Publications
Publication Information
| Title | Automation of particulate characterization |
| Authors | Josh Spradlin, A-M Valente-Feliciano, Olga Trofimova, Charles Reece |
| JLAB number | JLAB-ACC-19-3155 |
| LANL number | (None) |
| Other number | DOE/OR/23177-5414 |
| Document Type(s) | (Meeting) |
| Category: | SRF Technology |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Meeting Paper compiled for SRF 2019 (19th International Conference on RF Superconductivity) Proceedings Proceedings of SRF 2019 Edited By JACOW (2019) Page(s) 477-481 | |
| Publication Abstract: | Foreign particulates residing on high electric field surfaces of accelerator cavities present sources for field emission of electrons that limit the useful dynamic range of that cavity. Developing the methods and tools for collecting and characterizing particulates found in an accelerator enables process development towards creating and maintaining field emission free SRF cavities. Methods are presented for sampling assemblies, components, processes, and environmental conditions utilizing forensic techniques with specialized tooling. Sampling activities to date have produced an inventory of over 850 samples. Traditional SEM + EDS analysis of this volume of spindles is challenged by labor investment, spindle sampling methods, and the subsequent data pipeline which ultimately results in a statically inadequate dataset for any particulate distribution characterization. A complete systematic analysis of the spindles is enabled by third party software controlling SEM automation for EDS data acquisition. Details of spindle creation, collection equipment, component sampling, automating particle assessment, and data analysis used to characterize samples from beamline elements in CEBAF are presented. |
| Experiment Numbers: | other |
| Group: | SRF Processes & Materials |
| Document: | |
| DOI: | https://doi.org/10.18429/JACoW-SRF2019-TUP030 |
| Accepted Manuscript: | tup030.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |