The formed surface characteristics of SiCf/SiC composite in the nanosecond pulsed laser ablation

For the advantages of high-temperature resistance, corrosion resistance and ultra-high hardness, SiC_f/SiC composite is becoming a preferred material for manufacturing aero-engine parts. However, the anisotropy and heterogeneity bring great challenges to the processing technology. In this study, a nanosecond pulsed laser is applied to process SiC_f/SiC composite, where the influence of the scanning speed and laser scanning direction to the SiC fibers on the morphology of ablated grooves is investigated. The surface characteristics after ablation and the involved chemical reaction of SiC_f/SiC are explored. The results show that the increased laser scanning speed, accompanied by the decreasing spot overlap rate, leads to the less accumulation of energy on the material surface, so the ablation effect drops. In addition, for the anisotropy of the SiC_f/SiC material, the obtained surface characteristics are closely dependent on the laser scanning direction to the SiC fibers, resulting in different groove morphology. The element composition and phase analysis of the machined surface indicate that the main deposited product is SiO₂ and the carbon substance. The results can provide preliminary technical support for controlling the machining quality of ceramic matrix composites.     

Effect of support and reduction temperature in the hydrogenation of CO2 over the Cu–Pd bimetallic catalyst with high Cu/Pd ratio

Metal-support interaction and catalyst pretreatment are important for industrial catalysis. This work investigated the effect of supports (SiO₂, CeO₂, TiO₂ and ZrO₂) for Cu–Pd catalyst with high Cu/Pd ratio (Cu/Pd = 33.5) regarding catalyst cost, and the reduction temperatures of 350 °C and 550 °C were compared. The activity based on catalyst weight follows the order of Si > Ce > Zr > Ti when reduced at 350 °C. The reduction temperature leads to the surface reconstruction over the SiO₂, CeO₂ and TiO₂ catalysts, while results in phase transition over Cu–Pd/ZrO₂. The effect of reduction temperature on catalytic performance is prominent for the SiO₂ and ZrO₂ supported catalysts but not for the CeO₂ and TiO₂ ones. Among the investigated catalysts, Zr-350 exhibits the highest methanol yield. This work reveals the importance of the supports and pretreatment conditions on the physical-chemical properties and the catalytic performance of the Cu–Pd bimetallic catalysts.     

Crystalline phase of TiO2 nanotube arrays on Ti–35Nb–4Zr alloy: Surface roughness,electrochemical behavior and cellular response

An investigation was made into the electrochemical, structural and biological properties of self-organized amorphous and anatase/rutile titanium dioxide (TiO₂) nanotubes deposited on Ti–35Nb–4Zr alloy through anodization-induced surface modification. The surface of as-anodized and heat-treated TiO₂ nanotubes was analyzed by field emission scanning electron microscopy (FE-SEM), revealing morphological parameters such as tube diameter, wall thickness and cross-sectional length. Glancing angle X-ray diffraction (GAXRD) was employed to identify the structural phases of titanium dioxide, while atomic force microscopy (AFM) was used to measure surface roughness associated with cell interaction properties. The electrochemical stability of TiO₂ was examined by electrochemical impedance spectroscopy (EIS) and the results obtained were correlated with the microstructural characterization. The in vitro bioactivity of as-anodized and crystallized TiO₂ nanotubes was also analyzed as a function of the presence of different TiO₂ polymorphic phases. The results indicated that anatase TiO₂ showed higher surface corrosion resistance and greater cell viability than amorphous TiO₂, confirming that TiO₂ nanotube crystallization plays an important role in the material's electrochemical behavior and biocompatibility.     

The surface carboxyl group of carbonaceous microspheres effects on the synthesis and structure of SiOC ceramics

In this study, C/SiOC and C/SiO₂ composites were prepared by using carbonaceous microspheres with different surface functional groups. Carbonaceous microspheres based on hydrothermal reaction of glucose contains hydroxyl group, while the surface carboxyl group increases after NaOH etching. The hydroxyl group increases the oxygen-enriched structural units of SiOC ceramics, and the C spheres are closely enwrapped in SiOC matrix after pyrolysis at 900 °C. However, the interfacial reaction of surface carboxyl with Si–OH results in the formation of cristobalite SiO₂, and C spheres are not only encased inside the SiOC matrix, but also dispersed outside of SiOC ceramics. After removal of C via calcination at 500 °C for 5 h, C/SiOC and C/SiO₂ composites are transformed into amorphous SiO₂ and cristobalite SiO₂, respectively. The thermogravimetric analysis indicates the oxidation resistance of SiOC is superior to that of C and SiO₂.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Morphological study of thin films: Simulation and experimental insights using horizontal visibility graph

