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.

中文翻译：

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探索原子探针质谱中同位素分析的准确性

原子探针断层扫描 (APT) 理论上可以以高灵敏度、精度和空间分辨率提供准确的化学和同位素分析。然而，经验 APT 数据通常包含显着的偏差，导致错误的化学浓度和同位素丰度测量。本研究探讨了通过原子探针质谱法进行的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，以提供一种可重现且数学上可靠的方法来确定特定离子种类的质谱中的峰形状和强度。使用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。