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.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

某金矿尾矿提纯石英应用对比试验研究

针对某金矿，在回收金银的基础上，对金银浮选尾矿进行云母和长石浮选，以及提纯石英试验，最终获得SiO2品位99.91%的石英产品。提纯石英产品与比利时矽比科矿业有限公司石英产品进行对比试验，通过化学成分、粒度组成、熔制、粘度和热膨胀系数测试对比试验，得出金矿提纯石英达到甚至超过矽比科进口石英产品的各项技术指标。     

Nanocrystallization and optical properties of CsPbBr3−xIx perovskites in chalcogenide glasses

The confinement of CsPbX₃ (X = Cl, Br, and I) perovskite nanocrystals (NCs) in a stabilized inorganic glass matrix is a new strategy for improving their long-term stability and promoting their applications in the optoelectronic field. Here, in situ nanocrystallization strategy is developed to precipitate CsPbBr_3?xI_x NCs with arbitrary I/Br ratio among an elaborately designed GeS₂–Sb₂S₃-based chalcogenide glass matrix. Spherical CsPbBr_3?xI_x NCs are homogeneously distributed in the glass matrix after thermal treatment. The photoluminescence (PL) spectra show that the emission peaks of CsPbBr_3?xI_x NCs can be tuned from 570 nm to 722 nm with the replacement of Br by I. The fs transient absorption (TA) spectra reveal that there exists some structural defects in the NCs, leading to short PL decay life. This work would shed light on confining CsPbX₃ NCs into glassy matrices, facilitating their future applications in photoelectronic fields.     

Development of silica based organic slurries for stereolithographic printing process

In this study, silica based slurries for stereolithographic printing of glass structures are developed and characterized. Stereolithography has the potential to print complex structures with high resolution. Therefore, acrylate based photocurable slurries have been developed and their viscosities are examined as a function of the solid loading. A critical shear rate can be derived, which must not be exceeded during the printing process. Therefore, rheological characterizations provide important insights into the printing process and the ability to produce samples with precise structures. Other properties such as polymerization time and curability kinetic were investigated with time dependent attenuated total reflection infrared spectroscopy (ATR-IR). Afterwards, the slurries were printed on a commercial printer operating with visible light. For debinding the printed green bodies, the decomposition temperatures were derived from thermogravimetric analysis in order to obtain stable and transparent samples.     

Surveying of Na2O3–BaO–PbO–Nb2O5–SiO2–Al2O3 glass-ceramics system in terms of alpha,proton, neutron and gamma protection features by utilizing GEANT4 simulation codes

In this study, nuclear shielding qualities of glass-ceramics with chemical composition Na₂O₃–BaO–PbO–Nb₂O₅–SiO₂–Al₂O₃ containing different amount of BaO and PbO were investigated. The μ_ρ values were simulated using GEANT4 toolkit at 0.015–20 MeV wide energy range and the obtained results were verified by theoretical WinXCOM results. The variables such as μ_ρ, HVL, MFP, Z_eff, N_eff, EBF and EABF were computed to determine the gamma-ray shielding performances of studied glass ceramics. The results revealed that increase in PBO and BaO percentages in glass samples has caused to decrease the HVL, MFP, EBF and EABF values and increase μ_ρ, Z_eff values. It has been seen that N28 and S24 samples own superior protection ability against gamma radiation. In addition, the shielding capacity of these glass ceramics against charged and uncharged particles were predicted by determination of MSP and PR values for alpha, proton and ΣR values for neutrons. It has been concluded that PbO and BaO addition improve radiation shielding competences of glass ceramics. The data obtained from this study will be beneficial for designing glass ceramics shields for radiation protection enforcements.     

Affective matches of fabric and lighting chromaticity

This study investigates the impact of lighting colors on subjective judgments of fabric: in particular, whether the influence of lighting varies depending on fabric types and color combinations. We conducted two visual assessments. In Study 1 (N = 44), eight illuminants and six types of fabric were presented as cloth stimuli. Derived from the literature review, four sets of adjectives (humble-luxurious, cool-warm, old-new, and not preferred-preferred) were used as metrics. In Study 2 (N = 41), five sets of fabric color combination swatches were assessed under lighting conditions that were identical to those of Study 1. Three bipolar scales (ordinary-characterful, classic-modern, and soft-rigid), were employed from factor analysis along with three unipolar scales (luxurious, preferred, harmonious with lighting). The results showed that hue characteristics of lighting and cloth types influenced participants' perceptions of the fabric. Overall, white lighting with 4000 K was the most preferred and luxurious lighting across various types of clothes, while a pinkish white with 4700 K (duv = −0.0127) was the best matched in every color combination. In addition, there were interaction effects between lighting colors, clothes types, and fabric color combinations with regard to each of the perceptual qualities. This study provides empirical evidence for optimally match lighting colors with fabric in the presentation of fabric goods.     

Mechanical damage characterization of glass fiber-reinforced polymer laminates by ultrasonic maps

A method for simultaneous measurement of the thickness and density for Glass Fiber-Reinforced Polymer (GFRP) laminate plates with ultrasonic waves in C-Scan mode is presented in the form of maps. The method uses three different signals in immersion pulse-echo C-Scan mode. The maps obtained based on the density show the heterogeneity of the material at high resolution at the pixel level (1 × 1 mm²) and therefore they represent an efficient tool to assess and evaluate the damage of the composite structures after manufacturing and after an applied mechanical loading.     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.