There are numerous studies on the growth of planar films on sp 2 -bonded two-dimensional (2D) layered materials. However, it has been challenging to grow single-crystalline films on 2D materials due to the extremely low surface energy. Recently, buffer-assisted growth of crystalline films on 2D layered materials has been introduced, but the crystalline quality is not comparable with the films grown on sp 3 -bonded three-dimensional materials. Here we demonstrate direct van der Waals epitaxy of high-quality single-crystalline GaN films on epitaxial graphene with low defectivity and surface roughness comparable with that grown on conventional SiC or sapphire substrates. The GaN film is released and transferred onto arbitrary substrates. The post-released graphene/SiC substrate is reused for multiple growth and transfer cycles of GaN films. We demonstrate fully functional blue light-emitting diodes (LEDs) by growing LED stacks on reused graphene/SiC substrates followed by transfer onto plastic tapes.

Epitaxial lift-off process enables the separation of III-V device layers from gallium arsenide substrates and has been extensively explored to avoid the high cost of III-V devices by reusing the substrates. Conventional epitaxial lift-off processes require several post-processing steps to restore the substrate to an epi-ready condition. Here we present an epitaxial liftoff scheme that minimizes the amount of post-etching residues and keeps the surface smooth, leading to direct reuse of the gallium arsenide substrate. The successful direct substrate reuse is confirmed by the performance comparison of solar cells grown on the original and the reused substrates. Following the features of our epitaxial lift-off process, a high-throughput technique called surface tension-assisted epitaxial lift-off was developed. In addition to showing full wafer gallium arsenide thin film transfer onto both rigid and flexible substrates, we also demonstrate devices, including light-emitting diode and metal-oxidesemiconductor capacitor, first built on thin active layers and then transferred to secondary substrates.

The outbreak of COVID-19 caused by SARS-CoV-2 has resulted in more than 50 million confirmed cases and over 1 million deaths worldwide as of November 2020. Currently, there are no effective antivirals approved by the Food and Drug Administration to contain this pandemic except the antiviral agent remdesivir. In addition, the trimeric spike protein on the viral surface is highly glycosylated and almost 200,000 variants with mutations at more than 1,000 positions in its 1,273 amino acid sequence were reported, posing a major challenge in the development of antibodies and vaccines. It is therefore urgently needed to have alternative and timely treatments for the disease. In this study, we used a cell-based infection assay to screen more than 3,000 agents used in humans and animals, including 2,855 small molecules and 190 traditional herbal medicines, and identified 15 active small molecules in concentrations ranging from 0.1 nM to 50 μM. Two enzymatic assays, along with molecular modeling, were then developed to confirm those targeting the virus 3CL protease and the RNA-dependent RNA polymerase. Several water extracts of herbal medicines were active in the cell-based assay and could be further developed as plant-derived anti–SARS-CoV-2 agents. Some of the active compounds identified in the screen were further tested in vivo, and it was found that mefloquine, nelfinavir, and extracts of Ganoderma lucidum (RF3), Perilla frutescens, and Mentha haplocalyx were effective in a challenge study using hamsters as disease model.

Background: RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and laborintensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities.