A New Mapping Protocol for Laser Ablation (with <scp>Fast‐Funnel</scp>) Coupled to a <scp>Time‐of‐Flight</scp> Mass Spectrometer (<scp>LA‐FF‐ICP‐ToF‐MS</scp>) for the Rapid, Simultaneous Quantification of Multiple Minerals
Bibliographic record
Abstract
Although in situ analysis by LA‐ICP‐MS is considered a rapid technique with minimal sample preparation and data reduction, mapping areas of millimetres in size using a small beam (< 15 μm) can be time consuming (several hours) when a quadrupole ICP‐MS is used. In addition, fully quantitative imaging using internal standardisation by LA‐ICP‐MS is challenging in samples with more than one mineral phase present due to varying ablation rates. A new protocol for the quantification of multiple coexisting phases, mapped at a rate of about 12 mm 2 h ‐1 and a resolution of 12 μm × 12 μm per pixel, is presented. The protocol allows mapping of most atomic masses, ranging from 23 Na to 238 U, using a time‐of‐flight mass spectrometer (ICP‐ToF‐MS, TOFWERK) connected to a 193 nm excimer laser. A fast‐funnel device was successfully used to increase the aerosol transport speed, reducing the time usually required for mapping by a factor of about ten compared with a quadrupole ICP‐MS. The lower limits of detection for mid and heavy masses are in the range 0.1–10 μg g ‐1 , allowing determination of trace to ultra‐trace elements. The presented protocol is intended to be a routine analytical tool that can provide greater access to the spatial distribution of major and trace elements in geological materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".