Influence of the bath pH value on the microstructure and properties of a novel electrodeposited Ni-W-Ti₃C₂T_X composite coating

Abstract

According to the previous research, the hardness of Ni-W-Ti3C2TX composite coating is 24.3% higher than that of Ni-W alloy coating without adding Ti3C2TX. Meanwhile, the wear resistance and corrosion resistance of the composite coating are significantly better than that of Ni-W coating. It is known that the electrodeposition process parameters have a great influence on the performance. The Ni-W-Ti3C2TX composite coating was prepared by the direct current deposition method. The influence of bath pH on the morphology, composition, mechanical properties, corrosion resistance and friction resistance of the coating was systematically studied. The results show that the surface morphology of the coating is improved with the increase of pH value. The content of W in the coating decreases from 44.95 wt.% to 12.66 wt.%, and the grain size decreases from 77.1 nm to 8.9 nm. The hardness of the coating increases first and then decreases with the increase of pH under the joint action of fine grain strengthening and solution strengthening. When pH is 9, the corrosion resistance and tribological properties are the best, and the corrosion current density and wear rate are 1.782×10-3 mA•cm-2 and 0.865×10-6 mm3•n-1•m-1, respectively. A suitable pH value provides excellent surface quality, appropriate grain size and good hardness for the composite coating to show excellent corrosion resistance and friction resistance.

Synthetic data enable experiments in atomistic machine learning

“…47 We also note that Huang et al have used atomic energies to train NN-based atomistic models. 69 In a wider perspective, the pre-training of NN models is a well-documented approach in the ML literature for various applications and domains, [70][71][72][73][74] and it has very recently been described in the context of interatomic potential models, 47,75,76 property prediction with synthetic pre-training data, 77 and as a means to learn generalpurpose representations for atomistic structure. 76…”