Optimisation of mass ranging for atom probe microanalysis and application to the corrosion processes in Zr alloys

Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemical composition features and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterization tasks. For atom-probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT data sets contain substantial noise. Given that sufficiently general predictive models for such noise remain elusive, crystallography tools for APT face several limitations: their robustness to noise is limited, and therefore so too is their capability to identify and distinguish different crystal structures and orientations. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantification with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, the existing methods are reviewed and how they link to the methods currently used by the electron and X-ray diffraction communities is discussed. As a result of this, some of the APT methods are modified to yield more robust descriptors of the atomic arrangement. Also reported is how this enables the development of an open-source software tool for strong scaling and automated identification of a crystal structure, and the mapping of crystal orientation in nanocrystalline APT data sets with multiple phases.

Study of LEAP® 5000 Deadtime and Precision via Silicon Pre-Sharpened-Microtip™ Standard Specimens

Atom probe tomography (APT) is a technique that has expanded significantly in terms of adoption, dataset size, and quality during the past 15 years. The sophistication used to ensure ultimate analysis precision has not kept pace. The earliest APT datasets were small enough that deadtime and background considerations for processing mass spectrum peaks were secondary. Today, datasets can reach beyond a billion atoms so that high precision data processing procedures and corrections need to be considered to attain reliable accuracy at the parts-per-million level. This paper considers options for mass spectrum ranging, deadtime corrections, and error propagation as applied to an extrinsic-silicon standard specimen to attain agreement for silicon isotopic fraction measurements across multiple instruments, instrument types, and acquisition conditions. Precision consistent with those predicted by counting statistics is attained showing agreement in silicon isotope fraction measurements across multiple instruments, instrument platforms, and analysis conditions.

Machine-learning-enhanced time-of-flight mass spectrometry analysis

Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.

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A machine-learning method provides reliable atomic/molecular labels for ToF-MS

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No human labeling or prior information required

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The training dataset is artificially generated based on isotopic abundances

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Method validated on a variety of materials and two ToF-MS-based techniques

Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data.

Atom probe tomography

Atom probe tomography (APT) provides three-dimensional compositional mapping with sub-nanometre resolution. The sensitivity of APT is in the range of parts per million for all elements, including light elements such as hydrogen, carbon or lithium, enabling unique insights into the composition of performance-enhancing or lifetime-limiting microstructural features and making APT ideally suited to complement electron-based or X-ray-based microscopies and spectroscopies. Here, we provide an introductory overview of APT ranging from its inception as an evolution of field ion microscopy to the most recent developments in specimen preparation, including for nanomaterials. We touch on data reconstruction, analysis and various applications, including in the geosciences and the burgeoning biological sciences. We review the underpinnings of APT performance and discuss both strengths and limitations of APT, including how the community can improve on current shortcomings. Finally, we look forwards to true atomic-scale tomography with the ability to measure the isotopic identity and spatial coordinates of every atom in an ever wider range of materials through new specimen preparation routes, novel laser pulsing and detector technologies, and full interoperability with complementary microscopy techniques.

Processing APT Spectral Backgrounds for Improved Quantification

We describe a method to estimate background noise in atom probe tomography (APT) mass spectra and to use this information to enhance both background correction and quantification. Our approach is mathematically general in form for any detector exhibiting Poisson noise with a fixed data acquisition time window, at voltages varying through the experiment. We show that this accurately estimates the background observed in real experiments. The method requires, as a minimum, the z-coordinate and mass-to-charge-state data as input and can be applied retrospectively. Further improvements are obtained with additional information such as acquisition voltage. Using this method allows for improved estimation of variance in the background, and more robust quantification, with quantified count limits at parts-per-million concentrations. To demonstrate applications, we show a simple peak detection implementation, which quantitatively suppresses false positives arising from random noise sources. We additionally quantify the detectability of 121-Sb in a standardized-doped Si microtip as (1.5 × 10−5, 3.8 × 10−5) atomic fraction, α = 0.95. This technique is applicable to all modes of APT data acquisition and is highly general in nature, ultimately allowing for improvements in analyzing low ionic count species in datasets.

Atom Probe Mass Spectrometry of Uranium Isotopic Reference Materials

Atom probe tomography (APT)-based isotopic analyses are becoming increasingly attractive for analysis applications requiring small volumes of material and sub-micrometer length scales, such as isotope geochemistry, nuclear safety, and materials science. However, there is an open question within the atom probe community as to the reliability of atom probe isotopic and elemental analyses. Using our proposed analysis guidelines, in conjunction with an empirical calibration curve and a machine learning-based adaptive peak fitting algorithm, we demonstrate accurate and repeatable uranium isotopic analyses, via atom probe mass spectrometry, on U₃O₈ isotopic reference materials. By using isotopic reference materials, each measured isotopic abundance value could be directly compared to a known certified reference value to permit a quantitative statement of accuracy. The isotopic abundance measurements for ²³⁵U and ²³⁸U in each individual APT sample were consistently within ±1.5% relative to the known reference values. The accuracy and repeatability are approaching values consistent with measurements limited primarily by Poisson counting statistics, i.e., the number of uranium atoms recorded.

