Graphene-Enhanced UV-C LEDs.

Light-emitting diodes in the UV-C spectral range (UV-C LEDs) can potentially replace bulky and toxic mercury lamps in a wide range of applications including sterilization and water purification. Several obstacles still limit the efficiencies of UV-C LEDs. Devices in flip-chip geometry suffer from a huge difference in the work functions between the p-AlGaN and high-reflective Al mirrors, whereas the absence of UV-C transparent current spreading layers limits the development of UV-C LEDs in standard geometry. Here it is demonstrated that transfer-free graphene implemented directly onto the p-AlGaN top layer by a plasma enhanced chemical vapor deposition approach enables highly efficient 275 nm UV-C LEDs in both, flip-chip and standard geometry. In flip-chip geometry, the graphene acts as a contact interlayer between the Al-mirror and the p-AlGaN enabling an external quantum efficiency (EQE) of 9.5% and a wall-plug efficiency (WPE) of 5.5% at 8 V. Graphene combined with a ≈1 nm NiOx support layer allows a turn-on voltage <5 V. In standard geometry graphene acts as a current spreading layer on a length scale up to 1 mm. These top-emitting devices exhibit a EQE of 2.1% at 8.7 V and a WPE of 1.1%.

Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks.

The early diagnosis of sepsis reduces the risk of the patient's death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs.

Quantifying the hydrodynamic stress for bioprocesses.

Hydrodynamic stress is an influential physical parameter for various bioprocesses, affecting the performance and viability of the living organisms. However, different approaches are in use in various computational and experimental studies to calculate this parameter (including its normal and shear subcomponents) from velocity fields without a consensus on which one is the most representative of its effect on living cells. In this letter, we investigate these different methods with clear definitions and provide our suggested approach which relies on the principal stress values providing a maximal distinction between the shear and normal components. Furthermore, a numerical comparison is presented using the computational fluid dynamics simulation of a stirred and sparged bioreactor. It is demonstrated that for this specific bioreactor, some of these methods exhibit quite similar patterns throughout the bioreactor-therefore can be considered equivalent-whereas some of them differ significantly.

Effect of 38% silver diamine fluoride on fracture resistance of leucite reinforced feldspathic ceramic CAD/CAM class I inlay restorations.

INTRODUCTION: Silver diamine fluoride (SDF) is a topical agent that has recently gained popularity for its ability to stop and prevent dental caries.

Direct growth of graphene on Ge(100) and Ge(110) via thermal and plasma enhanced CVD.

The integration of graphene into CMOS compatible Ge technology is in particular attractive for optoelectronic devices in the infrared spectral range. Since graphene transfer from metal substrates has detrimental effects on the electrical properties of the graphene film and moreover, leads to severe contamination issues, direct growth of graphene on Ge is highly desirable. In this work, we present recipes for a direct growth of graphene on Ge via thermal chemical vapor deposition (TCVD) and plasma-enhanced chemical vapor deposition (PECVD). We demonstrate that the growth temperature can be reduced by about 200 °C in PECVD with respect to TCVD, where usually growth occurs close to the melting point of Ge. For both, TCVD and PECVD, hexagonal and elongated morphology is observed on Ge(100) and Ge(110), respectively, indicating the dominant role of substrate orientation on the shape of graphene grains. Interestingly, Raman data indicate a compressive strain of ca. - 0.4% of the graphene film fabricated by TCVD, whereas a tensile strain of up to + 1.2% is determined for graphene synthesized via PECVD, regardless the substrate orientation. Supported by Kelvin probe force measurements, we suggest a mechanism that is responsible for graphene formation on Ge and the resulting strain in TCVD and PECVD.