Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Multi-Wall Carbon Nanotube Paste Electrode for Adsorptive Stripping Determination of Quercetin: A Comparison with Graphite Paste Electrode via Voltammetry and Chronopotentiometry

Abstract: Adsorptive-stripping voltammetry and chronopotentiometry were used to study the adsorption and oxidation of quercetin at both graphite-nujol paste electrode (GPE) and carbon nanotubes-nujol paste electrode (CNTPE) for the potential application of carbon nanotube to flavonoids determination. As compared with GPE, CNTPE showed very great power to adsorb quercetin and resulted in a considerable signals enhancement. The adsorption isotherm of quercetin on CNTPE was of Langmuir type, and the stripping of quercetin adsorbed on CNTs showed a quasi-reversible oxidation reaction involving two-electron and two-proton. The high adsorbtive activity of CNTPE was contributed to the high specific surface area and the special surface characteristics of carbon nanotubes. The peak current response of differential pulse voltammetry depended linearly on quercetin concentration. A linear equation Ip(μA)=0.987c(μmol L−1)+0.023 with a correlation coefficient of 0.994 was obtained over the concentration range 0.1–1.0 μmol L−1.

Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm

Abstract: For thermal error compensation technology of CNC machine tools, the collinearity between temperature sensitive points is the main factor for determining the predicted robustness of thermal error model. The temperature sensitive points are input variables of the thermal error model. This paper studies the thermal error of Leaderway V-450 type CNC machine tools during different seasons. It is found that although the commonly used temperature sensitive point selection methods can significantly reduce the collinearity between temperature sensitive points, the correlation between some of the selected temperature-sensitive points and thermal error is weak. This causes the temperature-sensitive points to be variable and the predicted accuracy and robustness of thermal error to be reduced. Therefore, in this paper, the temperature sensitive points are selected directly by their correlation with thermal error to eliminate variability. However, the experimental results also show that the collinearity between temperature sensitive points is very large. Hence, the ridge regression algorithm is used to establish a thermal error model to inhibit the bad influence of collinearity on the thermal error predicted robustness. Thus, the “robustness ridge regression machine tool thermal error modeling method” is proposed, the “RRR method” for short. In addition, in the “RRR method”, the correlation coefficient is used to measure the correlation between temperature sensitive points and thermal error instead of the commonly used gray correlation; because this paper finds that the gray correlation algorithm is essentially inapplicable for measuring a negative correlation. Based on the thermal error experiment data for the whole year, the “RRR method” is compared with two currently used methods, and the results show that the “RRR method” can significantly enhance the long-term predicted accuracy and robustness of thermal error. Finally, the application effect of practical compensation shows that the “RRR method” is usable and effective.