Meisenkothen Frederick, Samarov Daniel V, Kalish Irina, Steel Eric B
Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States.
Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States.
Ultramicroscopy. 2020 Sep;216:113018. doi: 10.1016/j.ultramic.2020.113018. Epub 2020 May 21.
Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.
原子探针断层扫描（APT）理论上能够以高灵敏度、高精度和高空间分辨率进行准确的化学和同位素分析。然而，实际的APT数据往往包含显著偏差，导致化学浓度和同位素丰度测量出现错误。本研究探讨了通过原子探针质谱法进行定量同位素分析的准确性。开发了一种基于机器学习的自适应峰拟合算法，以提供一种可重复且在数学上合理的方法来确定特定离子物种在质谱图中的峰形和强度。将原子探针进行的同位素丰度测量直接与每种材料的已知同位素丰度值进行比较。即使存在极高数量的多次命中检测事件（高达80%），且未进行任何死时间校正，我们的方法所产生的同位素丰度测量结果的准确性与主要受计数统计限制的值一致。
