A Research on Preparation and Application of the Monolithic Catalyst with Interconnecting Pore Structure

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training

Nowadays, China's sports industry has attained effective development, but the athlete's efficiency in the training process is too complex to have a scientific guarantee. Machine learning technology's help in guiding the sports training process has become a hot spot. In this work, we investigate the use of deep learning in real-time analysis of basketball sports data, utilizing research approaches such as scientific reporting, audio/video analysis, experimental research, and mathematical statistics. The suggested basketball stance action recognition and analysis system are made up of two pieces that are sequentially connected. The bottom-up stance estimate approach is utilized to locate the joint locations in the first segment, which is then used to extract the target's posture sequence from the video. The analyses are needed for a Support Vector Machine (SVM) algorithm based on the deep learning method of the space-time graph. The basketball activity of the set classification is recognized and extracted from the segmented stance sequence. The study used an auxiliary method, which is contrasted to standard training, in order to get higher accuracy and also correct player errors in a timely manner. The approach can help players rectify technical errors, develop muscle memory, and increase their abilities. The results revealed that the algorithm generated 97.7% accuracy in evaluating data from basketball training.

Photothermal Effect: The Amygdaloidal Nano-Structure Based on Bi2S3 for the Enhanced Degradation of Rhodamine B Under Irradiation by NIR

In recent years the photothermal effect, an auxiliary strategy for increasing the degradation rate of pollutants under irradiation by near-infrared (NIR), has become a research focus. In this study a novel amygdaloidal nanophotocatalyst, Bi2S3, was synthesized by a traditional approach using a hydrothermal process, in which Bi2S3 nanostructures were spread out like a peacock's tail. The produced Bi2S3 photocatalyst exhibited excellent performance in the rapid degradation of Rhodamine B (RB). This proved that the photothermal effect is mainly responsible for the rapid degradation of RB under NIR laser irradiation. Moreover, it was found that the photothermal effect could not degrade the products with NIR radiation in darkness. However, with the support of visible radiation, the photothermal effect of the Bi2S3 photocatalyst enhanced degradation of RB (degradation rate 90% under 1 h). This novel structure exhibited a potential ability for degrading pollution in industry or agriculture.

Gallium nitride catalyzed the direct hydrogenation of carbon dioxide to dimethyl ether as primary product

The selective hydrogenation of CO₂ to value-added chemicals is attractive but still challenged by the high-performance catalyst. In this work, we report that gallium nitride (GaN) catalyzes the direct hydrogenation of CO₂ to dimethyl ether (DME) with a CO-free selectivity of about 80%. The activity of GaN for the hydrogenation of CO₂ is much higher than that for the hydrogenation of CO although the product distribution is very similar. The steady-state and transient experimental results, spectroscopic studies, and density functional theory calculations rigorously reveal that DME is produced as the primary product via the methyl and formate intermediates, which are formed over different planes of GaN with similar activation energies. This essentially differs from the traditional DME synthesis via the methanol intermediate over a hybrid catalyst. The present work offers a different catalyst capable of the direct hydrogenation of CO₂ to DME and thus enriches the chemistry for CO₂ transformations.

The conversion of CO₂ to valuable chemicals is still challenged by catalyst developments. Herein, the authors found that GaN is an efficient catalyst for selective CO₂ hydrogenation to dimethyl ether as the primary product, in contrast to the traditional methanol-intermediate route over hybrid catalysts.

Flow-through enzymatic reactors using polymer monoliths: From motivation to application

The application of monolithic materials as carriers for enzymes has rapidly expanded to the realization of flow-through analysis and bioconversion processes. This expansion is partly attributed to the absence from diffusion limitation in many monoliths-based enzyme reactors. Particularly, the relatively ease of introducing functional groups renders polymer monoliths attractive as enzyme carriers. After summarizing the motivation to develop enzymatic reactors using polymer monoliths, this review reports the most recent applications of such reactors. Besides, the present review focuses on the crucial characteristics of polymer monoliths affecting the immobilization of enzymes and the processing parameters dictating the performance of the resulting enzymatic reactors. This review is intended to provide a guideline for designing and applying flow-through enzymatic reactors using polymer monoliths.