Search

Abstract A technique for the transfer-free growth of 2-inch wafer-scale patterned graphene directly on GaN LED epilayers is introduced. High-quality graphene as transparent electrodes and heat spreaders is synthesized directly on GaN by PECVD at only 600 °C deposition temperature and within 3 min growth time. Co acts as both the catalyst for graphene growth and the dry etching mask for GaN mesas, which greatly improves the efficiency of the semiconductor device process. Elegantly, the graphene growth is in accordance with the shape of Co, which offers a lithography-free patterning technique of the graphene. Afterward, using our penetration etching method through the PMMA and graphene layers, the Co is peacefully removed, and in-situ Ohmic contact is achieved between the graphene and p-GaN where the contact resistivity is only 0.421 Ω cm2. The graphene sheet resistance is as low as 631.2 Ω sq−1. The device is also superior to the counterpart graphene-free LED in terms of heat spreading behavior, as evidenced by the lower junction temperature and thermal resistance. Most importantly, the developed technique produces graphene with excellent performance and is intrinsically more scalable, controllable, and semiconductor industry compatible than traditionally transferred graphene.

Micro-light-emitting diodes (μLEDs), with their advantages of high response speed, long lifespan, high brightness, and reliability, are widely regarded as the core of next-generation display technology. However, due to issues such as high manufacturing costs and low external quantum efficiency (EQE), μLEDs have not yet been truly commercialized. Additionally, the color conversion efficiency (CCE) of quantum dot (QD)-μLEDs is also a major obstacle to its practical application in the display industry. In this review, we systematically summarize the recent applications of nanomaterials and nanostructures in μLEDs and discuss the practical effects of these methods on enhancing the luminous efficiency of μLEDs and the color conversion efficiency of QD-μLEDs. Finally, the challenges and future prospects for the commercialization of μLEDs are proposed.

AbstractBackground/Aims Acute pancreatitis is a common condition of the digestive system, but sometimes it develops into severe cases. In about 10–20% of patients, necrosis of the pancreas or its periphery occurs. Although most have aseptic necrosis, 30% of cases will develop infectious necrotizing pancreatitis. Infected necrotizing pancreatitis (INP) requires a critical treatment approach. Minimally invasive surgical approach (MIS) and endoscopy are the management methods. This meta-analysis compares the outcomes of MIS and endoscopic treatments.Methods We searched a medical database until December 2022 to compare the results of endoscopic and MIS procedures for INP. We selected eligible randomized controlled trials (RCTs) that reported treatment complications for the meta-analysis.Results Five RCTs comparing a total of 284 patients were included in the meta-analysis. Among them, 139 patients underwent MIS, while 145 underwent endoscopic procedures. The results showed significant differences (p 0.05) in the RR for death, bleeding, incisional hernia, percutaneous drainage, pancreatic endocrine deficiency, pancreatic exocrine deficiency, or the need for enzyme use.Conclusions Endoscopic management of INP performs better compared to surgical treatment due to its lower complication rate and higher patient life quality.

With the rapid development of graphene technology, today graphene performs well in the application of light-emitting diode (LED) transparent electrodes. Naturally, high-quality contact between the graphene and the GaN underneath is very important. This paper reports a process for nickel-assisted transfer-free technology of graphene chemical vapor deposition on GaN. The nickel film plays the dual role of etching mask and growth catalyst, and is removed by the subsequent “penetration etching” process, achieving good direct contact between the graphene and GaN. The results show that the graphene effectively improves the current spreading of GaN-based LEDs and enhances their electrical performance. This scheme avoids the wrinkles and cracks of graphene from the transfer process, and is not only suitable for the combination of graphene and GaN-based LEDs, but also provides a solution for the integration of graphene and other materials.

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder−decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.