Heterogeneities in sol–gel-derived paramagnetics-doped forsterites and willemites — Electron microprobe analysis and stretched-exponential <sup>29</sup>Si MAS NMR spin–lattice relaxation studies
Bibliographic record
Abstract
We report the synthesis and analysis of sol–gel-derived samples of forsterite (Mg 2 SiO 4 ) and willemite (Zn 2 SiO 4 ), doped with paramagnetic Cu 2+ , Ni 2+ , and Co 2+ , at a range of dopant concentrations. Electron probe microanalysis and backscattered electron imaging show the presence of major micrometre-scale heterogeneities in the distribution of paramagnetic centres. Despite the inhomogeneities, the 29 Si NMR spin–lattice relaxation behaviour is well-behaved and is consistent with the stretched-exponential expression M z (t) = M z (∞){1 – a exp[–(t/T′) n ]}. The exponent n is 0.5 within the experimental error in some samples. This value is consistent with relaxation by immobile isolated paramagnetic impurities with negligible 29 Si spin diffusion from the impurity centres, but careful curve fitting confirms that n is significantly larger than 0.5 in other samples. Relaxation efficiency is highly dependent on the dopant ion and its concentration. Although the purely empirical stretched-exponential function does not provide a unique physical picture, it is noteworthy that it is sufficiently robust to describe spin–lattice relaxation even in highly inhomogeneous systems. Spin–lattice relaxation is a useful probe of paramagnetics-doped solid samples, but NMR does not provide information on homogeneity. Careful sample characterization on the micrometre scale is highly desirable, as a complement to NMR studies.Key words: MAS NMR, spin–lattice relaxation, 29 Si, forsterite, willemite, stretched-exponential relaxation, sol–gel, minor-component heterogeneity, backscattered electron analysis.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".