We report a resonant inelastic x-ray scattering investigation of ultrathin epitaxial films of Ba2IrO4, and compare their low-energy magnetic and spin-orbit excitations to those of their sister compound Sr2IrO4. Due to the 180∘ Ir-O-Ir bond, the bandwidth of the magnon and spin orbiton is significantly larger in Ba2IrO4, making it difficult to describe these two types of excitations as separate well-defined quasiparticles. Both types of excitations are found to be quite sensitive to the effect of epitaxial strain.

Recent experiments in moiré transition metal dichalcogenide materials have reported the observation of a continuous bandwidth-tuned transition from a metal to a paramagnetic Mott insulator at a fixed filling of one electron per moiré unit cell. The electrical transport measurements reveal a number of puzzling features that are seemingly at odds with the theoretical expectations of an interaction-induced, but disorder-free, bandwidth-tuned metal-insulator transition.

Prior research has indicated that students in the undergraduate physics lab divide work inequitably with regard to gender. In this work, we further probed women's experiences in lab group work, focusing on women who take on managerial and leadership roles in the lab. We interviewed and surveyed women enrolled in a sophomore-level project-based lab course, drawing on a practice-linked identity framework to characterize their opportunities for engagement and identity development within the course.

The original version of this Article contained an error in the Acknowledgements, which incorrectly read ‘The authors acknowledge support by the NSF [Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials (PARADIM)] under cooperative agreement no. DMR-U638986.’. The correct version states ‘DMR-1539918’ in place of ‘DMRU638986’. This has been corrected in both the PDF and HTML versions of the Article. © The Author(s) 2023.

Linear-accelerator-based applications like x-ray free electron lasers, ultrafast electron diffraction, electron beam cooling, and energy recovery linacs use photoemission-based cathodes in photoinjectors for electron sources. Most of these photocathodes are typically grown as polycrystalline materials with disordered surfaces. In order to understand the mechanism of photoemission from such cathodes and completely exploit their photoemissive properties, it is important to develop a photoemission formalism that properly describes the subtleties of these cathodes.

Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement.

We present a comprehensive set of experimentally validated/calibrated models that capture the physics of the nanoscale spin-orbit torque (SOT) devices. We consider various effects that are prominent at nanoscale including incomplete current redistribution, interface spin mixing, and nonuniform resistivity that were ignored in the prior modeling efforts. We develop a formalism based on drift-diffusion equations and the transfer matrix method to accurately estimate spin current distribution.

Strongly correlated bosons in a lattice are a platform that can realize rich bosonic states of matter and quantum phase transitions1. While strongly correlated bosons in a lattice have been studied in cold-atom experiments2–4, their realization in a solid-state system has remained challenging5. Here we trap interlayer excitons–bosons composed of bound electron–hole pairs, in a lattice provided by an angle-aligned WS2/bilayer WSe2/WS2 multilayer. The heterostructure supports Coulomb-coupled triangular moiré lattices of nearly identical period at the top and bottom interfaces.

Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays.