Jun Ma

Abstract Electrocatalytic alkynes semi-hydrogenation to produce alkenes with high yield and Faradaic efficiency remains technically challenging because of kinetically favorable hydrogen evolution reaction and over-hydrogenation. Here, we propose a hierarchically nanoporous Cu 50 Au 50 alloy to improve electrocatalytic performance toward semi-hydrogenation of alkynes. Using Operando X-ray absorption spectroscopy and density functional theory calculations, we find that Au modulate the electronic structure of Cu, which could intrinsically inhibit the combination of H* to form H 2 and weaken alkene adsorption, thus promoting alkyne semi-hydrogenation and hampering alkene over-hydrogenation. Finite element method simulations and experimental results unveil that hierarchically nanoporous catalysts induce a local microenvironment with abundant K + cations by enhancing the electric field within the nanopore, accelerating water electrolysis to form more H*, thereby promoting the conversion of alkynes. As a result, the nanoporous Cu 50 Au 50 electrocatalyst achieves highly efficient electrocatalytic semi-hydrogenation of alkynes with 94% conversion, 100% selectivity, and a 92% Faradaic efficiency over wide potential window. This work provides a general guidance of the rational design for high-performance electrocatalytic transfer semi-hydrogenation catalysts.

BACKGROUND: Since chronic hyperglycemia-related damage to small blood vessels results in complications, patients with longer durations of type 2 diabetes mellitus (T2DM) are more likely to develop microvascular complications, such as retinopathy, neuropathy and nephropathy, which are very harmful to the health of humans. Therefore, this study aimed to assess the prevalence of diabetes-related microvascular complications and to explore their risk factors in patients with T2DM in Tianjin, China. METHODS: This observational, cross-sectional study was conducted at 8 hospitals in urban and suburban regions of Tianjin, China. The information collected from the subjects mainly included demographic characteristics, anthropometric measurements and clinical information. Univariate and multivariate logistic regression was used to identify the possible risk factors for microvascular complications (retinopathy, neuropathy and nephropathy). RESULTS: A total of 4,490 patients with T2DM from 8 hospitals in Tianjin, China were selected from November 2015 to January 2016. Of the study subjects, 2,270 (50.6%) were males. The median age was 64.0±13.0 years. The percentage of patients with T2DM who had at least one microvascular complication was 34.5%. The prevalence rates of neuropathy, retinopathy, and nephropathy were 23.5%, 17.4%, and 10.8%, respectively. The results of the multivariate logistic regression showed that the duration of diabetes, insulin use, and the presence of hypertension and dyslipidemia were the main risk factors for developing microvascular complications of T2DM. CONCLUSIONS: The incidence of diabetes complications in Tianjin is high. Increasing the control of risk factors can reduce the occurrence of complications to reduce the disease burden and improve the quality of life of patients.

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.