Exploring the accuracy of isotopic analyses in atom probe mass spectrometry

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.

Enhancing Element Identification by Expectation-Maximization Method in Atom Probe Tomography

This paper describes an alternative way to assign elemental identity to atoms collected by atom probe tomography (APT). This method is based on Bayesian assignation of label through the expectation-maximization method (well known in data analysis). Assuming the correct shape of mass over charge peaks in mass spectra, the probability of each atom to be labeled as a given element is determined, and is used to enhance data visualization and composition mapping in APT analyses. The method is particularly efficient for small count experiments with a low signal to noise ratio, and can be used on small subsets of analyzed volumes, and is complementary to single-ion decomposition methods. Based on the selected model and experimental examples, it is shown that the method enhances our ability to observe and extract information from the raw dataset. The experimental case of the superimposition of the Si peak and N peak in a steel is presented.

Quantifying Uncertainty from Mass-Peak Overlaps in Atom Probe Microscopy

There are many sources of random and systematic error in composition quantification by atom probe microscopy, often, however, only statistical error is reported. Significantly larger errors can occur from the misidentification of ions and overlaps or interferences of peaks in the mass spectrum. These overlaps can be solved using maximum likelihood estimation (MLE), improving the accuracy of the result, but with an unknown effect on the precision. An analytical expression for the uncertainty of the MLE solution is presented and it is demonstrated to be much more accurate than the existing methods. In one example, the commonly used error estimate was five times too small.Literature results containing overlaps most likely underestimate composition uncertainty because of the complexity of correctly dealing with stochastic effects and error propagation. The uncertainty depends on the amount of overlapped intensity, for example being ten times worse for the CO/Fe overlap than the Cr/Fe overlap. Using the methods described here, accurate estimation of error, and the minimization of this could be achieved, providing a key milestone in quantitative atom probe. Accurate estimation of the composition uncertainty in the presence of overlaps is crucial for planning experiments and scientific interpretation of the measurements.

Nanoscale Stoichiometric Analysis of a High-Temperature Superconductor by Atom Probe Tomography

The functional properties of the high-temperature superconductor Y1Ba2Cu3O7-δ (Y-123) are closely correlated to the exact stoichiometry and oxygen content. Exceeding the critical value of 1 oxygen vacancy for every five unit cells (δ>0.2, which translates to a 1.5 at% deviation from the nominal oxygen stoichiometry of Y7.7Ba15.3Cu23O54-δ ) is sufficient to alter the superconducting properties. Stoichiometry at the nanometer scale, particularly of oxygen and other lighter elements, is extremely difficult to quantify in complex functional ceramics by most currently available analytical techniques. The present study is an analysis and optimization of the experimental conditions required to quantify the local nanoscale stoichiometry of single crystal yttrium barium copper oxide (YBCO) samples in three dimensions by atom probe tomography (APT). APT analysis required systematic exploration of a wide range of data acquisition and processing conditions to calibrate the measurements. Laser pulse energy, ion identification, and the choice of range widths were all found to influence composition measurements. The final composition obtained from melt-grown crystals with optimized superconducting properties was Y7.9Ba10.4Cu24.4O57.2.

Evaluation of Analysis Conditions for Laser-Pulsed Atom Probe Tomography: Example of Cemented Tungsten Carbide

Cemented tungsten carbide has been analyzed using laser-pulsed atom probe tomography (APT). The influence of experimental parameters, including laser pulse energy, pulse repetition rate, and specimen base temperature, on the acquired data were evaluated from different aspects, such as mass spectrum, chemical composition, noise-to-signal ratio, and multiple events. Within all the applied analysis conditions, only 1 MHz pulse repetition rate led to a strong detector saturation effect, resulting in a largely biased chemical composition. A comparative study of the laser energy settings showed that an ~12 times higher energy was required for the less focused green laser of the LEAPTM 3000X HR system to achieve a similar evaporation field as the finer spot ultraviolet laser of the LEAPTM 5000 XS system.

Interpreting atom probe data from chromium oxide scales

Picosecond-pulsed ultraviolet-laser (UV-355 nm) assisted atom probe tomography (APT) was used to analyze protective, thermally grown chromium oxides formed on stainless steel. The influence of analysis parameters on the thermal tail observed in the mass spectra and the chemical composition is investigated. A new parameter termed "laser sensitivity factor" is introduced in order to quantify the effect of laser energy on the extent of the thermal tail. This parameter is used to compare the effect of increasing laser energy on thermal tails in chromia and chromite samples. Also explored is the effect of increasing laser energy on the measured oxygen content and the effect of specimen base temperature and laser pulse frequency on the mass spectrum. Finally, we report a preliminary analysis of molecular ion dissociations in chromia.