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.     

Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

Polyimide foams from powder: Experimental analysis of competitive diffusion phenomena

In the present study, various diffusive processes have been investigated during foaming of powdered precursors of polyimide. A detailed analysis of the powdered precursor's characteristics allows for an enhanced morphological understanding of the resulting microstructures and foam unit cell. Parameters that are central to the foaming process such as particle morphology, volatile concentration and sorption-desorption processes are evaluated. Isothermal and non-isothermal desorption experiments have been carried out by thermogravimetric analysis (TGA), and specific diffusive processes have been correlated to thermodynamic and kinetic transitions by means of modulated differential scanning calorimetry (MDSC) of the corresponding materials. It was found that two primary fluxes of volatiles, one out of the external surface of the particles (responsible for volatile desorption) and the other into the growing bubble (responsible for vapor supersaturation inside the bubble) compete against each other creating a competitive scenario that becomes the controlling factor for potential inflation within the precursor particles.     

Effect of beam voltage on the properties of aluminium nitride prepared by ion beam assisted deposition

The effects of nitrogen-beam voltage on the structure, stress, energy band gap and hardness of AIN thin films deposited on Si (111), Si (100) and sapphire (0001) by ion beam assisted deposition (IBAD) are reported. As the nitrogen-beam voltage was increased from 50 to 200 V, the stress and disorder in the AIN films increased as determined by X-ray diffraction, FTIR and Raman spectroscopy. The preferred orientation of the film's c-axis changed from completely normal to the film at 100 V, to a mixture of normal and in the plane of the film at 200 V. For AIN films deposited under the same conditions, the films were more highly oriented on sapphire (0001) than in Si (111). The hardness of the films increased from 18.2 to 23.7 GPa with the nitrogen-beam voltage, and possible reasons for this change in hardness are considered.     

Modeling Solvent Drying of Coated Webs Including the Initial Transient

A complete model for the drying of coated webs. as implemented in the spreadsheet DRYWEB for Lotus and for Excel, is described. The model predicts the temperatures and solvent levels throughout the length of the dryer. The initial transient as the coated web approaches the equilibrium temperature in the consiant rate period is calculated. The coating is divided into slices for a finite difference calculation of solvent diffusion to the surface, using a simple free volume model of diffusion. The semi-empirical constants needed can be found from one or two trial runs. The possible use of infra-red heating in addition to convection heating is included in both the constant and falling rates. The calculation of infra-red energy is discussed. The model assumes that the temperature is uniform from the top of the coating to the bottom of the base, and that assumption is justified.     

Optimal control of auxiliary heating of passive-solar-heated buildings

Previous papers have demonstrated that low-cost off-peak electricity can be used to provide auxiliary heating for a passive-solar-heated residence. This paper describes the development of optimal control strategies for the operation of this auxiliary heating system. The temperature in the residence throughout the day as determined by computer simulation is presented for several optimal and conventional control strategies. The optimal control strategies improved the temperature control in the building compared to the conventional strategies. All strategies were shown to depend heavily on weather prediction. Computer simulations showed that errors in weather prediction had a small effect on heating cost and a large effect on being able to maintain the desired indoor temperature.