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...     

A Regioselective Approach to Synthesize Indolyl Diketone Derivatives via Magnetic Polymeric Copper-Catalyst

Catalysis Letters - In the present paper, an efficient Cu-catalyzed regioselective acylation of indoles with phenylglyoxals was developed which is the first example of indolyl diketones synthesis...     

Synergistic effect of high-intensity ultrasound and β-cyclodextrin treatments on browning control in apple juice

This work evaluated the synergistic effects of combined high-intensity ultrasound (HIU) with β-cyclodextrin (β-CD) treatments on inhibiting browning of apple juice and explored the mechanism through simulation system. The combined treatment of 300 W HIU with 0.006 g mL⁻¹ β-CD had a synergistic impact on maintaining juice colour, resulting in a 39.06% reduction in browning degree, only a 36.64% decrease in total phenolic content, and a 17.82% reduction in PPO activity. The inhibition of enzymatic browning in simulated system revealed that HIU suppressed the enzyme (Polyphenol oxidase, PPO) and β-CD inhibited enzyme (PPO) and embedded substrate (polyphenol). The results of spectroscopic analysis showed that the particle-size distribution of PPO narrowed, the content of α-helix in the secondary structure increased, the fluorescence intensity increased, and the maximum wavelength was red-shifted after HIU and β-CD treatment. Changes in structure could further result in PPO activity loss. Hence, the combined treatment could synthetically alleviate the browning of apple juice.     

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...     

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.     

Superswelling Microneedle Arrays for Dermal Interstitial Fluid (Prote)Omics

The noninvasive sampling of dermal interstitial fluid (ISF) for the monitoring of clinical biomarkers is a greatly appealing area of research. The identification of molecular biomarkers in biological fluids has been accelerated with -omics analyses but remains limited in ISF because of its time-consuming and complex extraction process. Here, the generation of microneedle (MN) patches made of superabsorbent acrylate-based hydrogels for the rapid sampling of dermal ISF is described to explore its proteome. In depth, iterative optimization allows the identification of novel acrylate-based compositions with the required chemical, mechanical, and biocompatibility properties allowing proteomic analysis of the extracted ISF for the first time after sampling with swelling MNs. The generated MN arrays show no cytotoxic effect, successfully cross the stratum corneum, and can collect up to 6 µL of dermal ISF in 10 min in vivo. Proteomics lead to the detection of 176 clinically relevant biomarkers in the collected samples validating the use of ISF as a relevant bodily fluid for disease monitoring and diagnostic. Importantly, it is discovered that extraction fingerprint is strongly dependent on the MNs chemistry, and thus specific biomarkers could be selectively extracted by tuning the composition of the patch, making the system versatile and specific.     

Discovery of GPR183 Agonists Based on an Antagonist Scaffold

The G protein-coupled receptor GPR183/EBI2, which is activated by oxysterols, is a therapeutic target for inflammatory and metabolic diseases where both antagonists and agonists are of potential interest. Using the piperazine diamide core of the known GPR183 antagonist (E)-3-(4-bromophenyl)-1-(4-(4-methoxybenzoyl)piperazin-1-yl)prop-2-en-1-one (NIBR189) as starting point, we identified and sourced 79 structurally related compounds that were commercially available. In vitro screening of this compound collection using a Ca²⁺ mobilization assay resulted in the identification of 10 compounds with agonist properties. To enable establishment of initial structure-activity relationship trends, these were supplemented with five in-house compounds, two of which were also shown to be GPR183 agonists. Taken together, our findings suggest that the agonist activity of this compound series is dictated by the substitution pattern of one of the two distal phenyl rings, which functions as a molecular efficacy-switch.