计算机辅助玻璃组分设计

根据硅酸盐玻璃成分与物理性质的线性关系,利用VisualC 语言进行编程,实现玻璃组成成分的计算过程。通过对钨组、钼组以及铂组等多种电真空玻璃系统的性能计算与实际性能的比较分析可以看出,玻璃的密度、膨胀系数等性能参数的计算值和实测值具有非常好的一致性。采用VisualC 程序辅助玻璃成分设计,能够提高新型玻璃的研究效率。     

Phosphate glasses with improved optical properties

A study was undertaken to see how some components can affect the physicochemical properties of PbO−BaO−Al₂O₃−P₂O₅ optical glasses. The possibility is demonstrated for the manufacture of phosphate-base flint glasses with a high refractive index and improved transmission in the visible and UV regions of the spectrum. Translated from Steklo i Keramika, No. 2, pp. 3–5, February, 1997.     

Modeling and optimization of photocatalytic/photoassisted-electro-Fenton like degradation of phenol using a neural network coupled with genetic algorithm

Oxidation of phenol in aqueous media using supported TiO₂ nanoparticles coupled with photoelectro-Fenton-like process using Mn²⁺ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn²⁺ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R² = 0.949).     

Study of the iron-containing sodium borosilicate two-phase glasses and porous glasses by optical spectroscopy

The results of studying the iron oxide-containing two-phase sodium borosilicate glass and porous glass based on it using the method of optical spectroscopy in the visible and infrared regions are presented. Decoding the transmittance spectra of this glass in these spectral ranges using the literary data and their comparative analysis with two-phase sodium borosilicate glass that does not contain iron were carried out.     

Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search

A genetic algorithm (GA) combined with a tabu search (TA) hasbeen applied as a minimization method to rake the appropriateassociated sites for some biomolecular systems. In our dockingprocedure, surface complementarity and energetic complementarityof a ligand with its receptor have been considered separatelyin a two-stage docking method. The first stage was to find aset of potential associated sites mainly based on surface complementarityusing a genetic algorithm combined with a tabu search. Thisstep corresponds with the process of finding the potential bindingsites where pharmacophores will bind. In the second stage, severalhundreds of GA minimization steps were performed for each associatedsite derived from the first stage mainly based on the energeticcomplementarity. After calculations for both of the two stages,we can offer several solutions of associated sites for everycomplex. In this paper, seven biomolecular systems, includingfive bound complexes and two unbound complexes, were chosenfrom the Protein Data Bank (PDB) to test our method. The calculatedresults were very encouraging—the hybrid minimizationalgorithm successfully reaches the correct solutions near thebest binded modes for these protein complexes. The docking resultsnot only predict the bound complexes very well, but also geta relatively accurate complexed conformation for unbound systems.For the five bound complexes, the results show that surfacecomplementarity is enough to find the precise binding modes,the top solution from the tabu list generally corresponds tothe correct binding mode. For the two unbound complexes, dueto the conformational changes upon binding, it seems more difficultto get their correct binding conformations. The predicted resultsshow that the correct binding mode also corresponds to a relativelylarge surface complementarity score. In these two test cases,the correct solution can be found in the top several solutionsfrom the tabu list. For unbound complexes, the interaction energyfrom energetic complementarity is very important, it can beused to filter these solutions from the surface complementarity.After the evaluation of the energetic complementarity, the conformationsand orientations close to the crystallographically determinedstructures are resolved. In most cases, the smallest root meansquare distance (r.m.s.d.) from the GA combined with TA solutionsis in a relatively small region. Our program of automatic dockingis really a universal one among the procedures used for thetheoretical study of molecular recognition.     

Surface nitrogen doping of optical glasses (a review)

The need is demonstrated for improvement in the service properties of the surfaces of optical components fabricated from chemically unstable glasses. A number of solid and gaseous reagents are proposed for surface nitrogen doping of optical glasses and the requirements they should meet are defined. The process variables involved in the nitrogen doping of glass at temperatures belowt _g are examined. The service properties of lead-containing, phosphate and borate glasses with a nitrogen-doped surface are listed. Translated from Steklo i Keramika, No. 1, pp. 6–8, January, 1997.     

Modeling of a reaction network and its optimization by genetic algorithm

Continuous endeavors are going on in many research works to find out the strategy to mathematically model and optimize complex reaction networks in order to maximize the main product and at the same time keeping the reactor dimensions within some acceptable limits. The aim of this work is to provide with a strategy for efficient modeling and optimization of reaction networks for reaction controlled processes. Genetic algorithm (GA) has been used for optimizing complex search spaces with multiple optima. Formation of styrene monomer from the ethylbenzene dehydrogenation, with several by-products in a fixed bed reactor, is taken as an example for this study. Two activation energies are found to be the best in term of maximizing styrene productivity.     

