Dataset Information

Smart Nanocatalysts with Streamline Shapes.

ABSTRACT: Particulate catalysts with streamline shapes have important impacts on fluid-related reactions, and they need to be properly characterized. However, utilization of streamline-shaped catalysts for heterogeneous catalysis has remained an unexplored area due to the lack of easy-to-use techniques to produce such shaped catalysts, especially at the small length scale of the submicron to micron regime. Herein, we report our recent development of a class of prototype nanocatalysts with streamline shapes. In this research, the kinetic control is adapted to obtain streamline-shaped supports, followed by functionalizing such supports with catalytically active metal nanoclusters (e.g., Au, Pd, Pt, and Ag or their combinations) in a stepwise manner. Advantages related to the streamline morphology of catalysts have been demonstrated with a number of solid-solution systems such as alcohol oxidation, olefin hydrogenation, and Suzuki-Miyaura coupling. We believe these findings will promote new research on the design and synthesis of functional materials with additional fluid-advanced features.

SUBMITTER: Zhan G

PROVIDER: S-EPMC5532712 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Smart Nanocatalysts with Streamline Shapes.

Zhan Guowu G Zeng Hua Chun HC

ACS central science 20170711 7

Particulate catalysts with streamline shapes have important impacts on fluid-related reactions, and they need to be properly characterized. However, utilization of streamline-shaped catalysts for heterogeneous catalysis has remained an unexplored area due to the lack of easy-to-use techniques to produce such shaped catalysts, especially at the small length scale of the submicron to micron regime. Herein, we report our recent development of a class of prototype nanocatalysts with streamline shape ...[more]

PMID: 28776022

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Project description:Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

Project description:BackgroundWhile graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes.PurposeThe purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability.Study typeRetrospective analysis of previously prospective study.PopulationTen healthy subjects, 70% female, aged 25.3 ± 5.9 years.Field strength/sequenceA 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ).AssessmentA total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived.Statistical testsPaired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility.ResultsModularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided.Data conclusionModularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count.Evidence level1.Technical efficacyStage 1.

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Smart Nanocatalysts with Streamline Shapes.

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Smart Nanocatalysts with Streamline Shapes.

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