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.     

Microwave hybrid fast sintering of red clay ceramics

This work aimed to examine the performance of the hybrid sintering of clay ceramic in a microwave furnace, compared to the sintering process in a conventional furnace. The raw materials were subjected to X-ray fluorescence, loss on ignition (LOI), X-ray diffraction, particle size distribution, real specific mass, and thermogravimetric analyses. The red clay ceramic mass was prepared, extruded, pre-sintered in a conventional furnace at 600°C/60 min, and sintered at temperatures between 700 °C and 1100 °C. The sintering conventional (resistive oven) was carried out for 60 min with a heating rate of 10°C/min. In the microwave furnace, the sintering times were 5, 10, and 15 min, with a heating rate of 50°C/min, with a sintering chamber coated with silicon carbide (susceptor). The sintered specimens were characterized according to linear shrinkage, water absorption, apparent porosity, apparent specific mass, X-ray diffraction, Raman spectroscopy analysis, spectroscopy analysis in the ultraviolet and visible regions, microhardness, and scanning electron microscopy. The results showed that microwave sintering promoted an increase in the microhardness and apparent specific mass, and reduction in water absorption and apparent porosity values, due to greater densification in the microstructure. The best results occurred for specimens sintered at 1100°C.     

Thermal prehistory,structure and high-temperature thermodynamic properties of Y2O3-CeO2 and Y2O3-ZrO2-CeO2 solid solutions

Ceria-based solid solutions are important materials for high- and medium-temperature electrochemical applications. However, the stabilities of both binary and ternary ceria-based solid solutions are insufficient at elevated temperatures, which limits their application as solid electrolytes or SOFC cathodes. Data on the high-temperature stability of ceria-based ceramics are unavailable in the literature. In the present study, we report a thermodynamic stability investigation of Y₂O₃-CeO₂ and Y₂O₃-ZrO₂-CeO₂ solid solutions. The thermal prehistories of binary and ternary systems were investigated using STA, XRD, and ESCA techniques. The vaporization processes were investigated in the temperature range of 1577–2227°С via the Knudsen effusion mass spectrometry technique. Using data on the component activity in solid-phase thermodynamic properties of Y₂O₃-CeO₂ solid solutions, which is represented as the Gibbs energy, the excess Gibbs energy was calculated as a function of the ceria mol. %. It was shown that the reduction of Ce⁴⁺ to Ce³⁺ in Y₂O₃-CeO₂ and Y₂O₃-ZrO₂-CeO₂ solid solutions corresponds to less-negative Gibbs energy compared to ZrO₂-CeO₂ solid solutions.     

A novel ceramic matrix composite based on YNbO4–TiO2 for microwave applications

This work presents the dielectric properties of YNbO₄ (YNO)–TiO₂ composites in the microwave range. X-ray diffraction analysis demonstrates that the addition of TiO₂ to YNO results in the formation of a Y(Nb_0.5Ti_0.5)₂O₆ phase. In the microwave range, the values of permittivity and dielectric loss did not present major changes with the increment of TiO₂. Moreover, the addition of TiO₂ results in an improvement in the thermal stability of YNO, with YNO63 demonstrating a resonant frequency of ?8.96 ppm.°C^?1. We utilised numerical simulations to evaluate the behaviour of these materials as dielectric resonator antennae and it is found that they exhibit a reflection coefficient below ?10 dB at the resonant frequency, with a realised gain of 4.94 – 5.76 dBi, a bandwidth of 665–1050 MHz and a radiation efficiency above 84%. Our results indicate that YNO–TiO₂ composites are interesting candidates for microwave operating devices.     

Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression

Both water balance (WB) and rating curve (RC) are methods for estimating streamflow. The first is mostly used to estimate reservoir outflows, while the second is usually adopted in hydrometeorological network streamflow gauges. While WB uses hourly collected data, the RC estimates streamflow using current water level and extrapolation techniques. The objective of this study was to analyze variations in the reservoir’s hourly outflow at Queimado Hydroelectric Power Plant (HPP Queimado) and to propose a method to evaluate whether the estimate of the daily outflows, obtained by the WB method, is similar to the flow values obtained at a conventional station. The logistic regression (LR) model was used because it is a method that adopts binary, categorically dependent variables to identify the event of interest. The results showed that the values of streamflow, obtained from an average of two daily readings, were a good representation of the flows in the region. The LR was able to identify atypical data, especially in the rainy season. This means that data consistency analysis can be faster and safer, when adequately employed and considering the proposed conditions, contributing to both management policies and the management of water resources.

     

Pressure drop and optimization meta-models for arbitrary low-height pleated filter shapes and flowrates

Journal of Mechanical Science and Technology - This study delivers equations useful for low-height pleated fibrous filter design: two pressure drop equations and one set of optimum design equations...     

Chitosan Microspheres as Carriers for pH‐Indicating Species in Corrosion Sensing

Chitosan microspheres containing bromocresol green, cresol red, and phenolphthalein for corrosion detection, through pH change, are synthesized in order to be used in protective coatings for aluminium alloys. Microspheres containing corrosion detection species are characterized morphologically (SEM) and physico‐chemically (FTIR, TGA). Release studies (UV–vis) are performed in corrosion‐promoting conditions (pH, NaCl), and detection studies by immersion in media associated with corrosion activity while microspheres' sensing activity is evaluated visually. Electrochemical characterization of AA2024 substrates in the presence of chitosan spheres is performed to understand material performance, and a color change is observed as a result of local pH increase in cathodic areas when corrosion takes place. These findings can be correlated with the results from release studies and seem a promising approach for corrosion sensing purposes, not only because pH increase is possible to detect due to corrosion, but also because chitosan is considered an environmentally friendly material.