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MXene-zingerone synergized gelatin-based hydrogel for accelerated open wound healing and epidermal regeneration in a preclinical model
No full textThe treatment of open wounds presents a significant challenge in clinical practice due to the increased risk of bacterial infections and prolonged recovery times. Currently, antibiotics are highly preferred to address underlying problems; however, their misuse has led to antibacterial resistance and compromised biocompatibility. This underscores the need for an advanced biological macromolecule-based system with multifunctional properties. Herein, we report a gelatin-based hydrogel dressing integrated with MXene and zingerone (GPM Z) to manage open wounds. Nanocomposite hydrogels demonstrated porous networks, hydrophilic nature, and rheological behavior, ensuring their reliability in a physiological environment. The sustained release of zingerone from hydrogels promotes prolonged therapeutic effects while minimizing side effects associated with burst release. In vitro analysis confirms >80 % antioxidant potential as determined by DPPH assay, hemolysis (<5 %), n-HDF cells compatibility, and antibacterial activity of nanocomposite hydrogels, which can be tuned by varying MXene concentration in hydrogels. In vivo analysis highlights the effectiveness of GPM-5 Z hydrogel in facilitating wound closure (98.75 % by day 14). The synergistic interplay of MXene and zingerone enhances wound healing by combining MXene antibacterial potential and bio interactive surface supporting cell proliferation with zingerone antioxidant activity that mitigates oxidative stress, thereby facilitating collagen deposition, re-epithelialization, and angiogenesis
Inverse Design of High Power and High Voltage LDMOS Transistors Using Deep Learning Based Sample-Efficient Surrogate Model
No full textDevice design using Machine Learning has been used in the semiconductor industry over the past ten years. However, the generation of the training data set for precise predictions using this technique remains burdensome. Addressing this, in this work, we propose eight sample-efficient techniques to train the Deep Neural Network (DNN) based surrogate models that emulate Technology Computer-Aided Design (TCAD). We showcase their efficacy by predicting off-state breakdown voltage (BVDS,off) and specific on-resistance (Rsp) of a Laterally Diffused Metal Oxide Semiconductor Field-effect Transistor (LDMOSFET). Our findings highlight the potential for 38% reduction in training dataset size while maintaining a strong predictive baseline accuracy. Specifically, the Diverse Representative-Query-by-Committee (DR-QBC) technique works best yielding 6.5% Euclidean Norm of Prediction Error (ENPE). We also demonstrate an inverse design framework by leveraging the same surrogate model with Differential Evolution (DE) and Bayesian Optimizer (BO). It mimics the role of a device design engineer by optimizing the values of structural parameters of the LDMOS transistors such that the desired BVDS,off is attained while minimizing Rsp
Simultaneous electron and proton conduction in a stable metal organic material with highly selective electrocatalytic oxygen reduction reaction to water
No full textProton coupled electron transfer (PCET) is considered as the elementary step of several chemical, electrochemical and biological processes and thus the development of dual conducting materials has recently become a major focus in Chemical Science. Herein, we report the highly selective electrocatalytic oxygen reduction to water by the stable dual conducting metal-organic material (MOM) [Cu(INA)2(H2O)4] (INA = isonicotinate). Structural analysis reveals the important role of both, hydrogen bonding and π-interactions, in the formation of a supramolecular 3D network. Theoretical calculations show that hydrogen bonding interactions among the coordinated water molecules and deprotonated carboxylate oxygen atoms induce proton transport (2.26 ± 0.10 × 10−5 S cm−1 at 98% RH) while weak intermolecular π-interactions (π-π and anion-π) provide the pathway for electron transport (1.4 ± 0.1 × 10−7 S cm−1 at 400 K). Such dual proton and electron conductivity leads to a selective oxygen reduction reaction (ORR) to water in an alkaline medium. To the best of our knowledge, this is the first report on electrocatalytic ORR by a dual-conducting metal-organic material
Effect of lime and fiber treatment on geotechnical response of pond ash with varying bottom ash content
No full textNegative Sequence Current Injection Based Active Islanding Detection Method
No full textThis article presents a fast and accurate active islanding detection scheme for a grid-tied voltage source inverter (VSI). In the proposed scheme, the VSI generates a small negative sequence voltage to continuously inject a small negative sequence current into the system. The voltage at the point of common coupling (PCC) is measured and the negative sequence component of PCC voltage is extracted. The negative sequence voltage generated by the VSI and the negative sequence voltage at PCC are combined to yield a time-invariant coefficient, which is proposed here to be used for islanding detection. In case detection is done solely based on the magnitude of negative sequence voltage at PCC, higher threshold has to be set as a small magnitude of negative sequence voltage at PCC may also be present due to grid unbalance and other transient events. In the proposed scheme, the islanding detection coefficient depends on the similarity between the injected and measured negative sequence voltage and hence is not dependent on the absolute magnitude of the negative sequence voltage. Thus a very low magnitude of negative sequence current can be injected to effectively detect islanding conditions without a significant impact on power quality. Simulation results show that the proposed scheme is effective for unbalanced grid voltage, or unbalanced load, even in the presence of noise in acquired data. Experimental studies on a laboratory prototype validate the performance of the proposed islanding detection scheme
Influence of cooling rate on the evolution of γˈ precipitates in a low-density CoNi-base γ/γ′ superalloy
No full textThe mechanical properties of γ/γʹ superalloys are governed by the size, shape, and distribution of the γʹ precipitates within the γ matrix. This work explores the feasibility of microstructure tuning by varying cooling rates in a low mass density γ/γ′ Co-30Ni-10Al-2Nb-4Ti-12Cr (at %) superalloy. We observe a strong cooling rate dependence on the morphology, composition, shape, and size distribution of γ′ precipitates. Cooling rates ≥6.25 K/s from super-solvus temperature (1413 K) of the alloy show unimodal size distribution of γˈ precipitates with a high number density. Whereas a slower rate of cooling (≤6.25 K/s) results in the formation of the bimodal size distribution of γˈ precipitates. For all the cooling rates explored (100, 28, 6.25, 1.625, 0.43, and 0.108 K/s), secondary γˈ precipitates exhibit nearly cuboidal morphology, and power law describes the evolution of their size with different cooling rates. Atomic-scale compositional analysis by an atom probe reveals the composition of secondary γˈ precipitates is dependent on the cooling rates. In addition, we found finer tertiary γˈ precipitates near the secondary γˈ precipitates and matrix interface, while relatively larger tertiary γˈ precipitates away from the secondary γˈ precipitates for the slow cooling rates (1.625 K/s, and 0.108 K/s). This was attributed to the concentration gradient that develops in the γ matrix region in between the secondary γʹ precipitates during continuous cooling. In the light of classical nucleation theory, the results indicate a multi-stage formation of γˈ precipitates whose morphology, size distribution, and composition are found to be dependent on the cooling rates. Hence, these experiments shows the possibility of tuning the microstructure of Co-based superalloys that is critical in governing their mechanical properties