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Hyphenated techniques
(2012)
ESEM-EDX analyses for the characterisation and reproduction of nineteenth-century gold tesserae
(2012)
Environmental scanning electron microscopy with energy dispersive X-ray analysis (ESEM-EDX) is a versatile working tool for the examination of gold tesserae as well as for the selection of suitable materials for their reproduction. In this paper, the ESEM-EDX analyses of original gold tesserae and their damage types are presented. These analytical results form the basis for further calculations of glass properties and experiments. These calculated values enable the determination of the relevant glass properties and allow us to choose the appropriate type of modern glass for further experiments. Subsequent experiments convert this knowledge into practice. By comparing the analytical results from the original glasses and the replicas the accuracy and precision will be shown.
Much has been published over the last years on finding the best (consensus) reference value (RV) for a given set of laboratory data attained in inter- or key comparisons (the latter with a specific view to the CIPM MRA). However, the discussion is ongoing. Except cases where real reference values are available from other sources, the quality of the (consensus value is of major importance in proficiency testing, method Validation, or reference material certification.
A distribution-free, self-consistent RV estimation approach has been developed which is based on the single criterion of participant's compatibility, at the cost of adjusted uncertainties. The final result is a fully compatible data set with minimum Overall variance, and adjusted uncertainties for those values reported which had to be made compatible with the (key comparison) reference value.
Any outlier discussion becomes obsolete, including considerations concerning different degrees of robustness of the various location and .dispersion estimators available. The penalty for "badly" performing laboratories is a large attributed uncertainty which forces them to either improve the measurement or adjust (i.e. reduce) their performance Claims. The evaluation algorithm is described. An example from a CCQM comparison is given, and consequences for the performance Claims of the participants discussed.