Viscosity Prediction of Lubricants by a General Feed-Forward Neural Network

Modern industrial lubricants are often blended with an assortment of chemical additives to improve the performance of the base stock. Machine learning-based predictive models allow fast and veracious derivation of material properties and facilitate novel and innovative material designs. In this study, we outline the design and training process of a general feed-forward artificial neural network that accurately predicts the dynamic viscosity of oil-based lubricant formulations. The network hyperparameters are systematically optimized by Bayesian optimization, and strongly correlated/collinear features are trimmed from the model. By harnessing domain knowledge in the selection of features, the quantitative structure-property relationship model is built with a relatively simple feature set and is versatile in predicting the dynamic viscosity of lubricant oils with and without enhancement by viscosity modifiers (VMs). Moreover, partial dependency, local-interpretable model-agnostic explanations, and Shapley values consistently show that the eccentricity index, Crippen MR, and Petitjean number are important predictors of viscosity. All in all, the neural model is reasonably accurate in predicting the dynamic viscosity of lubricant solvents and VM-enhanced lubricants with an R² of 0.980 and 0.963, respectively.

Fast water desalination by carbon-doped boron nitride monolayer: transport assisted by water clustering at pores

The well-being of the ever-escalating world population hinges largely upon the adequacy of clean, fresh water. Desalination is one of the most promising approaches in such an endeavor. Using molecular dynamics simulations, we take a close look at nanoporous hexagonal boron nitride nanosheets as desalination membranes, and study how C dopants affect their performance. The calculations predict that the desalination performance of C-doped BN membranes compares favorably to that of MoS₂ membranes: the water flux through the 0% (0C_B-0C_N), 25% (3C_B-0C_N), 75% (3C_B-6C_N), and 100% C terminated BN membrane (6C_B-6C_N) is 29.9, 47.5, 95.3, and 81.5 molecules ns^-1 per pore, respectively, and there is a strong correlation between the water flux and the axial diffusion coefficient. Through our study of the effect of C content on the desalination performance, it is found that more clustering of water molecules at membrane pores due to a smaller hydration free energy and pore energy barrier assists water transport through the pores, and allows a greater water flux.

Out-of-plane structural flexibility of phosphorene

Phosphorene has been rediscovered recently, establishing itself as one of the most promising two-dimensional group-V elemental monolayers with direct band gap, high carrier mobility, and anisotropic electronic properties. In this paper, surface buckling and its effect on its electronic properties are investigated by using molecular dynamics simulations together with density functional theory calculations. We find that phosphorene shows superior structural flexibility along the armchair direction allowing it to have large curvatures. The semiconducting and direct band gap nature are retained with buckling along the armchair direction; the band gap decreases and transforms to an indirect band gap with buckling along the zigzag direction. The structural flexibility and electronic robustness along the armchair direction facilitate the fabrication of devices with complex shapes, such as folded phosphorene and phosphorene nano-scrolls, thereby offering new possibilities for the application of phosphorene in flexible electronics and optoelectronics.

Spin-dependent metallic properties of a functionalized MoS2 monolayer

Stability and electronic properties of a two-dimensional MoS₂ monolayer functionalized with atomic wires of Fe and Co are investigated using density functional theory.

Robust magnetic domains in fluorinated ReS2 monolayer

The robust metallic mid-gap states in localized domains of fluorinated ReS₂ monolayer could be useful in spintronic devices, such as spin-transfer torque and spin-wave logic devices.

Re-ordering chaotic carbon: origins and application of textured carbon

Formation of nanocrystals with preferred orientation within the amorphous carbon matrix has attracted lots of theoretical and experimental attentions recently. Interesting properties of this films, easy fabrication methods and practical problems associated with the growth of other carbon nanomaterials such as carbon nanotubes (CNTs) and graphene gives this new class of carbon nanostructure a potential to be considered as a replacement for some applications such as thermal management at nanoscale and interconnects. In this short review paper, the fabrication techniques and associated formation mechanisms of these nanostructured films have been discussed. Besides, electrical and thermal properties of these nanostructured films have been compared with CNTs and graphene.