The influence of voxel size on atom probe tomography data

Torres, Karen L.; Daniil, Maria; Willard, M. A.; Thompson, Gregory B.

doi:10.1016/j.ultramic.2011.01.001

Cited by 26 publications

(20 citation statements)

References 16 publications

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“…An isoconcentration surface of 3 at% Cu is utilized as a reference surface for the proxigram analysis which provided a topologically stable representation of Cu clusters when varying the isoconcentration value around 3 at% Cu. The volume occupied by the aforementioned Cu isoconcentration surface is also used for the estimation of Cu cluster size (diameter) and the volume fraction using the bounded volume approach [17]. The voxel edge length and delocalization were maintained at a constant ratio of 1:2 with the voxel edge length being 1 nm.…”

Section: Experimental Methodsmentioning

confidence: 99%

Atomic scale study of CU clustering and pseudo-homogeneous Fe–Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys

2015

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Section: Experimental Methodsmentioning

confidence: 99%

Atomic scale study of CU clustering and pseudo-homogeneous Fe–Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys

2015

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“…The second phase is to extract useful information from the reconstructed image; for example, to identify crystalline structures, clusters, and precipitates. There are two parameters of interest here which need to be determined during the 3D image reconstruction: the voxel size [19][20][21] and the elemental concentration threshold for the voxels. Normally these two parameters are determined empirically by trial and error-i.e., a value is set for the parameter and if the expected features are visible then the image is considered to be correct.…”

Section: Atom Probe Tomography Data and Analysismentioning

confidence: 99%

“…That is, by identifying where there is a change in crystal structure, we can identify phase transitions. A popular way to detect the crystalline structure in a set of atoms is to find repetitive patterns formed by local subsets of atoms [21]. The local subset of an atom is defined as the set of neighboring atoms within the nth coordinate shell together with the atom itself.…”

Section: Atom Probe Tomography Data and Analysismentioning

confidence: 99%

Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images

2018

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Atom probe tomography (APT) represents a revolutionary characterization tool for materials that combine atomic imaging with a time-of-flight (TOF) mass spectrometer to provide direct space three-dimensional, atomic scale resolution images of materials with the chemical identities of hundreds of millions of atoms. It involves the controlled removal of atoms from a specimen's surface by field evaporation and then sequentially analyzing them with a position sensitive detector and TOF mass spectrometer. A paradox in APT is that while on the one hand, it provides an unprecedented level of imaging resolution in three dimensions, it is very difficult to obtain an accurate perspective of morphology or shape outlined by atoms of similar chemistry and microstructure. The origins of this problem are numerous, including incomplete detection of atoms and the complexity of the evaporation fields of atoms at or near interfaces. Hence, unlike scattering techniques such as electron microscopy, interfaces appear diffused, not sharp. This, in turn, makes it challenging to visualize and quantitatively interpret the microstructure at the "meso" scale, where one is interested in the shape and form of the interfaces and their associated chemical gradients. It is here that the application of informatics at the nanoscale and statistical learning methods plays a critical role in both defining the level of uncertainty and helping to make quantitative, statistically objective interpretations where heuristics often dominate. In this chapter, we show how the tools of Topological Data Analysis provide a new and powerful tool in the field of nanoinformatics for materials characterization.

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“…Unfortunately, this algorithm seemed to work only for strong compositional variations [56]. It had already been successfully tested for adaptation to 3D voxels, but all details of this conversion was not presented [57].…”

Section: Voxel Size For Visualization Of Small--scale Fluctuationsmentioning

confidence: 99%

Atom Probe Tomography of TiSiN Thin Films

Engberg¹

2015

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This thesis concerns the wear resistant coating TiSiN and the development of the analysis technique atom probe tomography (APT) applied to this materials system. The technique delivers compositional information through time--of--flight mass spectrometry, with sub--nanometer precision in 3D for a small volume of the sample. It is thus a powerful technique for imaging the local distribution of elements in micro and nanostructures. To gain the full benefits of the technique for the materials system in question, I have developed a method that combines APT with isotopic substitution, here demonstrated by substitution of nat N with 15 N. This alters the time--of--flight of ions with of one or more N and will thereby enable the differentiation of the otherwise inseparable isotopes 14 N and 28 Si. Signs of small--scale fluctuations in the data led the development of an algorithm needed to properly visualize these fluctuations. A method to identify the best sampling parameter for visualization of small--scale compositional fluctuations was added to an algorithm originally designed to find the best sampling parameters for measuring and visualizing strong compositional variations. With the identified sampling parameters, the nano--scale compositional fluctuations of Si in the metal/metalloid sub--lattice could be visualized. The existence and size of these fluctuations were corroborated by radial distribution functions, a technique independent of the previously determined sampling parameter. The radial distribution function algorithm was also developed further to ease in the interpretation. The number of curves could thereby be reduced by showing elements, rather than single and molecular ions (of which there were several different kinds). The improvement of the algorithm also allowed interpretation of signs regarding the stoichiometry of SiNy. With a combination of analytical transmission electron microscopy and APT we show Si segregation on the nanometer scale in arc--deposited Ti0.92Si0.08 15 N and Ti0.81Si0.19 15 N thin films. APT composition maps and proximity histograms generated from Ti--rich domains show that the TiN contain at least ~2 at. % Si for Ti0.92Si0.08N and ~5 at. % Si for Ti0.81Si0.19N, thus confirming the formation of solid solutions. The formation of relatively pure SiNy domains in the Ti0.81Si0.19N films is tied to pockets between microstructured, columnar features in the film. Finer SiNy enrichments seen in APT possibly correspond to tissue layers around TiN crystallites, thus effectively hindering growth of TiN crystallites, causing TiN renucleation and thus explaining the featherlike nanostructure within the columns of these films. IVV

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The influence of voxel size on atom probe tomography data

Cited by 26 publications

References 16 publications

Atomic scale study of CU clustering and pseudo-homogeneous Fe–Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys

Atomic scale study of CU clustering and pseudo-homogeneous Fe–Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys

Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images

Atom Probe Tomography of TiSiN Thin Films

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