Sintering Mechanism of silicon from K

In order to clarify the reaction process of Si phase, Differential Scanning Calorimeter (DSC), as the main measurement, was used to prepare Si samples and monitor the sintering reaction process. Combined with X-ray diffraction analysis, the formation process of Si phase was summarized. The reaction between K2SiF6 and Al powders occurred at 580°C, but not completed until 660°C. The whole formation process of Si includes two different stages: One is the solid–solid reaction stage, the other is the solid–liquid reaction stage

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction

miR-92 regulates the proliferation, migration, invasion and apoptosis of glioma cells by targeting neogenin

This study aimed to explore the pathological mechanism in regulating glioma progression. The expression of miR-92 and neogenin was evaluated by qRT-PCR and western blot. Cell viability and apoptosis were measured by MTT and flow cytometry assays, respectively. The migration and invasion abilities were examined by transwell assays. The interaction between miR-92 and neogenin was conducted by dual-luciferase reporter system. As a result, we found that the expression of miR-92 was up-regulated in glioma tissues and cell lines. Down-regulation of miR-92 inhibited glioma cell proliferation, migration, invasion and promoted cell apoptosis rate of U251 and U87 cells. Notably, miR-92 was identified to directly target to 3’-UTR of neogenin. Furthermore, neogenin was down-regulated in glioma tissues and cells in a miR-92-correlated manner. Overexpression of neigenin could cause similar results to miR-92 knockdown in U251 and U87 cells. However, the silencing of neogenin partially reversed the effects of miR-92 knockdown on cell proliferation, migration, invasion and apoptosis of glioma cells in vitro. In conclusion, we clarified that miR-92 knockdown could suppress the malignant progression of glioma cells in vitro by targeting neogenin. Therefore, miR-92 could serve as a potential diagnostic and prognostic marker in glioma patient

Revealing the Early Forming Behaviors of a Carbon-Fiber-Reinforced Aluminum Foam through Synchrotron X-ray

The effect of copper-coated carbon fiber (Cf) on the foaming behavior of aluminum foam prepared by the powder metallurgy (PM) method, was studied, by using the synchrotron radiation technique. The corresponding stabilizing mechanism of the Cf was discussed and analyzed, by a comparison of the dynamic foaming process of the samples, prepared using pure Al, and that with an additional Cf, under the same heating regime. It was found that the Cf, acting as an “artificial defect” in the matrix, effectively guided the cell’s nucleation process. It not only improved the dispersion of the cell nucleation—which led to a more dispersed distribution of internal stress in the early nucleation stage—but also effectively eliminated the influence of the internal differences caused by the preparation method, which led to a more uniform distribution of cells, during the nucleation and growth stage. Thus, the cell evolution stability was greatly improved when the matrix was still in the solid phase

A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed

Significant Improvement of Mechanical Properties of SiC-Nanowire-Reinforced SiCf/SiC Composites via Atomic Deposition of Ni Catalysts

This study aimed to study the effects of different catalyst introduction methods on the distribution of SiC nanowires (SiCNWs) and the mechanical properties of SiCf/SiC composites. Two different catalyst-introduction methods (electroplating (EP) vs. atomic deposition (AD)) have been used to catalyze the growth of SiC nanowires in SiCf preforms. The morphology, structure and phase composition were systematically investigated using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). The SiCNWs-reinforced SiCf/SiC composited was densified by CVI. The compressive strength of the SiCNWs-reinforced SiCf/SiC composites was evaluated by radial crushing test. Compared with EP, atomic Ni catalysts fabricated by AD have higher diffusivity for better diffusion into the SiCf preform. The yield of SiCNWs is effectively increased in the internal pores of the SiCf preform, and a denser network forms. Therefore, the mechanical properties of SiCNW-containing SiCf/SiC composites are significantly improved. Compared with the EP-composites and SiCf/SiC composites, the compressive strength of AD-composites is increased by 51.1% and 56.0%, respectively. The results demonstrate that the use of AD method to grow SiCNWs is promising for enhancing the mechanical properties of SiCf/SiC composites

Enhancing the Photocatalytic Hydrogen Evolution Activity of Mixed-Halide Perovskite CH3NH3PbBr3-xIx Achieved by Bandgap Funneling of Charge Carriers

Powder samples of mixed halide perovskite MAPbBr(3-x)I(x) (MA = methylammonium ion, CH3NH3+) were prepared by employing a facile light-assisted halide-exchange method in aqueous halide solution at room temperature. It is found that the distribution of iodide ions in the MAPbBr(3-x)I(x) particles tends to be largest on the surface and becomes lower on going into the interior so that they have a correct bandgap funnel structure that is needed for transferring photogenerated charge carriers from the interior to the surface. Consequently, the MAPbBr(3-x)I(x)/Pt powder sample (250 mg) exhibits an enhanced photocatalytic activity for a H-2 evolution under visible light (100 mW cm(-2), lambda > 420 nm) with the rate of 651.2 mu mol h(-1) and a solar-to-chemical conversion efficiency of 1.05%

Effect of the intra- and inter-triazine N-vacancies on the photocatalytic hydrogen evolution of graphitic carbon nitride

We developed a new method to introduce N-vacancies of graphitic carbon nitride (GCN, typically in the Melon structure) at the inter-triazine sites and investigated how the visible-light photocatalytic H2 evolution of GCN is affected by the N-vacancies at the intra- and inter-triazine sites of GCN. Theoretical and experimental results show that these N-vacancies of GCN create singly-occupied defect states within the band gap acting as a trap for photogenerated electrons and act as the reaction sites for H+ reduction. Compared with the intra-triazine N-vacancy, the inter-triazine N-vacancy exhibits stronger electron localization leading to a more efficient H2 evolution. The photocatalytic reaction rate of GCN with inter-triazine N-vacancies is 9 times higher than that of “defect free” GCN, and 2.2 times higher normalized reaction rates than GCN with intra-triazine N-vacancies. The catalysis mechanism and the method to prepare melon with inter-triazine N-vacancies can be extended to explore new photocatalysts with high activities.</p