ABSTRACT
The storage period of tea is a major factor affecting tea quality. However, the effect of storage years on the non-volatile major functional components and quality of green tea remains largely unknown. In this study, a comparative analysis of organic green teas with varying storage years (1-16 years) was conducted by quantifying 47 functional components, using electronic tongue and chromatic aberration technology, alongside an evaluation of antioxidative capacity. The results indicated a significant negative correlation between the storage years and levels of tea polyphenols, total amino acids, soluble sugars, two phenolic acids, four flavonols, three tea pigments, umami amino acids, and sweet amino acids. The multivariate statistical analysis revealed that 10 functional components were identified as effective in distinguishing organic green teas with different storage years. Electronic tongue technology categorized organic green teas with different storage years into three classes. The backpropagation neural network (BPNN) analysis demonstrated that the classification predictive ability of the model based on the electronic tongue was superior to the one based on color difference values and 10 functional components. The combined analysis of antioxidative activity and functional components suggested that organic green teas with shorter storage periods exhibited stronger abilities to suppress superoxide anion radicals and hydroxyl radicals and reduce iron ions due to the higher content of eight components. Long-term-stored organic green teas, with a higher content of substances like L-serine and theabrownins, demonstrated stronger antioxidative capabilities in clearing both lipid-soluble and water-soluble free radicals. Therefore, this study provided a theoretical basis for the quality assessment of green tea and prediction of green tea storage periods.
ABSTRACT
Developing a high-performance piezocatalyst that directly transforms mechanical energy into hydrogen is highly desirable in the field of new energy. Herein, the Aurivillius-layered Bi2WO6 (BWO) nanoplates are prepared through a hydrothermal reaction at a moderate temperature of 160 °C, and exhibit strong piezoelectric properties, enabling them to catalyze water splitting through ultrasonic-induced piezocatalysis effect. The hydrogen evolution reaction (HER) and H2O2 generation efficiencies are measured to be 0.43 and 0.36 mmol g-1 h-1, respectively. To further boost piezocatalytic performance, cobalt oxide nanoparticles are intentionally photo-deposited onto these nanoplates as cocatalyst. This configuration results in a significantly boosted HER performance with an efficiency of 3.59 mmol g-1 h-1, which is 2.8 times higher than that of pristine nanoplates and demonstrates strong competitiveness compared to other reported piezocatalysts. The cobalt oxide cocatalyst plays a crucial role in facilitating efficient charge separation and migration, increasing the charge concentration, and ultimately enhancing piezocatalytic HER activity. Overall, this work highlights the potential of Aurivillius-layered bismuth oxide compounds as efficient piezocatalysts and provides valuable insights for designing high-performance piezocatalysts in the field of new energy.
ABSTRACT
Developing piezocatalysts with excellent piezocatalytic hydrogen evolution reaction (HER) performance is highly desired but also challenging. Here, facet engineering and cocatalyst engineering are employed to synergistically improve the piezocatalytic HER efficiency of BiVO4 (BVO). Monoclinic BVO catalysts with distinct exposed facets are synthesized by adjusting pH of hydrothermal reaction. The BVO with highly exposed {110} facet exhibits a superior piezocatalytic HER performance (617.9 µmol g-1h-1) compared with that with {010} facet, owing to the strong piezoelectric property, high charge transfer efficiency, and excellent hydrogen adsorption/desorption capacity. The HER efficiency is enhanced by 44.7% by selectively depositing cocatalyst of Ag nanoparticles specifically on the reductive {010} facet of BVO, where the Ag-BVO interface provides the directional electron transport for high-efficiency charge separation. Under the collaboration between cocatalyst of CoOx on {110} facet and the hole sacrificial agent of methanol, the piezocatalytic HER efficiency is evidently enhanced by 2 times because CoOx and methanol can impede the water oxidation and improve the charge separation. This easy and simple strategy provides an alternative perspective on designing high-performance piezocatalysts.
ABSTRACT
With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs to be solved urgently. Especially in the face of large-scale real-world data, which contains a large number of complex distributions of noises and outliers, the research on robust large-scale real-world data clustering algorithms has become one of the hottest topics. In response to this issue, a robust large-scale clustering algorithm based on correntropy (RLSCC) is proposed in this paper, specifically, k-means is firstly applied to generated pseudo-labels which reduce input data scale of subsequent spectral clustering, then anchor graphs instead of full sample graphs are introduced into spectral clustering to obtain final clustering results based on pseudo-labels which further improve the efficiency. Therefore, RLSCC inherits the advantages of the effectiveness of k-means and spectral clustering while greatly reducing the computational complexity. Furthermore, correntropy is developed to suppress the influence of noises and outlier the real-world data on the robustness of clustering. Finally, extensive experiments were carried out on real-world datasets and noise datasets and the results show that compared with other state-of-the-art algorithms, RLSCC can improve efficiency and robustness greatly while maintaining comparable or even higher clustering effectiveness.
