We
synthesized a series of Ru@IL/AC catalysts using the incipient wetness
impregnation technique associated with five kinds of ionic liquids,
aiming to explore an efficient nonmercuric catalyst for the acetylene
hydrochlorination reaction. Over the optimal 1%Ru@15%TPPB/AC catalyst,
the acetylene conversion was maintained at 99.7% at 48 h (T = 170 °C, GHSVC2H2
= 360 h–1, and V
HCl/V
C2H2
= 1.15).
Additionally, with lower Ru loading (0.2%Ru@15%TPPB/AC), the acetylene
conversion still remained
at 99.3% within 400 h. Characterized by CO pulse chemisorption, TEM,
XPS, TGA, among other methods, it is indicated that TPPB IL could
effectively improve the dispersion of Ru species, suppress the reduction
of active Ru species, and inhibit the coke deposition during the acetylene
hydrochlorination reaction. The interactive mechanism between TPPB
and the reactants and the product was investigated to disclose the
effect of TPPB IL on the catalytic performance of Ru-based catalyst,
in combination with DFT calculations. The enhanced activity and long-term
stability of Ru@IL/AC suggest the promising industrial application
as the nonmercuric catalyst for acetylene hydrochlorination.
BackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.
MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.