Skyrmion-(Anti)Vortex Coupling in a Chiral Magnet-Superconductor Heterostructure

We report experimental coupling of chiral magnetism and superconductivity in [IrFeCoPt]/Nb heterostructures. The stray field of skyrmions with radius ≈50  nm is sufficient to nucleate antivortices in a 25 nm Nb film, with unique signatures in the magnetization, critical current, and flux dynamics, corroborated via simulations. We also detect a thermally tunable Rashba-Edelstein exchange coupling in the isolated skyrmion phase. This realization of a strongly interacting skyrmion-(anti)vortex system opens a path toward controllable topological hybrid materials, unattainable to date.

A perpendicular field electromagnet with a 250 mm access bore

We present a laboratory electromagnet capable of generating magnetic fields up to ±0.48 T, specifically designed as a perpendicular flux source for thin film samples in an ambient environment. The magnet features a 250 mm diameter clear access bore above the sample plane, thus offering compatibility with a wide variety of experimental apparatus. Despite its generous size, the magnet thermally dissipates less than 1 kW at maximum field. A shaped ferromagnetic core is used to amplify and homogenize the field B, leading to an estimated uniformity of ±1.5 mT (≲0.3%) in B within a 28 mm² zone at maximum field. The sample stage is thermally regulated and isolated from the magnet, enabling temperature control with ±5 mK precision even at elevated magnetic fields.

An ultra-high-vacuum rotating sample manipulator with cryogenic cooling

We report a homebuilt ultra-high-vacuum (UHV) rotating sample manipulator with cryogenic cooling. The sample holder is thermally anchored to a built-in cryogenic cold head through flexible copper beryllium strips, permitting continuous sample rotation. A similar contact mechanism is implemented for electrical wiring to the sample holder for thermometry. The apparatus thus enables continuous sample rotation at regulated cryogenic temperatures in a UHV environment. We discuss applications of this apparatus for cryogenic sputtering.

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.