Raman Imaging Techniques Compared for Analysis of Zirconium Oxide Layer

Article

The article discusses a comparison of Raman imaging assessment methods for phase determination and stress analysis of zirconium oxide layers and their application in the development of zirconium alloys, especially for nuclear applications.

Researchers at the National Centre for Nuclear Research in Otwock-Swierk, Poland have published a paper in the journal Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy comparing two methods of Raman imaging analysis (1). The study focuses on zirconium oxide, a material used in the development of zirconium alloys for nuclear applications.

Astronomy concept backdrop | Image Credit: © Sergey Nivens - stock.adobe.com

Astronomy concept backdrop | Image Credit: © Sergey Nivens - stock.adobe.com

Zirconium oxide plays a crucial role in the development of zirconium alloys for nuclear applications because of its properties such as corrosion resistance, thermal stability, and mechanical strength. Zirconium alloys are commonly used in nuclear fuel cladding and structural components of nuclear reactors due to their ability to withstand high temperatures and radiation exposure.

The paper describes how the researchers used both the built-in fitting function and K-means cluster analysis (KMC) followed by fitting in an external environment to evaluate data obtained from Raman imaging. The two methods were compared in terms of their principles, limitations, versatility, and process duration.

Built-in fitting function in Raman spectroscopy refers to the use of pre-defined mathematical models to fit Raman spectra to obtain the desired information such as peak positions, peak heights, and bandwidths. In contrast, K-means cluster analysis (KMC) is a statistical method that involves grouping Raman spectra into clusters based on their similarities without any prior assumptions. The main difference between these two approaches is that built-in fitting function relies on a priori knowledge of the system under study, whereas KMC is a data-driven approach that does not require any prior knowledge. Built-in fitting function is suitable for simple spectra where the peaks are well-defined and distinct, while KMC is more appropriate for complex spectra where the peaks overlap.

The researchers found that Raman imaging is indispensable for determining phase distribution, calculating phase content, and analyzing stress in zirconium oxide. By comparing the results obtained from the two methods, the researchers were able to identify the advantages and limitations of each. They hope that this will help researchers to select the most appropriate evaluation method for different applications.

Zirconium oxide is an important material in the development of zirconium alloys for nuclear applications. The ability to accurately analyze the phase distribution and stress in zirconium oxide is crucial for the development of these alloys. Raman imaging is a powerful tool for this type of analysis, as it allows researchers to visualize the distribution of different phases and the stresses present in the material.

The researchers used zirconium oxide formed on different zirconium alloys under various oxidation conditions as an exemplary material for their analysis. By comparing the results obtained from the two methods, they were able to demonstrate the importance of Raman imaging in this type of analysis.

The researchers believe that their work will be useful for researchers working in the field of materials science, particularly those working on the development of zirconium alloys for nuclear applications. By identifying the advantages and limitations of different evaluation methods, the researchers hope to help others select the most appropriate method for their specific needs.

Reference

(1) Suchorab, K.; Gaweda, M.; Kurpaska, L. Comparison of Raman imaging assessment methods in phase determination and stress analysis of zirconium oxide layer. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 2023, 295, 122625. DOI:10.1016/j.saa.2023.122625

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