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Compute and plot a 1D concentration profile (composition vs. distance) from ranged/allocated atom probe data by voxelising along one axis. Use when the user wants a concentration profile, line profile, or composition-along-z plot.
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| name | apt-profile-1d |
| description | Compute and plot a 1D concentration profile (composition vs. distance) from ranged/allocated atom probe data by voxelising along one axis. Use when the user wants a concentration profile, line profile, or composition-along-z plot. |
| argument-hint | [bin-nm] |
Build a 1D concentration profile by slicing the reconstructed tip into bins along one distance axis and computing the composition of each slice. Use the MATLAB MCP server.
The MATLAB workspace must contain:
pos — ranged/allocated atom probe data with an ion/atom column (from /apt-composition)colorScheme — ion color table (from /apt-setup)If pos has no ion column, tell the user to run /apt-composition first (ranging + posAllocateRange). If colorScheme is missing, run /apt-setup.
dist = pos.z; % distance variable to bin along (z is the default profile direction)
bin = 1; % bin width in nm
mode = 'distance';
[binCenters, binEdges] = binVectorsFromDistance(dist, bin, mode);
bin is the slice thickness in nm. Default 1 nm; use 0.25 nm near interfaces, 0.5 nm for a typical profile. Parse $ARGUMENTS for a bin width if the user supplied one. To profile along a different direction use pos.x or pos.y, or supply an ROI/interface distance from /apt-roi. There is also a 'count' mode (equal atoms per bin, e.g. bin = 390000) used mainly for proxigrams — only use it if the user explicitly asks.
vox = posNdBin(pos, dist, binEdges); % cell array: one pos subset per slice
The last slice is usually thinner / has fewer atoms than the rest — this is expected.
detEff = 37; % detector efficiency, percent or decimal both accepted
excludeListConc = {'unranged'}; % species ignored in the composition, optional
volumeName = ''; % volume label, optional
concentrationKernel = @(pos) posCalculateConcentrationSimple(pos, detEff, excludeListConc, volumeName);
The detection efficiency is critical for correct composition and must match the instrument: 37 (LEAP 4000X HR), 52 (LEAP 5000 XR), 80 (LEAP 5000 XS), 50 (EIKOS). If the user has not confirmed their instrument, ask before proceeding. Read it from config if available: cfg = APTConfig.getInstance(); detEff = cfg.reconstruction.detectionEfficiency*100;.
conc1D = binApplyConcentrationKernel(vox, binCenters, concentrationKernel, {'nm'});
This returns one table holding the concentration, counts and variance per slice, tagged with the bin-center distance in nm. Composition is reported with counting-based variance, so each point carries a statistical uncertainty (see concentrationUncertainty below).
excludeListPlot = {'unranged'}; % species hidden from the plot, optional
[p, ax, f] = concentrationProfilePlot(conc1D(conc1D.format=='concentration', :), excludeListPlot, colorScheme);
This plots concentration vs. distance for all ranged species (up to 4 volumes can be overlaid). ax is left in the workspace for further tweaking.
conc = cellfun(concentrationKernel, vox, 'UniformOutput', false);
concWantedSpecies = cellfun(@(x) x.Fe(x.format == 'concentration'), conc); % replace Fe with element/complex ion
concWantedSpecies is a column vector of the fraction of that species per slice, aligned with binCenters{1}.
unc = concentrationUncertainty(conc1D.counts);
Reports the statistical (counting) uncertainty on the profile values.
Tell the user the bin width used, the distance axis, and the detection efficiency applied, and that a 1D profile figure was created. Mention that each point has counting uncertainty (smaller bins = noisier profiles). Suggest:
/apt-roi to define a profile direction or sub-region (e.g. across an interface) before re-running/apt-profile-2d for a 2D concentration mapwritetable(conc1D, 'profile_1d.csv') or save the figure with exportgraphics(ax, 'profile_1d.png')Voronoi-cell cluster determination (Felfer et al. 2015) — detect and extract clusters via Voronoi-cell volumes, Delaunay triangulation, and a Kolmogorov-Smirnov test against a randomised reference. Use when the user wants the Voronoi/clusterDetermination method specifically (this is NOT DBSCAN — for density-based DBSCAN use /apt-cluster instead).
Create a new ion color scheme or add ions/colors to the existing one. Produces/updates the `colorScheme` workspace variable used by ranging and 3D visualization everywhere else. Use when the user wants custom ion colors, a new color scheme, or to add a missing ion color.
Per-vertex concentration mapping onto a mesh/interface — compute atom-to-mesh distances, clip a slab around the interface, voxelise by nearest vertex, and apply a concentration kernel to colour each vertex. Use when the user wants a concentration map painted onto an isosurface or modelled grain boundary mesh.
Run quality-control checks on an atom probe dataset before analysis — basic stats, voltage curve, detector hit map (FDM), mass spectrum sanity, coordinate ranges, multi-hit fraction, and automated quality metrics. Use when the user wants to assess data quality or sanity-check a reconstruction.
Create a field desorption map (detector hit-density / concentration image) and optionally a movie of detector activity over the course of the experiment. Use when the user wants an FDM, desorption map, detector image, or to watch features evolve during the measurement.
Quantify interfacial excess (Gibbsian excess of solute, in at/nm^2) across an interface — either a single value via the Krakauer-Seidman integral method, or a 2D excess map over the surface. Use when the user wants the amount of an element segregated to an interface or grain boundary.