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An international interlaboratory comparison of the measurement capabilities of four National Metrology Institutes (NMIs) and one Designated Institute (DI) in the determination of the chemical composition of thin Fe-Ni alloy films was conducted via a key comparison (K-67) of the Surface Analysis Working Group of the Consultative Committee for Amount of Substance. This comparison was made using XPS (four laboratories) and AES (one laboratory) measurements. The uncertainty budget of the measured chemical composition of a thin alloy film was dominated by the uncertainty of the certified composition of a reference specimen which had been determined by inductively coupled plasma mass spectrometry using the isotope dilution method. Pilot study P-98 showed that the quantification using relative sensitivity factors (RSFs) of Fe and Ni derived from an alloy reference sample results in much more accurate result in comparison to an approach using RSFs derived from pure Fe and Ni films. The individual expanded uncertainties of the participants in the K-67 comparison were found to be between 2.88 and 3.40 atomic %. The uncertainty of the key comparison reference value (KCRV) calculated from individual standard deviations and a coverage factor (k) of 2 was 1.23 atomic %.
Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches.