High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr–Cr₃C₂ coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.

With the development of society, human beings are facing environmental problems and an energy crisis worldwide. In this context, photocatalysis and electrocatalysis represent promising technologies to help solving these issues. Up to now, most of the catalysts intended for these usages are prepared via time-consuming wet-chemical approaches, e.g. hydrothermal or sol–gel methods. Moreover, these techniques produce powdery catalysts which need not only a post-filtration step, but also a shaping by a binder for their final applications, which makes the manufacturing cumbersome. Thermal spraying is currently a well-established deposition technique that is capable of elaborating a wide series of functional coatings based on all classes of materials (metals, polymers, ceramics) and featuring tunable compositions as well as micro- and nanostructures. In particular, thermal involving liquid feedstock, and more specifically precursor solutions or suspensions, have allowed generating a wide range of coating thicknesses, from the ten-micron to the submicron/nano scale, increasing thereby the possibilities for enhancing catalytic performances. The present review sets out marking researches relating to the preparation and testing of (i) some photocatalytic coatings intended for the degradation of aqueous organic pollutants and (ii) electrocatalytic coatings investigated as potential energy storage devices. This paper will not only deal with the preparation of catalysts via different thermal spray processes, but also will cover the adjustment of phase compositions and microstructures that are rendered possible when using liquid feedstock thermal spray techniques with the prospect of optimizing the catalytic performances.

A large magnetocaloric effect (MCE) has been achieved in a rare-earth-free CoMnSb alloy at low magnetic field and intermediate temperature. It is interesting to note that a relatively large magnetic entropy change $\mid \Delta S_M\mid$ of $2.06\mathrm{J}∕\mathrm{kg}\mathrm{K}$ with $\Delta H=0.9\mathrm{T}$ was obtained around $471\mathrm{K}$ . Arrott plots at various temperatures reveal that a second order magnetic transformation occurs near the Curie temperature. The origin of the large $\Delta S_M$ under the conditions of intermediate temperature and low field is attributed to the magnetic transition from ferromagnetic to paramagnetic. These results suggest that CoMnSb alloy is a good candidate for large MCE when being utilized in intermediate temperature refrigeration systems for some special cases. This investigation demonstrates that some transition metal alloys with large magnetic moments may exhibit large MCE, which extends the region for exploring useful MCE materials.