamic mode can be overcome with the integration of spray microfluidics with MEMS

Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg−Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg−Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.Peer reviewe

Real-Time Intrinsic Fluorescence Visualization and Sizing of Proteins and Protein Complexes in Microfluidic Devices.

Optical detection has become a convenient and scalable approach to read out information from microfluidic systems. For the study of many key biomolecules, however, including peptides and proteins, which have low fluorescence emission efficiencies at visible wavelengths, this approach typically requires labeling of the species of interest with extrinsic fluorophores to enhance the optical signal obtained - a process which can be time-consuming, requires purification steps, and has the propensity to perturb the behavior of the systems under study due to interactions between the labels and the analyte molecules. As such, the exploitation of the intrinsic fluorescence of protein molecules in the UV range of the electromagnetic spectrum is an attractive path to allow the study of unlabeled proteins. However, direct visualization using 280 nm excitation in microfluidic devices has to date commonly required the use of coherent sources with frequency multipliers and devices fabricated out of materials that are incompatible with soft lithography techniques. Here, we have developed a simple, robust, and cost-effective 280 nm LED platform that allows real-time visualization of intrinsic fluorescence from both unlabeled proteins and protein complexes in polydimethylsiloxane microfluidic channels fabricated through soft lithography. Using this platform, we demonstrate intrinsic fluorescence visualization of proteins at nanomolar concentrations on chip and combine visualization with micron-scale diffusional sizing to measure the hydrodynamic radii of individual proteins and protein complexes under their native conditions in solution in a label-free manner

Systematic development of small molecules to inhibit specific microscopic steps of Aβ42 aggregation in Alzheimer’s disease

The aggregation of the 42-residue form of the amyloid-β peptide (Aβ42) is a pivotal event in Alzheimer’s disease (AD). The use of chemical kinetics has recently enabled highly accurate quantifications of the effects of small molecules on specific microscopic steps in Aβ42 aggregation. Here, we exploit this approach to develop a rational drug discovery strategy against Aβ42 aggregation that uses as a read-out the changes in the nucleation and elongation rate constants caused by candidate small molecules. We thus identify a pool of compounds that target specific microscopic steps in Aβ42 aggregation. We then test further these small molecules in human cerebrospinal fluid and in a Caenorhabditis elegans model of AD. Our results show that this strategy represents a powerful approach to identify systematically small molecule lead compounds, thus offering an appealing opportunity to reduce the attrition problem in drug discovery

Attoliter protein nanogels from droplet nanofluidics for intracellular delivery.

Microscale hydrogels consisting of macromolecular networks in aqueous continuous phases have received increasing attention because of their potential use in tissue engineering, cell encapsulation and for the storage and release of cargo molecules. However, for applications targeting intracellular delivery, their micrometer-scale size is unsuitable for effective cellular uptake. Nanoscale analogs of such materials are thus required for this key area. Here, we describe a microfluidics/nanofluidics-based strategy for generating monodisperse nanosized water-in-oil emulsions with controllable sizes ranging from 2500 ± 110 nm down to 51 ± 6 nm. We demonstrate that these nanoemulsions can act as templates to form protein nanogels stabilized by supramolecular fibrils from three different proteins. We further show that these nanoparticles have the ability to penetrate mammalian cell membranes and deliver intracellular cargo. Due to their biocompatibility and lack of toxicity, natural protein-based nanoparticles present advantageous characteristics as vehicles for cargo molecules in the context of pharmaceutical and biomedical applications

Critical Performance Analysis of Four-Wheel Drive Hybrid Electric Vehicles Subjected to Dynamic Operating Conditions

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures