DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data
<p>We report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per ex...
| Автори: | , , |
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| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
Oxford University Press
2019
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| _version_ | 1826317761833336832 |
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| author | Haley, D Bagot, PAJ Moody, MP |
| author_facet | Haley, D Bagot, PAJ Moody, MP |
| author_sort | Haley, D |
| collection | OXFORD |
| description | <p>We report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods.</p>
<p>By examining traditional DFs in an automated fashion in real space, rather than using Fourier transform approaches, we utilize an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artifacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost.</p>
<p>This extension is demonstrated on acquired aluminum and tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses.</p> |
| first_indexed | 2024-03-06T23:52:43Z |
| format | Journal article |
| id | oxford-uuid:732852e7-0b79-452e-a29d-e9563ec9acb5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-03-11T16:59:03Z |
| publishDate | 2019 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:732852e7-0b79-452e-a29d-e9563ec9acb52025-03-05T15:36:06ZDF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:732852e7-0b79-452e-a29d-e9563ec9acb5EnglishSymplectic Elements at OxfordOxford University Press2019Haley, DBagot, PAJMoody, MP<p>We report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods.</p> <p>By examining traditional DFs in an automated fashion in real space, rather than using Fourier transform approaches, we utilize an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artifacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost.</p> <p>This extension is demonstrated on acquired aluminum and tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses.</p> |
| spellingShingle | Haley, D Bagot, PAJ Moody, MP DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title | DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title_full | DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title_fullStr | DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title_full_unstemmed | DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title_short | DF-Fit: a robust algorithm for detection of crystallographic information in atom probe tomography data |
| title_sort | df fit a robust algorithm for detection of crystallographic information in atom probe tomography data |
| work_keys_str_mv | AT haleyd dffitarobustalgorithmfordetectionofcrystallographicinformationinatomprobetomographydata AT bagotpaj dffitarobustalgorithmfordetectionofcrystallographicinformationinatomprobetomographydata AT moodymp dffitarobustalgorithmfordetectionofcrystallographicinformationinatomprobetomographydata |