Zirconium alloys are common fuel claddings in nuclear fission reactors and are susceptible to the effects of hydrogen embrittlement. There is a need to be able to detect and image hydrogen at the atomic scale to gain the experimental evidence necessary to fully understand hydrogen embrittlement. Through the use of deuterium tracers, atom probe tomography (APT) is able to detect and spatially locate hydrogen at the atomic scale. Previous works have highlighted issues with quantifying deuterium concentrations using APT due to complex peak overlaps in the mass-to-charge-state ratio spectrum between molecular hydrogen and deuterium (H2 and D). In this work, we use new methods to analyze historic and simulated atom probe data, by applying currently available data analysis tools, to optimize solving peak overlaps to improve the quantification of deuterium. This method has been applied to literature data to quantify the deuterium concentrations in a concentration line profile across an α-Zr/deuteride interface.

One of the main capabilities of atom probe tomography (APT) is the ability to not only identify but also characterize early stages of precipitation at length scales that are not achievable by other techniques. One of the most popular methods to identify nanoscale clustering in APT data, based on the density-based spatial clustering of applications with noise (DBSCAN), is used extensively in many branches of research. However, it is common that not all of the steps leading to the selection of certain parameters used in the analysis are reported. Without knowing the rationale behind parameter selection, it may be difficult to compare cluster parameters obtained by different researchers. In this work, a simple open-source tool, PosgenPy, is used to justify cluster search parameter selection via providing a systematic sweep through parameter values with multiple randomizations to minimize a false-positive cluster ratio. The tool is applied to several different microstructures: a simulated material system and two experimental datasets from a low-alloy steel . The analyses show how values for the various parameters can be selected to ensure that the calculated cluster number density and cluster composition are accurate.

We investigated a new method for estimating specimen hydrogen content in atom probe tomography (APT) in experiments where molecular hydrogen ions (${\rm H}_2^ +$) originating from the measurement environment can overlap with deuterium (D+) in the mass-to-charge-state spectrum, thus preventing the direct application of isotopic marking for unambiguous hydrogen analysis. First, we applied an existing method for hydrogen content estimation, using ${\rm H}^ + {/}{\rm H}_2^ +$ ratios obtained from paired deuterated/nondeuterated experiments. These measurements demonstrated sufficient residual uncertainty to motivate exploring an alternative method to accurately estimate hydrogen content. By varying the time between evaporation events, it is then shown that a highly correlated relationship between field evaporation rate and hydrogen content exists and can also be used to predict hydrogen content. This leads to a new method for measuring hydrogen content within the specimen. We combine this extrapolation technique with continuous cycling of the evaporation rate or pulse frequency during an APT experiment. This could enable spatially resolved imaging of hydrogen concentrations despite the presence of a contaminant background hydrogen signal, without the need for deuteration.

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.

In this work, we demonstrate a new system for the examination of gas interactions with surfaces via atom probe tomography. This system provides capability of examining the surface and subsurface interactions of gases with a wide range of specimens, as well as a selection of input gas types. This system has been primarily developed to aid the investigation of hydrogen interactions with metallurgical samples, to better understand the phenomenon of hydrogen embrittlement. In its current form, it is able to operate at pressures from 10−6 to 1000 mbar (abs), can use a variety of gasses, and is equipped with heating and cryogenic quenching capabilities. We use this system to examine the interaction of hydrogen with Pd, as well as the interaction of water vapor and oxygen in Mg samples.

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.

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.

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.

All organisms encounter pathogens, and birds are especially susceptible to infection by malaria parasites and other haemosporidians. It is important to understand how immune genes, primarily innate immune genes which are the first line of host defense, have evolved across birds, a highly diverse group of tetrapods. Here, we find that innate immune genes are highly conserved across the avian tree of life and that although most show evidence of positive or diversifying selection within specific lineages or clades, the number of sites is often proportionally low in this broader context of putative constraint. Rather, evidence shows a much higher level of negative or purifying selection in these innate immune genes – rather than adaptive immune genes – which is consistent with birds' long coevolutionary history with pathogens and the need to maintain a rapid response to infection. We further explored avian responses to haemosporidians by comparing differential gene expression in wild birds (1) uninfected with haemosporidians, (2) infected with Plasmodium and (3) infected with Haemoproteus (Parahaemoproteus). We found patterns of significant differential expression with some genes unique to infection with each genus and a few shared between ‘treatment’ groups, but none that overlapped with the genes included in the phylogenetic study.

Due to the intrinsic evaporation properties of the material studied, insufficient mass-resolving power and lack of knowledge of the kinetic energy of incident ions, peaks in the atom probe mass-to-charge spectrum can overlap and result in incorrect composition measurements. Contributions to these peak overlaps can be deconvoluted globally, by simply examining adjacent peaks combined with knowledge of natural isotopic abundances. However, this strategy does not account for the fact that the relative contributions to this convoluted signal can often vary significantly in different regions of the analysis volume; e.g., across interfaces and within clusters. Some progress has been made with spatially localized deconvolution in cases where the discrete microstructural regions can be easily identified within the reconstruction, but this means no further point cloud analyses are possible. Hence, we present an ion-by-ion methodology where the identity of each ion, normally obscured by peak overlap, is resolved by examining the isotopic abundance of their immediate surroundings. The resulting peak-deconvoluted data are a point cloud and can be analyzed with any existing tools. We present two detailed case studies and discussion of the limitations of this new technique.

In this work, we report on the atom probe tomography analysis of two metallic hydrides formed by pressurized charging using an ex situ hydrogen charging cell, in the pressure range of 200–500 kPa (2–5 bar). Specifically we report on the deuterium charging of Pd/Rh and V systems. Using this ex situ system, we demonstrate the successful loading and subsequent atom probe analysis of deuterium within a Pd/Rh alloy, and demonstrate that deuterium is likely present within the oxide–metal interface of a native oxide formed on vanadium. Through these experiments, we demonstrate the feasibility of ex situ hydrogen analysis for hydrides via atom probe tomography, and thus a practical route to three-dimensional imaging of hydrogen in hydrides at the atomic scale.

Atom probe is a nanoscale technique for creating three-dimensional spatially and chemically resolved point datasets, primarily of metallic or semiconductor materials. While atom probe can achieve local high-level resolution, the spatial coherence of the technique is highly dependent upon the evaporative physics in the material and can often result in large geometric distortions in experimental results. The distortions originate from uncertainties in the projection function between the field evaporating specimen and the ion detector. Here we explore the possibility of continuum numerical approximations to the evaporative behavior during an atom probe experiment, and the subsequent propagation of ions to the detector, with particular emphasis placed on the solution of axisymmetric systems, such as isolated particles and multilayer systems. Ultimately, this method may prove critical in rapid modeling of tip shape evolution in atom probe tomography, which itself is a key factor in the rapid generation of spatially accurate reconstructions in atom probe datasets.

We present a summary of a three-year academic research proposal drafted during the Sao Paulo Advanced School of Astrobiology (SPASA) to prepare for upcoming observations of tidally locked planets orbiting M-dwarf stars. The primary experimental goal of the suggested research is to expose extremophiles from analogue environments to a modified space simulation chamber reproducing the environmental parameters of a tidally locked planet in the habitable zone of a late-type star. Here we focus on a description of the astronomical analysis used to define the parameters for this climate simulation.