Material removal profile prediction and experimental validation for obliquely axial ultrasonic vibration-assisted polishing of K9 optical glass

K9 optical glass is one of the typical components in optical systems. However, because of its poor fracture resistance, it is difficult to polish it with ultra-precision and high-efficiency and without any surface damage simultaneously. The emergence of the obliquely axial ultrasonic vibration-assisted polishing (UVAP) method can solve this problem which encounters in polishing efficiency and shape accuracy. However, due to the unclear material removal profile (MRP) mechanism, obliquely axial UVAP is not widely used in the processing field. This paper introduces the obliquely axial UVAP method in research processes, mainly focusing on the fixed point MRP analysis of the obliquely axial UVAP. Based on Hertz's contact theory, polishing pressure, the length of the semi-long axis (LLA) and the length of the semi-short axis (LSA) of the contact area are calculated under ultrasonic vibration conditions. Meanwhile, the relative linear velocity distribution of the oblique polishing tool in the instantaneous contact area is modeled by mathematical geometry method. A novel model of the MRP distribution for obliquely axial UVAP is proposed following the Preston equation. Subsequently, a series of polishing experiments were carried out to verify this model. The results show that the numerical model has good agreement with the experimental results on MRP, LLA, LSA, material removal depth and material removal rate (MRR). In addition, the material removal capability can be significantly improved by larger ultrasonic amplitude and larger oblique angle. This model not only more clearly elucidates the processing mechanism of obliquely axial UVAP, but also provides theoretical support for the polishing of free-form optical lenses.     

Synthesis of nanopowders with low agglomeration by elaborating Φ values for producing Gd2O3-MgO nanocomposites with extremely fine grain sizes and high mid-infrared transparency

Refining ceramic microstructures to the nanometric range to minimize light scattering provides an interesting methodology for developing novel optical ceramic materials. In this work, we reported the fabrication and properties of a new nanocomposite optical ceramic of Gd₂O₃-MgO. The citric acid sol-gel combustion method was adopted to fabricate Gd₂O₃-MgO nanocomposites with fine-grain sizes, dense microstructures and homogeneous phase domains. Nanopowders with low agglomeration and improved sinterability can be obtained by elaborating Φ values. Further refining of the microstructure of the nanocomposites was achieved by elaborating the hot-pressing conditions. The sample sintered at 65 MPa and 1300 °C showed a quite high hardness value of 14.3 ± 0.2 GPa, a high transmittance of 80.3 %–84.7 % over the 3?6 μm wavelength range, due mainly to its extremely fine-grain size of Gd₂O₃ and MgO (93 and 78 nm, respectively) and high density.     

Microstructure and coloring mechanism of iron spots on bluish white porcelain from Jingdezhen of the Song Dynasty

Jingdezhen is famous for its bluish white (Qingbai) porcelains of the Song Dynasty, and those decorated with iron spots are distinctive among them. Herein, iron spots on a bluish white porcelain were investigated using a series of microscopic and spectroscopic characterizations. We found the decreasing iron content from more than 8 wt% to about 2 wt% during the glaze color transition from rusty to brown and finally into green, which built a connection on the coloring mechanism of iron-rich crystallized glaze and celadon glaze. We identified the rare ε-Fe₂O₃, a promising magnetic material, in both the dark brown crystals and the triangular crystals in the rusty area, which is its first discovery among bluish white porcelains. Based on these findings, we discussed the coloring mechanism of iron-spot decoration along with the physical form of the iron oxide crystals, indicating the partially reducing atmosphere during firing process.     

Graphene-Based Heterostructure Composite Sensing Materials for Detection of Nitrogen-Containing Harmful Gases

Graphene-based heterostructure composite is a new type of advanced sensing material that includes composites of graphene with noble metals/metal oxides/metal sulfides/polymers and organic ligands. Exerting the synergistic effect of graphene and noble metals/metal oxides/metal sulfides/polymers and organic ligands is a new way to design advanced gas sensors for nitrogen-containing gas species including NH₃ and NO₂ to solve the problems such as poor stability, high working temperature, poor recovery, and poor selectivity. Different fabrication methods of graphene-based heterostructure composite are extensively studied, enabling massive progress in developing chemiresistive-type sensors for detecting the nitrogen-containing gas species. With the components of noble metals/metal oxides/metal sulfides/polymers and organic ligands which are composited with graphene, each material has its attractive and unique electrical properties. Consequently, the corresponding composite formed with graphene has different sensing characteristics. Furthermore, working ambient gas and response type can affect gas-sensitive characteristic parameters of graphene-based heterostructure composite sensing materials. Moreover, it requires particular attention in studying gas sensing mechanism of graphene-based heterostructure composite sensing materials for nitrogen-containing gas species. This review focuses on related scientific issues such as material synthesis methods, sensing performance, and gas sensing mechanism to discuss the technical challenges and several perspectives.     

Flash cosintering of a lanthanum strontium cobalt ferrite nanofibre/Gd-doped ceria bilayer structure

For solid oxide fuel cells, an important structural requirement is that the electrolyte layer needs to be dense and the electrode layer porous, which is difficult to obtain by conventional cosintering. In this work, flash cosintering of a double layer structure consisting of a Gd-doped ceria substrate with a lanthanum strontium cobalt ferrite nanofibre coating is investigated. Experimental and finite element modelling results reveal that when the LSCF layer is connected to the electrode, the heat is concentrated in the LSCF layer, which leads to a huge temperature gradient and introduces severe cracking. When the LSCF layer is electrically isolated from the electrode, the heat is concentrated in the GDC layer, and the temperature gradient is dramatically reduced. In this situation, the density of GDC can reach 92.86% while a high porosity of 52.26% is maintained in the LSCF layer, which is higher than that of the conventional cosintered sample.     

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.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

The role of carbon in microstructure evolution of SiBCO ceramics

The composition of polymer derived ceramics could be readily tuned through controlling the structure and element content of the polymer precursors, and investigation on the effect of the element on microstructure evolution is important to the design of advanced ceramics. In this article, the effect of carbon content in SiBCO polymer precursors was systematically investigated. The polymer network and thermal stability of polymer precursors and the carbon content of pyrolyzed SiBCO ceramic could be readily tuned by controlling the DVB amount used. Carbon contributed to the formation of graphitic carbon in SiBC_xO ceramics and inhibited the growth of β–SiC and SiO₂ crystals at 1600 °C, but lead to an increase in the graphitic carbon phase at 1800 °C.     

A kinetic model involving oxidation and volatilization in SiC-B4C-xAl2O3 ceramics system

In this paper, a new kinetic model considering both oxidation and volatilization kinetics is established and applied to analyze the oxidation of SiC-B₄C-xAl₂O₃ ceramics and other systems in various oxidation conditions. The effects of diffusion area and volume changes during the oxidation process are considered in this model. The physical meaning of each parameter in this model is explicit and simple. According to this model, the diffusion coefficient of species and the corresponding diffusion activation energy are easily available. The practicability of this model is well verified by the experimental data of SiC-B₄C-xAl₂O₃ and other systems oxidized under different conditions. In addition, the practice shows that the model is applicable not only to the systems where oxidation and volatilization coexist, but also to the system where only oxidation plays a major role. We hope the model proposed in this work can be used in other materials with more complex environments.