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We have developed an iron-catalyzed synthesis of pyrrolo[2,1- a ]isoquinoline derivatives with tetrahydroisoquinolines, arylacyl bromides, and nitroolefins. Highly functionalized pyrrolo[2,1- a ]isoquinolines can be obtained in moderate to good yields through a three-component N-alkylation/oxidative 1,3-dipolar cycloaddition/elimination/aromatization cascade. The obtained products in this study can be easily modified by easy chemical transformations to structurally complex molecules bearing privileged framework. [ABSTRACT FROM AUTHOR]

In this work, a large-scale and green method is reported for the facile synthesis of octahedral flower-like CuO nanocrystals via a coordination-deposition route by using Fehling regents. Not any harmful organic chemicals were used during the reaction period. The obtained hierarchical nanostructure can be rationally tailored by varying the concentration of tartrate ions and reaction time. The typical flower-like CuO nanocrystals in the range of 200–250 nm are consisted of numerous small crystalline whiskers, which present a porous surface with a specific surface area of 32.12 m 2 /g and a narrow band gap of 1.5 eV. Importantly, the flower-like CuO nanocrystals show an enhanced photocatalytic activity toward decomposing Rhodamine B (RhB) molecules. The degradation rate is about 87.9% in 40 min under visible light irradiation, which is about 2.5 times for the commercial CuO powers (35.2%). Moreover, the uniform flower-like monolayered CuO film exhibits an excellent photoelectrochemical (PEC) performance with a maximum photocurrent density of 58.8 μA/cm 2 , which is nearly five times higher than the commercial CuO film. This novel synthesis approach provides a large-scale and green protocol for synthesizing hierarchical metal oxide nanocrystals that are useful for photocatalysis, PEC water splitting and photovoltaic device. [ABSTRACT FROM AUTHOR]

A mild nitration of pyrrolo[2,1‐a]isoquinolines has been reached using sodium nitrite as nitro source in HFIP at ambient temperature. A variety of nitro‐bearing pyrrolo[2,1‐a]isoquinoline derivatives has been efficiently prepared in poor to quantitative yields (28–98 %). These products can be easily transformed to primary amines and amide by simple chemical reactions. Treatment of other nitrogen‐containing heterocycles such as pyrrolo[1,2‐a]quinoline and substituted indoles under this developed reaction system afforded nitro‐bearing N‐heterocycles in relatively lower yields (15–19 %). [ABSTRACT FROM AUTHOR]

Analyzing and presenting data from multiple groups are much more informative than that from two groups. However, common tools such as S plot and volcano plot are only available for identifying the significant features between two groups and are restricted to multiple-group comparisons. This study proposed novel visualization plots which not only overcame the restrictions of the above methods but also utilized the p values of multiple tests as the x-axis. The novel visualization plots included a parametric method and a nonparametric method. The parametric method was a combination of an analysis of variance and Welch's analysis of variance; the nonparametric method used the Kruskal-Wallis test. During the selection of significant features, machine learning algorithms were used to determine the cutting points of the x-axis. As a proof of concept, the real data from the experiments of 4-MeO-α-PVP metabolites and fish spoilage metabolomics were illustrated via our visualization method. The results showed that the novel visualization plots were much efficiently presented to identify significant metabolites in multiple-group comparisons. Especially, the positive predicted values of the nonparametric method and the cutting points determined by logistic regression were higher than those of other machine learning algorithms in determining the cutting points for multiple groups. • New visualization plots outweigh volcano plot and S plot for multiple-group study. • Parametric method requires normality of data and Bonferroni's adjustment is suggested to utilize on cut point of x-axis. • Nonparametric method is flexible on data type and machine learning method is suggested to use on cut point of x-axis. • As proof-of-concept, two methods perform well for multiple-group comparisons. [ABSTRACT FROM AUTHOR]