Manganese valence imaging in Mn minerals at the nanoscale using STEM-EELS
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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 L3/L2 white-line intensity ratios also were consistent with previous work. A calibration curve for Mn valence was produced using the L3/L2 white-line intensity ratios from measurements of synthetic standards. The average Mn valence was determined because it is not possible to distinguish Mn3+ from mixtures of Mn2+ and Mn4+ 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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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it