Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Effect of Drosophila suzukii on Blueberry VOCs: Chemical Cues for a Pupal Parasitoid,Trichopria anastrephae

Journal of Chemical Ecology - Biocontrol agents such as parasitic wasps use long-range volatiles and host-associated cues from lower trophic levels to find their hosts. However, this chemical...     

Analysis of energy saving in user equipment in LTE-A using stochastic modelling

Telecommunication Systems - Energy saving in User Equipment (UE) is one of the important issues for limited sources of power in the device. It is critical for the UE to maximize its energy...     

On the Growth of Thin-Film AlGaN/GaN Epitaxial Heterostructures on Hybrid Substrates Containing Layers of Silicon Carbide and Porous Silicon

Semiconductors - Abstract—In our work, we carry out a structural-spectroscopic study of AlGaN/GaN epitaxial layers grown by molecular-beam epitaxy with nitrogen-plasma activation on a hybrid...     

Influence of Continuous Wave Interference on the Efficiency of the Non-Threshold Search Procedure for a Noise-Like Signal by Delay Time with Transition to the Frequency Domain

Journal of Communications Technology and Electronics - A statistical study of the effectiveness of the non-threshold search procedure for a noise-like phase-shift keyed signal by the delay time is...     

Modern Photodetector IR-Modules

Journal of Communications Technology and Electronics - In recent years, there has been a rapid improvement in photonics products due to the use of multilayer heterostructures grown on the basis of...     

Phase Composition and Physico-Mechanical Properties at Different Firing Temperatures of Ceramic Earthquake-Resistant Bricks with the Use of Ferro-Dust

Glass and Ceramics - Ceramic bricks based on low-melting clay and ferro-dust from self-disintegrating, low-carbon, ferrochrome slags, graded from M100 to M175, respectively, were obtained in the...     

Superradiant Emission from Coherent Excitons in van Der Waals Heterostructures

Recent advancements in isolation and stacking of layered van der Waals materials have created an unprecedented paradigm for demonstrating varieties of 2D quantum materials. Rationally designed van der Waals heterostructures composed of monolayer transition-metal dichalcogenides (TMDs) and few-layer hBN show several unique optoelectronic features driven by correlations. However, entangled superradiant excitonic species in such systems have not been observed before. In this report, it is demonstrated that strong suppression of phonon population at low temperature results in a formation of a coherent excitonic-dipoles ensemble in the heterostructure, and the collective oscillation of those dipoles stimulates a robust phase synchronized ultra-narrow band superradiant emission even at extremely low pumping intensity. Such emitters are in high demand for a multitude of applications, including fundamental research on many-body correlations and other state-of-the-art technologies. This timely demonstration paves the way for further exploration of ultralow-threshold quantum-emitting devices with unmatched design freedom and spectral tunability.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Synthesis and characterization of transition metal mixed oxide doped graphene embedded durable electrocatalyst for hydrogen evolution reaction

A promising electrocatalyst containing variable percentage of V₂O₅–TiO₂ mixed oxide in graphene oxide support was prepared by embedding the catalyst on Cu substrate through facile electroless Ni–Co–P plating for hydrogen evolution reaction. The solvothermal decomposition method was opted for tuning the crystalline characteristics of prepared material. The optimized mixed oxide was well characterized, active sites centres were identified and explained by X-ray diffraction, high resolution tunnelling electron microscopy, scanning electron microscopy coupled with energy dispersive X-ray and X-ray photon spectroscopy analysis. The structural and electronic characteristics of material was done by fourier transform infrared spectroscopy and the electrochemical behaviour of the prepared material was evaluated by using Tafel plot, electrochemical impedance analysis, linear sweep voltammetry, open circuit analysis and chronoamperometry measurements. The results show the enhanced catalytic activity of Ni–Co–P than pure Ni–P plate, due to synergic effect. Moreover, the prepared mixed oxide incorporated Ni–Co–P plate has a high activity towards HER with low over potential of 101 mV, low Tafel slope of 36 mVdec^?1, high exchange current density of 9.90 × 10^?2 Acm^?2.