Metal single-atom catalysts (MSACs) have attracted considerable attention in the field of electrocatalysis due to their maximized atomic utilization, high activity, and superior selectivity. As a class of supported catalyst, the type of support material plays a key role in stabilizing metal single atoms (MSAs) and improving the overall catalytic performance. One-dimensional (1D) nanomaterials are regarded as ideal supports for MSACs owing to many of their unique advantages, such as controllable surface physicochemical properties, large specific surface area, efficient electron transfer pathway, and great flexibility in element selection. Therefore, recently developed MSACs supported by various types of 1D nanostructured substrates have shown fascinating electrocatalytic performance towards a wide range of electrochemical reactions and demonstrated great potential in practical applications. In this review, we summarize recent progress of 1D nanomaterial supported MSACs, from material synthesis, characterization, and theoretical calculation to their performance in five different kinds of electrochemical applications. In particular, the major synthetic strategies of these advanced MSACs and their catalytic performance and mechanisms in various electrocatalytic reactions are extensively discussed. Finally, the remaining challenges and future prospects of 1D nanomaterial supported MSACs are provided.
As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.
Based on the multifilament model with cross air blowing proposed by Dutta (1987) and the assumption that the quench air temperature around the filament obeys an exponential distribution, a multifilament model suitable for the annular air blowing condition of PET staple fiber melt spinning is proposed. The quench air velocity, quench air temperature, filament velocity, filament temperature, etc. at different positions were predicted and the relation between birefringence and the important quality index of as-spun fiber, Eys1.5 (elongation corresponding to 1.5 times the yielding stress in a stress-strain curve) was obtained through experiment. The asspun fiber properties of PET staple and its variability were predicted and the effects of spinning conditions and spinneret design on as-spun fiber properties were discussed and verified.
In digital printing, the fabric print quality can only be evaluated through the color and pattern of printed fabric after printing. The influence of the physical properties of fabric on the printing effect remains unclear. In this paper, the digital print suitability of cotton fabric was defined, and 14 samples of plain cotton fabrics were taken as research objects. In addition, 11 evaluation indexes of cotton fabrics were selected. The evaluation and prediction model of cotton fabric printability was established by principal component analysis. Furthermore, the prediction results of the model were verified by analyzing the colorimetric data of cotton fabric after printing. The result demonstrated that the evaluation model of printing suitability was applicable and can be used to quickly evaluate the printability of cotton fabrics. Particularly, fabric thickness had the greatest influence on printing permeability. In the case of moderate wicking, the higher the whiteness of cotton fabric, the better the color reproducibility after printing. Additionally, the wicking effect also had a great influence on color performance.
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