Determination of shearing stiffness parameters to design multi-layer spruce beams using welding-through wood dowels

This paper presents a methodology to characterize the secant shearing stiffness of welding-through wood dowels used in multi-layer spruce beams. Welding-through wood dowel’s stiffness behaviour as well as isostatic multi-layer beams’ behaviour were investigated experimentally. These elements are made by a specifically designed machine to guarantee repeatability. A model of finite elements was developed to reproduce the bending behaviour of the beams tested from non-linear connection behaviour. This model is then used to study the influence of parameters such as: layer number, spacing of dowels, load type (concentrated or distributed), strength class and beam span. After a sensibility analysis, a parameterized regression model allows to obtain k _ser and k _u fastener secant stiffness in compliance with EN 1995-1 standard to design multi-layer beams in an elastic analytical model way. This model is based on the work by Kreuzinger (1999).     

Machine learning–based optimization for hydrogen purification performance of layered bed pressure swing adsorption

An adsorption, heat and mass transfer model for the five-component gas from coal gas (H₂/CO₂/CH₄/CO/N₂ = 38/50/1/1/10 vol%) in a layered bed packed with activated carbon and zeolite was established by Aspen Adsorption software. Compared with published experimental results, the hydrogen purification performance by pressure swing adsorption (PSA) in a layered bed was numerically studied. The results show that there is a contradiction between the hydrogen purity and recovery, so the multi-objective optimization algorithms are needed to optimize the PSA process. Machine learning methods can be used for data analysis and prediction; the polynomial regression (PNR) and artificial neural network (ANN) were used to predict the purification performance of two-bed six-step process. Finally, two ANN models combined with sequence quadratic program (SQP) algorithm were used to achieve multi-objective optimization of hydrogen purification performance. According to the analysis of the optimization results, the ANN models are more suitable for optimizing the purification performance of hydrogen than the PNR model.     

Extracting supply chain maps from news articles using deep neural networks

Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their supply network. This poses a problem as visibility of the network structure is required for tasks like effectively managing supply chain risk. In this paper, we discuss automated supply chain mapping as a means of maintaining structural visibility of a company's supply chain, and we use Deep Learning to automatically extract buyer–supplier relations from natural language text. Early results show that supply chain mapping solutions using Natural Language Processing and Deep Learning could enable companies to (a) automatically generate rudimentary supply chain maps, (b) verify existing supply chain maps, or (c) augment existing maps with additional supplier information.     

Light Gold: A Colloidal Approach Using Latex Templates

A new 18 karat light gold, composed of gold single crystals, amyloids, and a polymer latex matrix is developed. It is similar to a glassy plastic, yet lighter than aluminum and of use in watches, jewelry, radiation shielding, catalysis, and electronics. The material is prepared via a hydrogel precursor dried into an aerogel. Annealing of the polystyrene matrix under vacuum gives rise to a homogeneous template. The final apparent density and porosity of the material depend directly on the volumetric concentration of the starting solution used for hydrogel formation. After annealing, a homogeneous microstructure is obtained in which the shining gold single crystal platelets are evenly embedded in a polystyrene matrix. The material has a glass transition temperature of ≈105 °C which allows for annealing and molding above this temperature. A general scaling behavior is found for the Young's modulus of the material with the density. The Young's modulus of the material with a density of 1.7 g cm^?3 is ≈50 MPa. The density and stiffness, as well as the color, of the material can be tuned depending on the final application.     

The Potential of Hybrid Mechanistic/Data-Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns

Extensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the integration of machine learning into existing reduction approaches are discussed. First, key concepts for dynamic model reduction and their limitations are briefly reviewed. Afterwards, promising model structures for reduced hybrid mechanistic/data-driven models are outlined. Finally, crucial future challenges as well as promising research perspectives are presented.     

The effect of adsorbed species and exposure to sulfuric acid on the electrical conductance of individual single-wall carbon nanotube transistors

The electrical conductance of individual single-wall carbon nanotube (SWCNT) transistors was investigated before, during, and after soaking in concentrated sulfuric acid in air environment. The resulting doping effect has induced a shift of the Fermi level which was monitored by the electrical behavior and sought in Raman spectroscopy signature. For either metallic or semiconducting SWCNTs, the maximum conductance value was lowered during the contact with the acid; remained low for a short time after removing the acid; and recovered after long air exposure. This demonstrated the significant contribution of the physisorbed species in the measurements, specifically at the metal pad/SWCNT contact lines. The study also demonstrated that washing with sulfuric acid is an easy method for removing adsorbed species and reaching the intrinsic conductance of SWCNT-based devices.     

Refractive index variations induced by femtosecond laser direct writing in the bulk of As2S3 glass at high repetition rate

This paper deals with the refractive index variation (Δn) induced by femtosecond laser irradiation in the bulk of As₂S₃ glass in a high repetition rate regime. Extensive measurements of spatially resolved Δn profiles of photowritten channels are reported for various values of the pulse energy. Nearly Gaussian profiles are obtained for energy slightly above the threshold whereas at higher energies the structure of Δn is more complex with a negative core surrounded by a positive ring. A thermal model is applied to reproduce the behaviour of the overall structure diameter.     

Theoretical and experimental studies of (In,Ga)As/GaP quantum dots

(In,Ga)As/GaP(001) quantum dots (QDs) are grown by molecular beam epitaxy and studied both theoretically and experimentally. The electronic band structure is simulated using a combination of k·p and tight-binding models. These calculations predict an indirect to direct crossover with the In content and the size of the QDs. The optical properties are then studied in a low-In-content range through photoluminescence and time-resolved photoluminescence experiments. It suggests the proximity of two optical transitions of indirect and direct types.     

Application of Thermodynamic Models for Better Understanding and Optimizing the Hall–Heroult Process

During the last decade, important improvements have been made in the application of thermodynamic models for studying the molten cryolite system used in the Hall?CHeroult process. This approach allows a better understanding and paves the way for furthering developments in bath chemistry and molten metal processing. In this article, thermodynamic modeling is used to explore the operating windows in the reduction of alumina in molten cryolite. The impact of a range of concentrations of AlF₃, CaF₂, and Al₂O₃ in conventional or ??lithium-free?? baths is also discussed. Subsequently, the model was also used to evaluate the impact of additions of lithium fluoride to the bath. Conditions allowing an operation at lower cell voltages and lower bath temperatures were identified. The modeling approach described in this article is considered as an important innovation to revisit fundamentals, to constantly re-examine paradigms, and to identify potential modifications?in bath chemistry for improving energy efficiency and productivity of modern prebaked Hall?CHeroult cells.