Core-shell Cu@SiO_(2)/SiO_(2) catalyst for 1,6-hexanediol dehydrogenation toε-caprolactone:High activity and stability from core-shell nanostructure

The gas-phase dehydrogenation of 1,6-hexanediol(1,6-HDO)toε-caprolactone(ε-CL)over the high-performance Cu-based catalysts is highly desirable,but with grand challenges,because the Cu nanoparticles(NPs)are easy to be sintered with the low Hüttig temperature(<150℃ vs.>250℃ of reaction temperature).Herein,we report a highly efficient silica-encapsulated nano-Cu catalyst(Cu@SiO_(2)/SiO_(2))prepared via a complexation–impregnation method for the dehydrogenation of 1,6-HDO,exhibiting a 1,6-HDO conversion of 95.3%andε-CL selectivity of 80.0%at 270℃.The catalyst also has the outstanding thermal stability(without sintering up to 270℃ for 100 h on stream),which can be attributed to the effective encapsulation of the SiO_(2)shell.In addition,the reaction network of 1,6-HDO dehydrogenation is proved.Finally,the pyridine-diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS)and in-situ X-ray photoelectron spectroscopy(XPS)reveal that the Cu^(0) species favor the conversion of 1,6-HDO toε-CL.The synergistic effect of Cu+and Cu^(0) benefits the conversion ofε-CL to 2-methylcyclopentanone(2-MCPN).This study is beneficial for designing the high-performance Cu-based catalysts for 1,6-HDO toε-CL,understanding the reaction network of 1,6-HDO dehydrogenation over the Cu-based catalysts,and offering a strong foundation for the largescale production ofε-CL.

A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model

This paper proposed a new method for quantitative assessment of visual detectability of damage based on logistic regression,using the Probability of Detection(POD)as a criterion.Experiments were performed to establish the massive hit/miss data of visual inspection.Authoritative investigations verified the reliability of the data.The prediction function concluded comprises more than one flaw size parameters,including the depth and diameter of the dents.The results show that the depth and diameter of the dents are pivotal for the evaluation of detectability;the type of detection,the detection distance,and the qualifications of personnel are critical external factors to be considered.This function,with an accuracy rate of nearly 85%,is capable of predicting the visual detection probability of impact damage under various detection environments,which will provide a reference for the damage tolerance design of composite materials and field maintenance in the NonDestructive Testing(NDT)field.