Bypassing the Brick-and-Mortar Hospital: Increasing Direct Admissions from the Emergency Department to Inpatient Hospital-at-Home.

Home hospital programs continue to grow across the United States. There are limited studies around the process of patient selection and successful acquisition from the emergency department. The article describes how an interdisciplinary team used quality improvement methodology to significantly increase the number of admissions directly from the emergency department to the Advanced Care at Home program.

Activating two-dimensional semiconductors for photocatalysis: a cross-dimensional strategy.

The emerging two-dimensional (2D) semiconductors substantially extend materials bases for versatile applications such as semiconductor photocatalysis demanding semiconductive matrices and large surface areas. The dimensionality, while endowing 2D semiconductors the unique properties to host photocatalytic functionality of pollutant removal and hydrogen evolution, hurdles the activation paths to form heterogenous photocatalysts where the photochemical processes are normally superior over these on the mono-compositional counterparts. In this perspective, we present a cross-dimensional strategy to employ theD (= 0-2) clusters or nanomaterials as activation partners to boost the photocatalytic activities of the 2D semiconductors.

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design.

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples.

A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations.

To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning.