Aligned structures of mesogenic motifs in epoxy resin and their thermal conductivities.

The epoxy-based crosslinked polymer with the mesogenic group has been studied as a candidate resin material with high thermal conductivity due to the ordered structure of the mesogenic groups. In this study, we conducted all atomic molecular dynamics simulations with iterative crosslinking procedures on various epoxy resins with mesogenic motifs to investigate the effect of molecular alignment on thermal conductivity. The stacked structure of aromatic groups in the crosslinked polymer was analyzed based on the angle-dependent radial distribution function (ARDF), where the resins were categorized into three groups depending on their monomer shapes. The thermal conductivities of resins were higher than those of conventional polymers due to the alignment of aromatic groups, but no distinct correlation with the ARDF was found. Therefore, we conducted a further study about two structural factors that affect the alignment and the TC by comparing the resins within the same groups: the monomer with an alkyl spacer and functional groups in hardeners. The alkyl chains introduced in the epoxy monomers induced more stable stacking of aromatic groups, but thermal conductivity was lowered as they inhibited phonon transfer on the microscopic scale. In the other case, the functional groups in the hardener lowered the TC when the polar interaction with other polar groups in the monomer was strong enough to compete with the pi-pi interaction. These results represent how various chemical motifs in mesogenic groups affect their alignment on the atomistic scale, and also how they have effects on the TC consequently.

Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.

Spin-Orbit Torque Switching in an All-Van der Waals Heterostructure.

Current-induced control of magnetization in ferromagnets using spin-orbit torque (SOT) has drawn attention as a new mechanism for fast and energy efficient magnetic memory devices. Energy-efficient spintronic devices require a spin-current source with a large SOT efficiency (ξ) and electrical conductivity (σ), and an efficient spin injection across a transparent interface. Herein, single crystals of the van der Waals (vdW) topological semimetal WTe2  and vdW ferromagnet Fe3 GeTe2 are used to satisfy the requirements in their all-vdW-heterostructure with an atomically sharp interface. The results exhibit values of ξ ≈ 4.6 and σ ≈ 2.25 × 105  Ω-1 m-1 for WTe2 . Moreover, the significantly reduced switching current density of 3.90 × 106 A cm-2 at 150 K is obtained, which is an order of magnitude smaller than those of conventional heavy-metal/ferromagnet thin films. These findings highlight that engineering vdW-type topological materials and magnets offers a promising route to energy-efficient magnetization control in SOT-based spintronics.

MAX-Phase Films Overcome Scaling Limitations to the Resistivity of Metal Thin Films.

Metal thin films have been widely used as conductors in semiconductor devices for several decades. However, the resistivity of metal thin films such as Cu and TiN increases substantially (>1000%) as they become thinner (<10 nm) when using high-density integration to improve device performance. In this study, the resistivities of MAX-phase V2AlC films grown on sapphire substrates exhibited a significantly weaker dependence on the film thickness than conventional metal films that resulted in a resistivity increase of only 30%, as the V2AlC film thickness decreased from approximately 45 to 5 nm. The resistivity was almost identical for film thicknesses of 10-50 nm. The small change in the resistivity of V2AlC films with decreasing film thickness originated from the highly ordered crystalline quality and a small electron mean free path (11-13.6 nm). Thus, MAX-phase thin films have great potential for advanced metal technology applications to overcome the current scaling limitations of semiconductor devices.