We studied the morphological nature of various thin films such as silicon carbide (SiC), diamond (C), germanium (Ge), and gallium nitride (GaN) on silicon substrate Si(100) using the pulsed laser deposition (PLD) method and Monte Carlo simulation. We, for the first time, systematically employed the visibility algorithm graph to meticulously study the morphological features of various PLD grown thin films. These thin-film morphologies are investigated using random distribution, Gaussian distribution, patterned heights, etc. The nature of the interfacial height of individual surfaces is examined by a horizontal visibility graph (HVG). It demonstrates that the continuous interfacial height of the silicon carbide, diamond, germanium, and gallium nitride films are attributed to random distribution and Gaussian distribution in thin films. However, discrete peaks are obtained in the brush and step-like morphology of germanium thin films. Further, we have experimentally verified the morphological nature of simulated silicon carbide, diamond, germanium, and gallium nitride thin films were grown on Si(100) substrate by pulsed laser deposition (PLD) at elevated temperature. Various characterization techniques have been used to study the morphological, and electrical properties which confirmed the different nature of the deposited films on the Silicon substrate. Decent hysteresis behavior has been confirmed by current-voltage (IV) measurement in all the four deposited films. The highest current has been measured for GaN at ～60 nA and the lowest current in SiC at ～30 nA level which is quite low comparing with the expected signal level (μA). The HVG technique is suitable to understand surface features of thin films which are substantially advantageous for the energy devices, detectors, optoelectronic devices operating at high temperatures.     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

Experimental study of crystal anisotropy based on ultra-precision cylindrical turning of single-crystal calcium fluoride

To realize ultimately efficient signal processing, it is necessary to replace electrical signal processing circuits with optical ones. The optical micro-resonator, which localizes light at a certain spot, is an essential component in optical signal processing. Single-crystal calcium fluoride (CaF₂) is the most suitable material for a highly efficient optical micro-resonator. The CaF₂ resonator can only be manufactured by ultra-precision machining processes, because its crystal anisotropy does not allow the application of chemical etching. However, the optical micro-resonator's performance depends definitely on the surface integrity.This study investigated the relationship between surface quality after ultra-precision machining and crystal anisotropy. Firstly, crack initiation was investigated on the (1 0 0), (1 1 0), and (1 1 1) planes using the micro-Vickers hardness test. Secondly, brittle-ductile transition was investigated by orthogonal cutting tests. Finally, cutting performance of cylindrical turning was evaluated, which could be a suitable method for manufacturing the CaF₂ resonator. The most difficult point in cylindrical turning of CaF₂ is that the crystalline plane and cutting direction vary continuously. In order to manufacture the CaF₂ optical micro-resonator more efficiently, analysis was conducted on crack initiation and surface quality of all crystallographic orientations from the perspective of slip system and cleavage.     

Cathode plasma electrolytic deposition of Al2O3 coatings doped with SiC particles

Semiconductor particles doped Al₂O₃ coatings were prepared by cathode plasma electrolytic deposition in Al(NO₃)₃ electrolyte dispersed with SiC micro- and nano-particles (average particle sizes of 0.5–1.7?µm and 40?nm respectively). The effects of the concentrations and particle sizes of the SiC on the microstructures and tribological performances of the composite coatings were studied. In comparison with the case of dispersing with SiC microparticles, the dispersion of SiC nanoparticles in the coatings was more uniform. When the concentration of SiC nanoparticles was 5?g/L, the surface roughness of the composite coating was reduced by 63%, compared with that of the unmodified coating. Friction results demonstrated that the addition of 5?g/L SiC nanoparticles reduced the friction coefficient from 0.60 to 0.38 and decreased the wear volume under dry friction. The current density and bath voltage were measured to analyze the effects of SiC particles on the deposition process. The results showed that the SiC particles could alter the electrical behavior of the coatings during the deposition process, weaken the bombardment of the plasma, and improve the structures of the coatings.