ABSTRACT
Li–air batteries have received significant attention for their ultrahigh theoretical energy density. However, the byproducts induced by attacking air hinder the conversion of Li–O2 batteries to Li–air batteries. Humidity is one of the main obstacles, not only causing side reactions with the discharge products but also leading to rapid corrosion of the lithium anode. Here, we fabricated a novel composite hydrophobic catalyst by loading RuO2 and graphene on N-doped porous carbon. The catalyst was endowed with hydrophobicity and showed superior catalytic performance and low affinity to water in the air. A Li–air battery equipped with this novel composite catalyst exhibited eminent cycling performance in pure oxygen (over 470 h), humid oxygen [∼40% relative humidity (RH), over 310 h], and ambient air (∼42% RH, over 330 h) at a current density of 500 mA g−1, and the discharge specific capacity increased from 13122.1 to 19358.6 mAh g−1. © 2022
ABSTRACT
Against the Covid-19 background, vaccine safety has aroused the wild attention of all social areas. However, the factors that cause vaccine safety risks are complicated and meanwhile, data is difficult to obtain, making it a challenge for analyzing vaccine safety risks quantitatively. This paper concretises the issue of vaccine system safety by creatively proposing an analytical framework for the problem of uncertainty. First, the paper focuses on the whole process of vaccine safety, analyses risk factors affecting vaccine safety in development, approval, production, transportation, and supervision of vaccines in order to build a vaccine risk assessment system. The proposed framework is then used to construct a Bayesian network early warning system for vaccine risk. To address the difficulty of obtaining data, the probability of safety risks occurring throughout the process is calculated by combining expert knowledge and fuzzy set theory to obtain uncertainty data. In response to structural complexity, a comprehensive framework is constructed using fault trees and Bayesian networks to capture the correlation between risk factors. This analytical framework can provide guidance to governments and vaccine-related companies in their decision-making to prevent vaccine safety issues. Finally, sensitivity analysis revealed a high probability of vaccine risk in the transport process. © 2021 IEEE.
ABSTRACT
Objective: To compare the consistency and detection capability of seven 2019-nCoV nucleic acid detection kits, and provide reference for detection method selection of clinical laboratory and diagnosis of new coronavirus pneumonia. Methods: Two batches of pharyngeal swab samples were collected from tenpatients with confirmed infection of 2019-nCoV and 10 suspected patients with negative 2019-nCoV test results during January 29 to February 5, 2020 in Shenzhen Luohu People's Hospital. Seven kinds of kits were labeled as ato g and used for nucleic acid detection respectively to evaluate the consistency of the test results of the clinical samples. A 2019-nCoV positive specimen was selected and diluted to 5-concentration gradient plates (Level-1 to 5) with RNase-free water. The positive detection rate and intra-batch repeatability of different brands of kits were compared. Results: The negative and positive coincidence rates of twenty clinical samples tested by six kinds of kits were 100%, and the positive and negative coincidence rate was 8/10 and 10/10 for the other kit, respectively. The results of intra-batch repeatability showed the CVs of viral loads tested by these seven kits were all less than 5%. In the concentration range of Level-1 to 3, the detection capability for open reading frame (ORF)1ab gene of Kit b,d and f was lower than Kit a,c,e and g, and the detection capability of kit e and g was the highest (14/15). The detection capability for N gene of Kit a (15/15) was higher than the other 5 kits. The comprehensive analysis of the detection capability for ORF1ab and N gene showedthat Kit d had the lowest detection capability (ORF1ab:40%,N:53%), and there was no significant difference in the detection capability of Kit a, b, c, e, and f. Conclusions: There was no significant difference in the accuracy and repeatability of the seven kits for positive samples with high viral loads, and the detection performance was good;but some kits had poor detection capability for weak positive samples. It is suggested that the weak positive samples should be rechecked by at least two manufacturers' kits to ensure the accuracy of the results.