This paper proposes a systematic approach to XPS data analysis, emphasizing the importance of collaboration and prior knowledge. Using a molybdenum sulfate sample as a case study, the authors demonstrate a methodology for constructing peak models and interpreting complex spectra, highlighting the challenges posed by factors like surface contamination and overlapping signals. The analysis showcases how combining information from various sources can lead to a more accurate and comprehensive understanding of sample composition and chemical states, especially in cases of non-homogeneous samples, such as those of tribological origin.

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This paper presents a robust and detailed methodology for XPS data analysis, focusing on a collaborative approach and leveraging prior knowledge. Despite some limitations regarding generalizability and quantification uncertainty, the depth of analysis and practical guidance make it a valuable resource for XPS practitioners. The use of a real-world example and the discussion of experimental considerations further enhance its practical relevance.