Multi-phase quantitative compositional mapping by LA-ICP-MS: Analytical approach and data reduction protocol implemented in XMapTools
Bibliographic record
Abstract
Mapping of trace element signatures is an expanding tool in geoscience and material sciences, which allows the study of solid materials, and processes that may not be captured by major elements. Developments in laser-ablation inductively-coupled-plasma mass-spectrometry (LA-ICP-MS) capabilities in the last decade now provide the necessary spatial resolution for in situ element mapping. The acquisition of two-dimensional, fully quantitative and geologically meaningful data with LA-ICP-MS is still a challenging task, and a particular obstacle is the calibration of inhomogeneous phases, such as chemically zoned minerals. This work presents a novel approach to data reduction and image generation for multi-element mapping employing LA-ICP- quadrupole MS (LA-ICP-QMS), implemented in the free and open-source software XMapTools. Three geological applications are presented to illustrate the benefits of the procedures. Garnet from an eclogitic sample (Latto Hills, Togo) and plagioclase, k-feldspar, biotite from a migmatite sample (El Oro Complex, Ecuador) were mapped multiple times at different spatial resolutions to test the calibration quality and chemical detection capabilities. Rutile in a metapelite sample (Val Malenco, Italian Alps) was mapped, and Zr-in-rutile thermometry shows a temperature range of 510 to 550 °C within a single grain. The accuracy of the LA-ICP-MS method was verified by comparison with zoned major and minor element maps (garnet, plagioclase) and Ti-in-biotite geothermometry maps obtained by electron probe microanalysis (EPMA). A spatial resolution of up to 5 μm is achieved with LA-ICP-QMS, which is similar to the resolution reported for LA-ICP time-of-flight mass spectrometry (LA-ICP-TOFMS), albeit at significantly lower acquisition speed. Maps with lower spatial resolution offer better chemical detection power as demonstrated by lower per-pixel limit of detection (LOD) map calculation. Moreover, such maps are also recorded faster. The pixel allocation strategy and the instrumental conditions also have a direct impact on map quality. We recommend that maps are interpolated to square pixels, where a pixel consists of multiple sweeps to gain an improved detection power. Benchmarks using an emulated LA-ICP-MS mapping show that the spot size, together with scan direction, can lead to a shift in composition depending on the feature size of chemical patterns. This is verified by mapping a thin <50 μm annulus in garnet visible in REE and such compositional shifts can have a significant impact on e.g., diffusion modelling. The new software solution provides a multi-standard and variable composition calibration of LA-ICP-MS maps with single pixel LOD filtering at 95% confidence, allowing the user to quantify inhomogeneous materials of major and trace elements simultaneously with improved accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".