Surface analysis is a measurement science. Therefore, the standardization process is to find the best way to get the best quantitative analytical results. That means we have to erase every possibility to make errors or fluctuations based on the scientific inspection, and find the one best way to give the smallest error or fluctuation. This paper follows the track of the concept from the compatibility to standards, and introduces the relation between the standardization activities by VAMAS / ISO and the Surface Analysis Society of Japan.

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Development of the global ionizing radiation monitoring network (GIRMN) system has progressed significantly by the use of SrI₂(Eu)-scintillator which has excellent material properties for γ-ray spectroscopy; high light yield (>80,000 ph/MeV), excellent light yield proportionality, high photo electric cross-section, etc. The high energy resolution (<4 %) and high sensitivity of the SrI₂(Eu)-scintillator of 25mmφ × 25 mm high supplied by Union Materials Inc. Japan allows to discriminate the Cs originated γ-rays from the other nucleus species. The software platform newly adopted in the GIRMN is based on the use of ANSI N4242 Data File Format published by NIST and is, therefore, expected to afford the requirements for the standardization and sustainability as a standard ionizing radiation monitoring.

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In recent years, the function of materials has been improved by refinement and the surface modification, therefore an analysis of the surface morphology and the ingredient distribution is required. We have developed a fixing material made of carbon nanotube (CNT) microstructure inspired by gecko adhesive system, which inhibits contamination and charging. We found that observation of morphology of the outermost surface and an ingredient analysis became more accurate using the CNT microstructure.

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Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF-SIMS rawdata is often difficult. Especially the identification of all spectra of complex samples including large molecules is difficult, since the ToF-SIMS spectrum data of polymer samples includes many fragment ion peaks originating in polymers and other contamination peaks. Multivariate analysis has become effective methods for the interpretation of ToF-SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF-SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF-SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF-SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished.

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