When crystallography can use help from tin-119 Mössbauer spectroscopy
Bibliographic record
Abstract
Crystallography, the most powerful method for obtaining structural data, can benefit from help from other techniques. In this work, 119Sn Mössbauer spectroscopy was used to assist crystallography, for finding the tin(II) positions in the unit-cell and determine a tin(II) coordination in agreement with both the diffraction data and the tin electronic structure. Even high quality single crystal data do not guarantee that the right solution will be obtained. A first attempt at the structure of α–SnF2 yielded the tin positions with very reasonable R and Rw residuals, 0.23-0.25. However, the fluorine positions could not be found (Bergerhoff, 1962). After many other attempts, the full crystal structure was finally solved 14 years later (R.C. McDonald et al. 1976). The difference in the tin position with the initial solution (1962) was that, in the latter, half of the tin atoms were on special sites, however, the tin sublattice was identical. Because the tin sites in the initial solution gave very reasonable residuals, 14 years of hopeless efforts were wasted. The presentation will show that this could have been avoided using 119Sn Mössbauer spectroscopy. This was possible since the spectrum had already been recorded (A.J.F. Boyle et al., 1962). Mössbauer spectroscopy can also help determine the tin coordination, when combined with powder diffraction data, in case of disordered structures. The presence of tin(II), disordered with a metal ion in cubic coordination, when diffraction shows there is no lattice distortion and no superstructure, suggests that tin has also a cubic coordination. This would require the tin lone pair to be non-stereoactive; however Mössbauer spectroscopy shows it is stereoactive.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.072 |
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".