Manganese valence imaging in Mn minerals at the nanoscale using STEM-EELS
Bibliographic record
Abstract
Electron energy loss spectroscopy (EELS) was used with scanning transmission electron microscopy (STEM) to quantify the average Mn valence in natural minerals at the nanometer scale. A method was developed to calibrate the energy-loss scale accurately, providing a comparison between STEMEELS and the X-ray absorption spectroscopy methods that investigate the L-edge chemical shift as Mn valence changes. The chemical-shift measurements were consistent with data reported by previous researchers from both X-ray and electron energy-loss spectroscopy. The L 3 /L 2 white-line intensity ratios also were consistent with previous work. A calibration curve for Mn valence was produced using the L 3 /L 2 white-line intensity ratios from measurements of synthetic standards. The average Mn valence was determined because it is not possible to distinguish Mn 3+ from mixtures of Mn 2+ and Mn 4+ using either method. The white-line intensity method was implemented in automated software that allows for rapid processing of point spectra, and 1-D and 2-D spectrum images. Point analyses of two natural pyrolusite samples indicated a Mn valence of 4.0, and point analyses of romanechite and manganite gave values of 3.8 and 3.4, respectively. An interface between braunite and bementite was used to illustrate 1-D and 2-D spectrum-imaging capabilities. The measured valence of Mn in the braunite and bementite was 2.9 and 2.0, respectively; both consistent with theoretical values. The braunitebementite sample demonstrated the heterogeneity of Mn valence common to natural minerals and the advantages of acquiring quantitative valence information in a known spatial context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".