Selenium-functionalized carbon as a support for platinum nanoparticles with improved electrochemical properties for the oxygen reduction reaction and CO tolerance

Using selenium-functionalized carbon as supports, platinum nanoparticles were uniformly dispersed on the carbon surface, and showed improved electrochemical properties for the oxygen reduction reaction. At the same time the CO tolerance is improved. The method provides a new route for functionalization of the carbon surface on which to disperse noble metal nanoparticles for use as electrocatalysts in the oxygen reduction reaction.Web of Scienc

Selenium-functionalized carbon as a support for platinum nanoparticles with improved electrochemical properties for the oxygen reduction reaction and CO tolerance

Using selenium-functionalized carbon as supports, platinum nanoparticles were uniformly dispersed on the carbon surface, and showed improved electrochemical properties for the oxygen reduction reaction. At the same time the CO tolerance is improved. The method provides a new route for functionalization of the carbon surface on which to disperse noble metal nanoparticles for use as electrocatalysts in the oxygen reduction reaction.Web of Scienc

The effect of PtRuIr nanoparticle crystallinity in electrocatalytic methanol oxidation

Two structural forms of a ternary alloy PtRuIr/C catalyst, one amorphous and one highly crystalline, were synthesized and compared to determine the effect of their respective structures on their activity and stability as anodic catalysts in methanol oxidation. Characterization techniques included TEM, XRD, and EDX. Electrochemical analysis using a glassy carbon disk electrode for cyclic voltammogram and chronoamperometry were tested in a solution of 0.5 mol L−1 CH3OH and 0.5 mol L−1 H2SO4. Amorphous PtRuIr/C catalyst was found to have a larger electrochemical surface area, while the crystalline PtRuIr/C catalyst had both a higher activity in methanol oxidation and increased CO poisoning rate. Crystallinity of the active alloy nanoparticles has a big impact on both methanol oxidation activity and in the CO poisoning rate

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas

Effect of the structure of Ni nanoparticles on the electrocatalytic activity of Ni@Pd/C for formic acid oxidation

Ni@Pd/C catalysts were synthesized, using Ni/C with different crystalline structures prepared with various ligands. A series of characterizations were performed by transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy. The results indicated the electrocatalysts with amorphous/crystalline (denoted as Nia and Nic) Ni structures decorated with Pd. The formic acid electrocatalytic oxidation results showed that the peak current of Nia@Pd/C was about 1.2 times higher than that of Nic@Pd/C. The good electrochemical performance and stability of Pd-modified amorphous Ni substrate reveals that the core structure plays an important role in the electrocatalytic activity and the change of the structure can improve the activity and stability of electrocatalysts.Web of Scienc

Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China

Objectives To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. Methods This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. Results A total of 484 patients (median age of 47 years, interquartile range 33–57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13–15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. Conclusions Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient’s condition during the course of illness

Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor

Low-level wind shear is a vital weather process affecting aircraft safety while taking off and landing and is known as the “aircraft killer” in the aviation industry. As a result, effective monitoring and warning are required. Several ramps detection algorithms for low-level wind shear based on glide path scanning of lidar have been developed, including double and simple ramp detection, with the ramp length extension and contraction strategies corresponding to the algorithm. However, current algorithms must be improved to determine the maximum shear value and location. In this paper, a new efficient algorithm based on the shear intensity factor value is presented, in which wind speed changes and distance are both considered when calculating wind shear. Simultaneously, the effectiveness of the improved algorithm has been validated through numerical simulation experiments. Results reveal that the improved algorithm can determine the maximum intensity value and wind shear location more accurately than the traditional algorithm. In addition, the new algorithm improved the detection ability of lidar for weak wind shear