Fluorophosphate glasses doped with PbSe quantum dots and their nonlinear optical characteristics

A new fluorophosphate glass matrix containing PbSe quantum dots characterized by a narrow size distribution (ΔR/R ∼ 5–7%, where R is the size of a quantum dot) is prepared under heat treatment. It is demonstrated that fluorophosphate glasses can be doped by introducing (ZnSe + PbO) or PbSe into the composition of the glass. The optical absorption spectra of the quantum dots corresponding to the strong quantum confinement regime are studied over a wide range of quantum-dot sizes (4–18 nm). The nonlinear optical absorption is investigated at a wavelength of 1.54 μm. It is revealed that the introduction of (ZnSe + PbO) rather than of PbSe into the composition of the glass improves the bleaching contrast owing to a more uniform distribution of the quantum dots over the volume of the glass sample.     

Development of compositions of uviol glasses with elevated radiation and optical resistance

The processes occurring in the glass shells of bactericidal lamps are considered. It is shown that it is expedient to develop new compositions of uviol glasses with elevated radiation and optical resistance (ROR). It was found that an increase in the total amount of alkaline-earth oxides and BaO individually at the expense of CaO +MgO decreases solarization of glasses of the SiO₂-MgO-CaO-BaO-Na₂O-K₂O system. The introduction of Fe₂O₃ in the composition with a decrease in the content of MgO for a certain concentration of Fe²⁺ and Fe³⁺ improves the anti-reflection properties of uviol glasses in the ultraviolet spectral region and increases the ROR to short-wave ultraviolet radiation. Addition of metallic aluminum increases the Fe²⁺Fe³⁺ ratio. The physicochemical properties of the synthesized compositions are presented.     

Optimization of Petlyuk sequences using a multi objective genetic algorithm with constraints

In this work we use genetic algorithms to optimize Petlyuk sequences using a rigorous design model. A multi objective genetic algorithm (GA) with constraints was formulated and interconnected with the Aspen Plus process simulator to obtain each data point during the search process. In addition to providing more energy-efficient designs than some reported structures, two relevant trends were observed from the results of the case studies; one had to do with the feed location to the prefractionator as a function of the mixture properties, and the other one with optimal structures requiring four interconnecting stages instead of the two normally used for Petlyuk sequences. An application for the separation of azeotropic mixtures is also included. The optimal placement of vapor-liquid interconnections is again shown to be different for each interconnecting stream. The GA showed a robust performance, and was practically independent on the initial values for the search variables.     

基于多重群体遗传算法的换热网络同步综合

通过对YEE换热网络分级超结构的分析和改进,建立了包含更多可行结构的换热网络超结构及其数学模型,扩大了网络结构的搜索范围。针对普通遗传算法和其他优化算法无法保证换热网络综合质量和效率的缺点,结合多重群体遗传算法进行网络优化综合,提高优化过程的稳定性,该方法将换热网络结构信息转化为种群和繁殖群体中个体的染色体信息,选择繁殖种群中优秀个体进入种群淘汰较差个体,实现种群的逐步新陈代谢。利用多重群体遗传算法对实际换热网络问题进行了优化。结果表明:多重群体遗传算法能有效提高换热网络优化的稳定性和鲁棒性,在优化变量和非凸性增加时,能获得综合性能良好的换热网络结构。     

Predicting and interpreting oxide glass properties by machine learning using large datasets

With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller datasets for each property investigated. Using the decision tree induction, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algorithms, we induced predictive models for glass transition temperature, liquidus temperature, elastic modulus, thermal expansion coefficient, refractive index, and Abbe number. Moreover, each model was induced with default and tuned hyperparameter values. We demonstrate that, apart from the elastic modulus (which had the smallest training dataset), the induced predictive models for the other five properties yield a comparable uncertainty to the usual data spread. However, for glasses with extremely low or high values of these properties, the prediction uncertainty is significantly higher. Finally, as expected, glasses containing chemical elements that are poorly represented in the training set yielded higher prediction errors. The method developed here calls attention to the success and possible pitfalls of machine learning algorithms. The analysis of the SHAP values indicated the key elements that increase or decrease the value of the modeled properties. It also estimated the maximum possible increase or decrease. Insights gained by this analysis can help empirical compositional tuning and computer-aided inverse design of glass formulations.     

Enhanced non-linear optical properties of Eu3+ activated glasses by embedding silver nanoparticles

Tri-positive lanthanide ion (Eu³⁺) activated glasses doped with different concentrations of silver (Ag0₎ nanoparticles obtained using thermal reducing agent were fabricated by applying the method of melt quench. The formation of Ag⁰ nanoparticles in glasses was revealed by the surface plasmon resonance (SPR) peak in the absorption spectra. Transmission electron microscopic measurements confirmed the presence of spherically shaped Ag⁰ nanoparticles of different size distribution. The absorption spectra showed a red–shift of the SPR peak with an increase in AgNO₃ concentration occurring through Ostwald's ripening process because of the growth of particle size (as evidenced from microscope images). The non-linear optical (NLO) and optical limiting measurements were performed in the near infrared spectral region and femtosecond pulse excitation. The non-linear parameters were found to increase as the AgNO₃ concentration increased to 0.6 mol %, however, the parameters subsequently decreased at higher doping level. The optical limiting threshold values demonstrated a reverse trend. The increase in non-linear optical properties regarding Ag nanoparticles concentration attributed to the enhancement of polarizabilities of glasses that occurred through local field stimulated by SPR of Ag nanoparticles when exposed to laser radiation of high energy. The increase in NLO coefficients (particularly the non-linear absorption coefficient) and the decrease in optical limiting threshold values with AgNO₃ concentration (up to 0.6 mol %) indicated that these glasses containing 0.6 mol % AgNO₃ are useful for the construction of the power optical limiters that function at the infrared region in the femtosecond pulse regime.     

