Mössbauer spectroscopy of phyllosilicates: effects of fitting models on recoil-free fractions and redox ratios
Bibliographic record
Abstract
Abstract Clay minerals are ubiquitous constituents in soils on Earth, are occasionally found in meteorites, and may also occur on planetary surfaces in the presence of water. However, little is known about the fundamental Mössbauer parameters (the intrinsic isomer shift, δI, the characteristic Mössbauer temperature, θM, and the recoil-free fraction, f) that are characteristic of clay minerals and critical to the correct interpretation of the Fe3+/ΣFe ratios as well as the mineral modes. Spectra of well characterized single mineral samples at multiple temperatures may be used for the determinations of f. Hence, measurements of five-layer silicates with a range of layer types are presented here: nontronite, Fe-smectite, glauconite, annite and biotite. The spectra were fitted using three different software packages: WMOSS from Science, Engineering & Education Co. in Minnesota; Recoil, from the University of Ottawa in Canada; and two programs used at the University of Ghent in Belgium. Four different approaches to modelling line shapes were used: (1) Lorentzian; (2) pseudo-Voigt (convolution of Lorentzian and Gaussian curves); (3) quadrupole-splitting distributions (QSD); and (4) a technique that does not assume a particular line shape (subsequently referred to as ‘model-independent’). Values of δI, θM and f were determined using the method of De Grave & Van Alboom (1991). Results show that multiple doublets are routinely required by all models to represent Fe-site occupancy, even when all the Fe atoms of the same valence are in the same site, as is the case for dioctahedral smectite, nontronite, mica and glauconite. Consistent values of centre shift (δ) and quadrupole splitting (Δ) were obtained for the two distributions of M2Fe3+ in the smectites. In glauconite, a single Fe2+ doublet was clearly resolved and gave systematic values for δ, Δ and area, but the two Fe3+ doublets were less defined. In annite, two Fe2+ and two Fe3+ doublets were modelled, while three Fe2+ and one Fe3+ doublet were used for biotite. Three different programs that use Lorentzian line shapes gave very similar results for δ, Δ and area. The two different implementations of QSD line shapes gave similar but sometimes slightly different results, and the pseudo-Voigt and model-independent fits usually fell between the ranges for Lorentzian and QSD results. The value of δI is ~0.58 mm/s for Fe3+ and ~1.31 mm/s for Fe2+ across all models and line shapes, which is expected because the Fe3+ has an additional shielding 3d electron. Values for θM data are nearly identical for Fe3+ in nontronite and Fe-smectite (~450 K), somewhat varied for Fe3+ in glauconite and biotite (θM = ~730 K and ~615 K, respectively), and relatively distinct for Fe2+ (~350 K). Some values for θM and f could not be determined due to the non-monotonic behaviour of the fitted values for δ as a function of temperature. Values of f295 were 0.821–0.917 for Fe3+ and 0.662–0.743 for Fe2+, consistent with previous studies of the recoil-free fraction in micas and other silicates. Calculated scatter in δ, Δ, area and f values as a function of different line shapes and computer software was significantly reduced at lower temperatures. Sources of error in each of the calculated parameters are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".