Podsednik, M., Weiss, M., Frank, J., Peck, E., Larisegger, S., Riedl, H., Mayrhofer, P. H., & Limbeck, A. (2025). Improved sampling strategy for high-performance materials: The introduction of the stethoscope cell for online-LASIL experiments. Microchemical Journal, 210, Article 112956. https://doi.org/10.1016/j.microc.2025.112956
The development and optimization of high-performance materials, which are crucial for efficient and sustainable technologies, require a precise quantitative analysis of their bulk composition. Online-laser ablation of solids in liquids (online-LASIL) combined with an ICP-MS detection is a method that allows simple determination of the elemental composition with commercially available liquid standards and avoids error-prone digestion of the sample. To overcome the limitations in the analysis of samples with varying geometries, the stethoscope cell was introduced to eliminate the problems of previous designs. In this work, the analytical performance of the stethoscope cell is demonstrated, enabling the analysis of samples with dimensions ranging between 7 and 20 mm and sample thicknesses up to 10 mm. Two application examples further show the achieved improvements using the stethoscope cell: A quantitative bulk measurement of an industrial TiW-sputter target and a depth-resolved analysis of protective coatings deposited on steel substrates. The analysis demonstrates the quantification of main and minor components within the samples with a measurement reproducibility varying between <1–10 %RSD depending on the concentration. To validate the results of the new cell design, acid digestions with a subsequent ICP-OES measurement were performed. Both online-LASIL measurements show a good agreement with the ICP-OES reference measurements, confirming the suitability of the stethoscope cell for quantitative and depth-resolved online-LASIL experiments.
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Project (external):
Austrian Research Promotion Agency (FFG)
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Project ID:
884573 ; 898207 ; 905107
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Research Areas:
Materials Characterization: 60% Surfaces and Interfaces: 40%