In recent years, many metal oxides have been rigorously studied to be employed as solid electrolytes for resistive switching (RS) devices. Among these solid electrolytes, lanthanum oxide (La2O3) is comparatively less explored for RS applications. Given this, the present work focuses on the electrodeposition of La2O3 switching layers and the investigation of their RS properties for memory and neuromorphic computing applications. Initially, the electrodeposited La2O3 switching layers are thoroughly characterized by various analytical techniques. The electrochemical impedance spectroscopy (EIS) and Mott–Schottky techniques are probed to understand the in situ electrodeposition, RS mechanism, and n‐type semiconducting nature of the fabricated La2O3 switching layers. All the fabricated devices exhibit bipolar RS characteristics with excellent endurance and stable retention. Moreover, the device mimics the various bio‐synaptic properties such as potentiation‐depression, excitatory post‐synaptic currents, and paired‐pulse facilitation. It is demonstrated that the fabricated devices are non‐ideal memristors based on double‐valued charge‐flux characteristics. The switching variation of the device is studied using the Weibull distribution technique and modeled and predicted by the time series analysis technique. Based on electrical and EIS results, a possible filamentary‐based RS mechanism is suggested. The present results assert that La2O3 is a promising solid electrolyte for memory and brain‐inspired applications.
We model the load sharing phenomenon in a -out-of-system through the accelerated failure time model. This model leads to multivariate families of distributions for ordered random variables, which are particular cases of the sequential order statistics. For illustrative purpose, we discuss the model, and the estimation problem for a two component parallel system under the setting of a linear failure rate distribution. In this set up, we discuss a test for the hypothesis that the failure times of components are statistically independent against the alternative that they show the load sharing phenomenon. We report simulation studies showing the performance of the estimators, and the test procedure. The test is also applied to two data sets for illustrative purpose.Index Terms-Accelerated failure time model, conditional distribution, Cox proportional hazard model, EM algorithm, linear failure rate distribution, sequential order statistics.
Various bacterial pathogens are responsible for nosocomial infections resulting in critical pathophysiological conditions, mortality, and morbidity. Most of the bacterial infections are associated with biofilm formation, which is resistant to the available antimicrobial drugs. As a result, novel bactericidal agents need to be fabricated, which can effectively combat the biofilm-associated bacterial infections. Herein, for the first time we report the antimicrobial and antibiofilm properties of silver-platinum nanohybrids (AgPtNHs), silver nanoparticles (AgNPs), and platinum nanoparticles (PtNPs) against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The AgPtNHs were synthesized by a green route using Dioscorea bulbifera tuber extract at 100°C for 5 h. The AgPtNHs ranged in size from 20 to 80 nm, with an average of ∼59 nm. AgNPs, PtNPs, and AgPtNHs showed a zeta potential of −14.46, −1.09, and −11.39 mV, respectively. High antimicrobial activity was observed against P. aeruginosa and S. aureus and AgPtNHs exhibited potent antimicrobial synergy in combination with antibiotics such as streptomycin, rifampicin, chloramphenicol, novobiocin, and ampicillin up to variable degrees. Interestingly, AgPtNHs could inhibit bacterial biofilm formation significantly. Hence, co-administration of AgPtNHs and antibiotics may serve as a powerful strategy to treat bacterial infections.
Dye-sensitized solar cells (DSSCs) are one of the most versatile and low-cost solar cells. However, DSSCs are prone to low power conversion efficiency (PCE) compared to their counterparts, owing to their different synthesis parameters and process conditions. Therefore, designing efficient DSSCs and identifying the parameters that control the PCE of DSSCs are a critical tasks. We have collected data from hydrothermally synthesized DSSCs in the present work, published from 2005 to 2020. In line with publishing trends in the said period, we evaluate ZnO as a popular photoactive material for DSSC applications. We further analyzed the performance of hydrothermally synthesized ZnO DSSCs using different statistical techniques and provided some significant insights. We further applied the machine-learning technique with a decision tree algorithm to understand and discover the possible set of rules and heuristics that govern the morphology of the hydrothermally grown ZnO. In addition, we also employed supervised and unsupervised machine-learning models using conventional decision trees and classification and regression trees, respectively, to identify the dependence of the PCE of ZnO DSSCs on the different synthesis parameters. The reported work also evidences the PCE predictions of the ZnO DSSCs by using random forest and artificial neural network algorithms. The results substantiate that the random forest and artificial neural network algorithms successfully predict the PCE of the ZnO DSSCs with reasonable accuracy. Thus, we present a novel approach of applying statistical analysis and machine-learning algorithms to understand, discover, and predict the performance of DSSCs. We recommend extending the said know-how to other solar cells to identify rules and heuristics and experimentally realize highly efficient solar cells in shrinking manufacturing windows with a cost-effective approach.
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