Over the last decade, the interstitial alloying of niobium has proven to be essential for enabling superconducting radiofrequency (SRF) cavities to operate more efficiently at high accelerating gradients. The discovery of "nitrogen doping" was the first readily accessible avenue of interstitial alloying in which researchers saw an increase in cavity performance. However, the serendipitous nature of the discovery led to additional research to fundamentally understand the physics behind the increase in cavity performance. This knowledge gap is bridged by materials characterization. Secondary ion mass spectrometry (SIMS) is a characterization technique which has become a staple of SRF cavity characterization that details elemental concentration profiles as a function of depth into the niobium surface with submicron resolution.
SIMS has been widely used by the semiconductor industry for decades but has found less application in other fields due to the difficulty to produce reproducible data for polycrystalline materials. Much effort has been given to reduce the uncertainty of SIMS results to as low as 1% - 2% for single crystals. However, less attention has been given to polycrystalline materials with uncertainty values reported between 40% - 50% The sources of uncertainty were found to be deterministic in nature and therefore could be mitigated to produce reliable results. This dissertation documents the efforts to reduce SIMS method uncertainty which has been further used to solve mysteries regarding the characterization of SRF cavities which include predictive modeling of oxygen diffusion as well as the identification of contaminants resulting from cavity furnace treatments. / Doctor of Philosophy / Particle accelerators find many uses in society in which their complexity depends on their intended purpose. These purposes vary from projecting an image as in cathode ray tube (CRT) TVs, to creating high energy x-rays for life saving cancer treatments, to researching the very fundamental principles of science and physics. The later uses particle accelerators which are very large, spanning multiple miles, and run at extremely high energies. To keep operational costs reasonable, these instruments need to run as efficiently as possible. Therefore, superconducting radiofrequency (SRF) niobium cavities are used and are responsible for propulsion of charged particles.
Although, niobium SRF cavities can pass incredibly high currents with very little surface resistance, these high-end particle accelerators push the operational boundaries of efficiency. To improve the efficiency of these cavities, an optimal concentration of impurities, such as oxygen and nitrogen, are added to the niobium surface. The addition of an impurity level that is too high or too low causes the resistance to increase which translates to higher operational costs. Therefore, it is important to accurately determine the concentration of impurities within the niobium and with high depth resolution.
Secondary ion mass spectrometry is a characterization method that uses a primary ion beam to impact the surface of a material to remove atoms at the very surface. The ejected atoms are then ionized into a secondary beam which can then be detected to determine the concentration and to identify the species which was removed. Historically, this instrument has yielded results with 40% - 50% uncertainty for polycrystalline metals, such as niobium, which do not sputter evenly. With SRF cavity performance depending on accurate quantitation of impurities, a more robust method is needed. Therefore, this dissertation identifies issues which cause high uncertainties for polycrystalline materials and in addition offers mitigation strategies to reduce uncertainty to below 10%. These methods were then applied to solve real problems aimed to improve the production of niobium SRF cavities.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/109615 |
| Date | 08 April 2022 |
| Creators | Angle, Jonathan Willis |
| Contributors | Materials Science and Engineering, Kelley, Michael J., Stevie, Fred A., Murayama, Mitsuhiro, Reece, Charles E., Reynolds, William T. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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