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  • 2020-2024
  • 1990-1994  (4)
  • CO hydrogenation  (2)
  • Multivariate kurtosis  (2)
  • 1
    ISSN: 1572-879X
    Keywords: Faujasite X hosted rhodium ; rhodium particle size ; IR-spectroscopy ; TEM ; CO hydrogenation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Different metal dispersions of Na-faujasite X hosted rhodium were generated and characterized by transmission electron microscopy (TEM) and IR-spectroscopy. CO hydrogenation has been studied over these Rh/NaX-zeolite catalysts. The finer rhodium dispersion with a mean particle size of 1–2 nm shows a higher selectivity towards oxygenates (methanol, ethanol and dimethyl ether) as compared to the coarser dispersion (4–5 nm), where nearly exclusively methane and higher hydrocarbons are formed.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1572-879X
    Keywords: faujasite-hosted rhodium ; in situ FTIR spectroscopy ; CO hydrogenation ; rhodium dispersion ; methanol selectivity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract In situ FTIR spectroscopy has been applied to study the reason of the different selectivity behaviour of rhodium in the CO hydrogenation reaction in dependence on the different particle size. Several forms of molecularly adsorbed CO are observed, such as linearly- and bridgedbonded CO on metallic rhodium as well as dicarbonyl species with Rh(I) centres formed by oxidation of Rh(0) with protons in presence of CO. Furthermore, non-reactive formate, acetate, and carbonate species are produced as side products of the reaction. The higher selectivity to oxygenates, particularly to methanol, is explained by the existence of multiple-bonded CO present only on small rhodium crystallites. The multiple-bonded CO formed at low temperatures is converted at higher temperatures to the highly reactive formyl species, which is hydrogenated to methanol.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 5 (1991), S. 241-248 
    ISSN: 0886-9383
    Keywords: Multivariate kurtosis ; Generalized distance ; Multivariate outliers ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate outliers in environmental data sets are often caused by atypical measurement error in a single variable. From a quality assurance perspective it is important to identify these variables efficiently so that corrective actions may be performed. We demonstrate a procedure for using two multivariate tests to identify which variable ‘caused’ each outlier. The procedure is tested with simulated data sets have have the same correlation structure as selected water chemistry variables from a survey of lakes in the Western United States. The success rates are evaluated for three of the variables for sample sizes of 50 and 100, significance levels of 0.01 and 0.05 and various amounts of mean shift. The procedure works best for highly correlated variables.
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 7 (1993), S. 165-176 
    ISSN: 0886-9383
    Keywords: Measurement error ; Outliers ; Environmental ; Quality control ; Multivariate kurtosis ; Generalized distance ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Environmental data are usually multivariate, with the variables conforming to some correlation structure. Occasionally, measurements which do not conform in structure or magnitude may occur in one or more variables. It is important (1) to characterize these discordancies in terms of the disturbed variables and the direction and magnitude of the anomalous error and (2) to associate each discordant observation with a specific cause of measurement error in order to prevent further mismeasurement. We describe a procedure for identifying suspected causes of discordant observations in otherwise multinormal data sets. Variables are assigned to groups, each of which is associated with a specific cause of measurement error. Discordant observations are identified with the generalized distance test or the multivariate kurtosis test. Suspected causes of measurement error are identified by repeating the tests with one of the groups of variables omitted in each analysis. The procedures are evaluated with simulated data sets having a correlation structure similar to that of a large environmental data set.
    Additional Material: 4 Ill.
    Type of Medium: Electronic Resource
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