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Transition metal dichalcogenide monolayers and heterostructures are highly tunable material systems that provide excellent models for physical phenomena at the two-dimensional (2D) limit. While most studies to date have focused on electrons and electron-hole pairs, phonons also play essential roles. Here, we apply ultrafast electron diffraction and diffuse scattering to directly quantify, with time and momentum resolution, electron-phonon coupling (EPC) in monolayer molybdenum disulfide (MoS$_{2}$) and phonon transport from the monolayer to a silicon nitride (Si$_3$N$_4$) substrate. Optically generated hot carriers result in a profoundly anisotropic distribution of phonons in the monolayer on the $\sim$ 5 ps time scale. A quantitative comparison with ab-initio ultrafast dynamics simulations reveals the essential role of dielectric screening in weakening EPC. Thermal transport from the monolayer to the substrate occurs with the phonon system far from equilibrium. While screening in 2D is known to strongly affect equilibrium properties, our findings extend this understanding to the dynamic regime.

Transition-metal dichalcogenide monolayers and heterostructures are highly tunable material systems that provide excellent models for physical phenomena at the two-dimensional (2D) limit. While most studies to date have focused on electrons and electron-hole pairs, phonons also play essential roles. Here, we apply ultrafast electron diffraction and diffuse scattering to directly quantify, with time and momentum resolution, electron-phonon coupling (EPC) in monolayer molybdenum disulfide and phonon transport from the monolayer to a silicon nitride substrate. Optically generated hot carriers result in a profoundly anisotropic distribution of phonons in the monolayer within ∼5 ps. A quantitative comparison with

We discuss modeling, algorithmic, and software aspects that allow a simulation tool called Chrono::Granular to run billion-degree-of-freedom dynamics problems on commodity hardware, i.e., a workstation with one GPU. The ability to scale the solution to large problem sizes is traced back to an adimensionalization process combined with the use of mixed-precision data types that reduce memory pressure and improve arithmetic intensity, judicious use of the memory ecosystem on GPU cards as exposed by CUDA on Nvidia architectures, and a software implementation that prioritizes execution speed over modeling generality. The simulation approach is demonstrated for 3D scenarios with up to 710 million bodies for the frictionless case (of relevance in emulsions), and up to 210 million bodies for scenarios with friction (of relevance in terradynamics, additive manufacturing, soft-matter physics). The frictional contact model used draws on the Discrete Element Method (DEM). A performance benchmark shows linear scaling with problem size up to GPU memory capacity. The implementation has an application programming interface that enables it to interact in a cosimulation framework with third-party dynamics engines. This interaction is anchored by a force–displacement data exchange protocol that brings in external bodies as geometries defined by triangle meshes. We demonstrate the cosimulation mechanism by interfacing to an open source, multiphysics simulation engine called Chrono. Therein, triangular meshes define moving boundary conditions for Chrono::Granular, which in turn provides forces and torques acting on the triangular meshes. Several tests are considered for validation and scaling analysis purposes. The limiting aspects of the current implementation are its exclusive support of monodisperse granular systems, and its lack of handling geometries beyond spheres. These limitations are addressed by ongoing work.

Tin selenide, a thermoelectric semiconductor, has gained attention for its high thermoelectric efficiency. Time-resolved angle-resolved photoemission spectroscopy reveals longer hot carrier lifetimes at the conduction band minimum than at the Γ point.

Background. Lifetime trajectories of mental ill health are often established during adolescence. Effective interventions to prevent the emergence of mental health problems are needed. In the current study we assessed the efficacy of the CBT informed Climate Schools universal eHealth preventive mental health program, relative to a control. We also explored whether the intervention had differential effects on students with varying degrees of social connectedness. Method. We evaluated the efficacy of the Climate Schools mental health program (19 participating schools; average age at baseline was 13.6) versus a control group (18 participating schools; average age at baseline was 13.5) which formed part of a large cluster randomised controlled trial in Australian schools. Measures of internalising problems, depression and anxiety were collected at baseline, immediately following the intervention and at 6-, 12- and 18-months post intervention. Immediately following the intervention, 2539 students provided data on at least one outcome of interest (2065 students at 18 months post intervention). Results. Compared to controls, we found evidence that the stand alone-alone mental health intervention improved knowledge of mental health, however there was no evidence that the intervention improved other mental health outcomes, relative to a control. Student’s social connectedness did not influence intervention outcomes. Conclusion. These results are consistent with recent findings that universal school-based, CBT informed, preventive interventions for mental health have limited efficacy in improving symptoms of anxiety and depression when delivered alone. We highlight the potential for combined intervention approaches, and more targeted interventions, to better improve mental health outcomes.

This study describes the implementation of a granular dynamics solver designed to run on Graphics Processing Units (GPUs). The discussion concentrates on how the Discrete Element Method (DEM) has been mapped onto the GPU architecture, the software design decisions involved in the process, and the optimizations allowed by those decisions. This solver, called Chrono::Granular, has been developed as a standalone library that can interface with other dynamics engines via triangle mesh co-simulation. A scaling analysis of the code presented herein demonstrates linear scaling with problem sizes of over two billion degrees of freedom and closing in on one billion bodies. We conclude with a study of hourglass (or hopper) mass discharge rate which compares the solver to experimental results and investigates a process for determining empirical coefficients of flow rate through simulation.