Determining the effect of <sup>57</sup>Fe enrichment on NRIXS-derived force constants
Bibliographic record
Abstract
Non-traditional stable isotopes offer an important avenue of investigation into many Earth processes.Stable isotopes of Fe are regarded with particular interest, as Fe is present in many minerals.As a result, Fe isotope data has now been collected on a wide variety of samples over several decades.Interpreting these data, however, requires robust fractionation factors.Nuclear resonant inelastic X-ray scattering (NRIXS) is used to determine force constants for the Fe-sublattice in Fe-bearing phases.From these force constants, isotope fractionation factors can be derived [1].This method is particularly appealing as phases do not need to be equilibrated to measure an equilibrium fractionation factor.However, force constants derived from density functional theory (DFT) can be inconsistent with those found by NRIXS, with the NRIXS values generally being greater than the DFT values [1][2][3][4].We are investigating the source of this discrepancy between DFT and NRIXS force constants.NRIXS is restricted to measuring Mössbauer-sensitive isotopes, which for Fe is 57 Fe.Natural iron contains only 2.2% 57 Fe, so to expedite NRIXS analysis researchers typically dope their samples with 57 Fe far in excess of natural abundances.We hypothesize that artificially high 57 Fe content could be the source of the inflated NRIXS force constants.To test for this effect, we produced metal (Fe), wüstite (FeO), and fayalite (Fe2SiO4) with varying 57 Fe/SFe, ranging from 0.02 (natural) to 0.70.We are currently carrying out NRIXS measurements at beamline 3ID at the Advanced Photon Source.These results will allow us to determine whether a correlation exists between 57 Fe content and the measured force constants.Results will also inform decisions on the doping level for future NRIXS measurements conducted for isotope geochemistry.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".