Sealing behaviour of glass-based composites for oxygen transport membranes

In this study, seven different filler materials in different proportions were added to a Ba-Ca-Si glass matrix “H” to investigate new sealant with higher thermal expansion coefficient (CTE) value and good sealing performance for application in oxygen transport membrane (OTM). SrTi_0.75Fe_0.25O_3-δ (STF25) was used as an OTM, and the sealing partners were ferritic steel Aluchrom and pre-oxidized Aluchrom. Compatibility tests were carried out to investigate the feasibility of the composites. Higher CTE values were found in dilatometer tests on composite samples by adding 40 wt% Ag (HAg40) and 30 wt% Ni-Cr (HNC30). Gas-tightness measurements of sandwiched samples produced appropriate helium leakage rates in the range of 10^?6 mbar·l·s^?1. Sealing behaviour of sealants HAg40 and HNC30 were investigated by joining STF25 and as-delivered/pre-oxidized Aluchrom together. Scanning electron microscopy (SEM) on cross-sections of the joints revealed a homogeneous microstructure and good adherence of the glass sealants to support metals and STF25.     

Design and Fabrication of Concentration‐Gradient Generators with Two and Three Inlets in Microfluidic Chips

A simple and low‐cost method for designing and fabricating concentration‐gradient generators with two and three inlets is proposed which can generate different concentration gradients at varying flow velocities. The microchannel structure was designed in S‐shape and left‐right symmetry. The concentration‐gradient generator was simulated based on the finite element method. The microchannels were processed on a computer numerical control (CNC) engraving and milling machine on poly(methylmethacrylate) substrate, and then two concentration‐gradient generators were fabricated by hot bonding technology. The results of experiment and simulation were compared to prove the feasibility of the method. Flow velocity was an important factor for generating different concentration gradients. The concentration‐gradient profiles of the generators with two and three inlets present approximately linear and quadratic curves.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

Formation of γ-LiAlO2 nano-network in an Al-based metal matrix composite

We report the synthesis of a nanostructured γ-LiAlO₂ network in an Al-based metal matrix composite (MMC) by arc-melting a powder compact of Al–15 wt.%Li₂O. During synthesis, chemical reactions between Al and Li₂O occurred, and a two-phase Al-γ-LiAlO₂ MMC was produced. The γ-LiAlO₂ was in the form of nano-network extended evenly in the Al matrix. The apparent density and Vickers' hardness number of the MMC were determined to be 2.31 gcm^− 3 and 143, respectively, and having a relative density of 0.87. The MMC was made nano-porous by etching away Al with a 2 M NaOH solution. The sizes of pores were about 150 nm and the width of the branches in the γ-LiAlO₂ network was about 90 nm. Atomic force microscopy revealed that the branches were composed of fine grains of 50 nm in diameter. Microstructure of the network was not altered even after prolonged etching, and this suggested that γ-LiAlO₂ was chemically inert to NaOH corrosion. Specific surface area of the γ-LiAlO₂ nano-network was measured to be 79 m²/g, and its porosity was determined to be 43.7%.     

Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the proximity relations between points in the low-dimensional subspace mimic the proximity relations between points in the response space. Such a projection facilitates the design of efficient regression models and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. The regression step after projection is performed using computationally light models with low maintenance cost like Multiple Linear Regression and Locally Linear Regression with k-NN neighbourhoods. For the evaluation, a benchmark and a full-scale calibration problem are discussed. The case studies pertain the estimation of a number of chemico-physical properties in diesel fuels and in light cycle oils, starting from near-infrared spectra. Based on the experimental results, we found that the SDPP leads to parsimonious projections that can be used to design light and yet accurate estimation models.     

Modelling of palm oil production using fuzzy expert system

The production of crude palm oil and palm kernel is a complex problem due to the influence of processing variables and environmental factors. These processing variables influence the amount of crude palm oil and palm kernel losses during processing. Instead of mathematical model, fuzzy logic approach provides a simpler and easier mechanism to describe the relationships between the processing variables and the amount of crude palm oil and palm kernel losses. In this study, four fuzzy expert system models were developed for each palm oil processing station. An approximation of centre of gravity method for defuzzification is also proposed to enable the development of the model using Microsoft^®Excel. For comparison purpose, the models were also developed using MATLAB^®. Results obtained from the Excel model are found to be very close to the results from MATLAB.     

Three-dimensional carbon nanotube based polymer composites for thermal management

In this study, the porous multiwall carbon nanotube (MWCNT) foams possessing three-dimensional (3D) scaffold structures have been introduced into polydimethylsiloxane (PDMS) for enhancing the overall thermal conductivity (TC). This unique interconnected structure of freeze-dried MWCNT foams can provide thermally conductive pathways leading to higher TC. The TC of 3D MWCNT and PDMS composites can reach 0.82 W/m K, which is about 455% that of pure PDMS, and 300% higher than that of composites prepared from traditional blending process. The obtained polymer composites not only exhibit superior mechanical properties but also dimensional stability. To evaluate the performance of thermal management, the LED modulus incorporated with the 3D MWCNT/PDMS composite as heat sink is also fabricated. The composites display much faster and higher temperature rise than the pristine PDMS matrix, suggestive of its better thermal dissipation. These results imply that the as-developed 3D-MWCNT/PDMS composite can be a good candidate in thermal interface for thermal management of electronic devices.     

Characterization of stainless steel parts by Laser Metal Deposition Shaping

Laser Metal Deposition Shaping (LMDS) is a new rapid manufacturing technology, which can build fully-dense metal components directly from the information transferred from a computer file by depositing metal powders layer by layer with neither mould nor tool. Typically, performed with stainless steel (SS) 316 powder, the orthogonal experiments combining with the ideal overlapping model were applied to ascertain the optimal processing parameters. Then the characteristics of microstructure, composition and phase of as-deposited cladding layers were analyzed through Scanning Electron Microscope (SEM) and X-ray diffraction (XRD), as well as relative model. Furthermore, the cooling rate and the solidification velocity during LMDS were evaluated based on empirical method. With the optimal parameters, some parts were fabricated without obvious defects, and then the mechanical properties of them were tested. Finally, the influencing regularities of critical parameters on microstructure and properties were concluded by comparison. The results prove that the microstructure of SS 316 deposits is composed of the slender dendrites growing epitaxially from the substrate, the mechanical properties are favorable and anisotropic, and the composition is uniform. Besides, the microstructure morphology and the mechanical properties are affected by the varied processing parameters at different degrees. Among them, the scanning speed shows the most remarkable effects on microstructure morphology, characteristic microscale, mechanical properties, as well as geometric shape of as-deposited parts.     

The influence of scanning methods on the cracking failure of thin-wall metal parts fabricated by laser direct deposition shaping

In order to improve the quality of thin-wall metal parts fabricated by laser direct deposition shaping method, using “element birth and death” technique, a three-dimensional multitrack and multilayer thin-wall model was developed. Different scanning methods, including long-edge parallel reciprocating scanning, short-edge parallel reciprocating scanning and inter-layer orthogonal parallel reciprocating scanning, were researched. The Von Mises equivalent stress, and its X-directional, Y-directional and Z-directional principal stresses were analyzed in detail. Under the same conditions used in the simulations, the deposition experiments were conducted, and the influence of different scanning methods on the cracking failure behavior of thin-wall metal parts was discussed.