Tannery wastewater treatment by electro-oxidation coupled with a biological process

The paper is aimed at defining the best option of a combination of electrochemical and biological processes for tannery wastewater treatment. Kinetic data for the electrochemical process are derived from an extensive experimental study, while those relating to biological processes are taken from the authors previously published studies or from the literature. Four different options based on a combination of electrochemical oxidation and biological treatment are defined and compared with the traditional tannery wastewater treatment with respect to the total required volume, energy need and the quantity of generated sludge.     

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains. After the feasible region is obtained, the sequential quadratic programming (SQP) is implemented to improve the solution. The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm, and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment.     

Orientational dynamics of liquids confined in nanoporous sol-gel glasses studied by optical kerr effect spectroscopy

When a liquid is restricted to volumes on a molecular distance scale, its orientational and translational dynamics are perturbed strongly by the confinement. Nanoporous sol-gel glasses are an excellent model system for studying the effects of confinement on the behavior of liquids, and in this Account we review experiments in which ultrafast optical Kerr effect spectroscopy has been used to study the orientational dynamics of liquids confined in these media. We contrast the effects of confinement on the orientational dynamics of weakly wetting, strongly wetting, and networked liquids, and we discuss the influence of factors such as pore size, molecular shape, and surface chemistry.     

High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach

A numerical approach combining finite element modeling and machine learning is used to inform the material performance of an alumina ceramic tile undergoing high-velocity impact. In this study, the alumina ceramic tile is simulated by incorporating a user-defined Johnson–Holmquist–Beissel (JHB) material model within the framework of smoothed particle hydrodynamics (SPH) in LS-DYNA finite element software. The implementation of the JHB model is verified by comparing equivalent stress–pressure responses through a single element simulation test. After implementation, the computational framework is simulated across our chosen range of conditions by matching the results from both plate impact experiments and ballistic testing from the literature. The computational model is then used to generate training data sets for an artificial neural network (ANN) to predict the residual velocity and projectile erosion for an alumina ceramic tile undergoing high-velocity impact in the SPH framework. The ANN is then used to perform a sensitivity analysis involving exploring the effect of mechanical properties (e.g., strength and shear modulus) and impact simulation geometries (e.g., thickness of ceramic tile) on material performance (i.e., residual projectile velocity and erosion). Overall, this study shows the capability of the FEM-ANN approach in studying the high-velocity impact on ceramic tiles and is applicable to guide the structural-scale design of ceramic-based protection systems.     

Modified genetic algorithm with sampling techniques for chemical engineering optimization

In this work, we develop a new efficient technique to enhance the optimization ability, and to improve the convergence speed of genetic optimization algorithm. We investigate and introduce a number of sampling techniques to generate a good set of initial population that encourages the exploration through out the search space and hence achieves better discovery of possible global optimum in the solution space. The introduced sampling techniques include Latin hypercube sampling (LHS), Faure sequence sampling (FSS), and Hammersley sequence sampling (HSS). The performances of the proposed algorithms and a conventional genetic algorithm using uniformly random population are compared, both in terms of solution quality and speed of convergence. A number of test problems and a case study, optimization of multi-effect distillation, demonstrate the feasibility and effectiveness of the proposed techniques. With the same parameters, our technique provides a better solution and converge to the global optimum faster than the traditional genetic algorithm.     

电感耦合等离子体发射光谱法测定镍−磷合金镀层中的磷

采用硝酸溶解从基体剥离下来的镍-磷合金镀层试样,以基体匹配法配制系列校准曲线溶液,选择213.617nm作为磷的分析谱线,建立了电感耦合等离子体发射光谱法(ICP-OES)测定镍-磷合金镀层中磷含量的方法。该方法的校准曲线的线性相关系数大于0.999,实际样品测定结果的相对标准偏差不大于3%,能够满足镍-磷合金镀层的常规和快速检验的需求。     

Evaluation of halogenated polyimide etching for optical waveguide fabrication by using inductively coupled plasma

Oxygen plasma etching of a series of halogenated polyimides was carried out for low‐loss waveguide fabrication by using inductively coupled plasma (ICP). The effects of etching parameters such as ICP power, rf power, and O₂ flow rate on the etching rate and etching profile of polymer films were investigated. The increase in the etch rate with the ICP power and the rf power was observed. Both the vertical profile and sidewall roughness were found to be related to the ion energy (dc bias). By optimizing these parameters, a vertical profile and a smooth sidewall were obtained by 500 W of ICP power, 150 W of rf power, 5 mTorr of chamber pressure, and 40 sccm of the O₂ flow rate. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 79: 176–182, 2001