Bonding characteristics of the Al_2O_3-metal composite coating fabricated onto carbon steel by combustion synthesis

The fabrication of an alumina-metal composite coating onto a carbon steel substrate by using a self-propagating high-temperature synthesis technique was demonstrated. The effects of the type and thickness of the pre-coated layer on the binding structure and surface qual- ity of the coating were systematically investigated. The macrostructure, phase composition, and bonding interface between the coating and the substrate were investigated by scanning electronic microscopy （SEM）, X-ray diffraction （XRD）, and energy-dispersive X-ray spectrometry （EDS）. The diffraction patterns indicated that the coating essentially consisted of α-Al2O3, Fe（Cr）, and FeO-Al2O3. With an increase in the thickness of the pre-coated working layer, the coating became more smooth and compact. The transition layer played an important role in enhancing the binding between the coating and the substmte. When the pre-coated working layer was 10 mm and the pre-coated transition layer was 1 ram, a compact structure and metallurgical bonding with the substrate were obtained. Thermal shock test results indicated that the ceramic coating exhibited good thermal shock resistance when the sample was rapidly quenched from 800℃ to room temperature by plunging into water.

Fabrication and characterization of Al_2O_3-metal composite coating on steel plate with nonpressure combustion synthesis

Al2O3-metal composite coatings with different reactants and diluents were fabricated on mild steel plate with nonpressure combustion synthesis process. The coat-ings were characterized by means of X-ray diffraction, scanning electron microscopy, and energy-dispersive spec-trometry, respectively. Thermal shock tests were carried out to determine the bond strength of the coating with the steel substrate. The results indicate that the coating is composed of α-A1203, α-（Fe-Cr） and Al2SiO5 as the main phases. It is found that the coating with the diluents of Al2O3-SiO2 and transition layer of Al2O3-Cr presents the hi.ghest hardness of 2270 HV0.2 and the lowest porosity of 3.93 %. Owing to a metallurgical bond of the coating-to-substrate, the coating exhibits a good thermal shock resistance.

A simple method to synthesize worm-like AlN nanowires and its field emission studies

The worm-like AlN nanowires are fabricated by the plasma-enhanced chemical vapor deposition(PECVD)on Si substrates through using Al powder and N2 as precursors,CaF2 as fluxing medium,Au as catalyst,respectively.The as-grown worm-like AlN nanowires each have a polycrystalline and hexagonal wurtzite structure.Their diameters are about 300 nm,and the lengths are over 10μm.The growth mechanism of worm-like AlN nanowires is discussed.Hydrogen plasma plays a very important role in forming the polycrystalline structure and rough surfaces of worm-like AlN nanowires.The worm-like AlN nanowires exhibit an excellent field-emission(FE)property with a low turn-on field of 4.5 V/μm at a current density of 0.01 mA/cm^(2) and low threshold field of 9.9 V/μm at 1 mA/cm^(2).The emission current densities of worm-like AlN nanowires each have a good stability.The enhanced FE properties of worm-like AlN nanowires may be due to their polycrystalline and rough structure with nanosize and high aspect ratio.The excellent FE properties of worm-like AlN nanowires can be explained by a grain boundary conduction mechanism.The results demonstrate that the worm-like AlN nanowires prepared by the proposed simple and the PECVD method possesses the potential applications in photoelectric and field-emission devices.

Prediction of sintered density of binary W(Mo)alloys using machine learning

Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo)alloys,which exhibit excellent application prospects at high temperatures.The properties of W(Mo)alloys are closely related to the sintered density.However,controlling the sintered density and porosity of these alloys is still challenging.In the past,the regulation methods mainly focused on timeconsuming and costly trial-and-error experiments.In this study,the sintering data for more than a dozen W(Mo)alloys constituted a small-scale dataset,including both solid and liquid phases sintering.Furthermore,simple descriptors were used to predict the sintered density of W(Mo)alloys based on the descriptor selection strategy and machine learning method(ML),where ML algorithm included the least absolute shrinkage and selection operator(Lasso)regression,k-nearest neighbor(k-NN),random forest(RF),and multi-layer perceptron(MLP).The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy(R>0.950).By further predicting the sintered density of W(Mo)alloys using different sintering processes,the error between the predicted and experimental values was less than 0.063,confirming the application potential of the model.