Combing experimental methods and molecular simulations to study self-healing behaviors of polyurethane elastomers containing multiple hydrogen bond networks and flexible blocks.

The preparation of polymers with high self-healing ability is conducive to environmental protection and resource conservation. In the present work, two kinds of polyurethane (PU) elastomers were prepared: the one containing flexible end blocks (polypropylene glycol) and the other containing flexible end blocks and 2-ureido-4[1H]-pyrimidinone (UPy) groups that can form reversible quadruple hydrogen bonds. Both of the two PU elastomers have self-healing ability. At low temperatures the PU without UPy groups exhibits stronger self-healing ability, while at high temperatures the PU with UPy groups has better self-healing function. The difference can be attributed to the combined effect of segmental mobility and reversible network strength. Based on molecular simulations, we further observed that the self-healing behaviors are affected by four factors: healing temperature, reversible interaction strength, reversible interaction site density and segment diffusion ability.

Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference.

Tea is an important beverage in humans' daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.

Identification of Similar Chinese Congou Black Teas Using an Electronic Tongue Combined with Pattern Recognition.

It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou black tea. The type of black tea was identified by principal component analysis and discriminant analysis. The latter showed better results. The samples of the two types of black tea distributed on the two sides of the region graph were obtained from discriminant analysis, according to tea type. For grade discrimination, we determined grade prediction models for each tea type by partial least-squares analysis; the coefficients of determination of the prediction models were both above 0.95. Discriminant analysis separated each sample in region graph depending on its grade and displayed a classification accuracy of 98.20% by cross-validation. The back-propagation neural network showed that the grade prediction accuracy for all samples was 95.00%. Discriminant analysis could successfully distinguish tea types and grades. As a complement, the models of the biochemical components of tea and electronic tongue by support vector machine showed good prediction results. Therefore, the electronic tongue is a useful tool for Congou black tea classification.

Influence of different nitrogen sources on carbon and nitrogen metabolism and gene expression in tea plants (Camellia sinensis L.).

Nitrogen plays an important role in plant growth and development, with different nitrogen forms also having an impact on carbon/nitrogen metabolism. Unlike most plants, tea plants prefer ammonium over nitrate. In this paper, we focused on how different nitrogen sources regulate the carbon/nitrogen metabolism in tea plants. Tea seedlings of 'Longjing 43' were cultivated hydroponically in four different solutions (zero-nitrogen, only NH4+, only NO3- and mixed nitrogen (NH4+: NO3- = 1:1). We analyzed characteristic components of tea plants and related genes in carbon and nitrogen metabolism. Tea polyphenols and catechins representing carbon pool, increased when NO3- was supplied as the nitrogen source, and similar findings were recorded in the zero-nitrogen treatment. The expression of most catechins biosynthesis-related genes was up regulated under NO3- and zero-N treatment, that was associated with tea polyphenols and catechins changes. Compared with NO3- as the nitrogen source, NH4+ and mixed nitrogen treatments had a positive effect on the accumulation of amino acids, especially theanine, glutamate and arginine, and these components contribute to the freshness flavor of tea. The expression of ammonium-assimilation genes was also up-regulated with NH4+ supply. Under mixed nitrogen treatment, the ratio of total polyphenols to free amino acids (PP/AA) was between sole NH4+ and NO3- supply. Therefore, compared with single nitrogen source, carbon and nitrogen metabolism of tea plant was more balanced under mixed nitrogen treatment. The results suggested that NO3- as the nitrogen source promoted the biosynthesis of catechins enriching the carbon pool, whereas NH4+ supply was more conducive to nitrogen metabolism, indicating that different nitrogen sources could affect the carbon and nitrogen balance.