Application of Solid-State <sup>209</sup>Bi NMR to the Structural Characterization of Bismuth-Containing Materials
Bibliographic record
Abstract
Herein, we report the first detailed study of (209)Bi solid-state NMR (SSNMR) spectroscopy of extremely broad central transition powder patterns. (209)Bi ultrawideline SSNMR spectra of several bismuth-containing materials (bismuth oxyhalides, bismuth nitrate pentahydrate, nonaaquabismuth triflate, and bismuth acetate) were acquired at field strengths of 9.4 and 21.1 T using frequency-stepped techniques. The (209)Bi SSNMR experiments at 9.4 T yield powder patterns with breadths ranging from 0.9 to 14.6 MHz, from which quadrupolar coupling constants, C(Q)((209)Bi), between 78 and 256 MHz, were extracted via analytical simulations. The breadths of the quadrupolar-dominated spectra and overall experimental times are greatly reduced for experiments conducted at 21.1 T, which yield high signal-to-noise spectra in which the smaller effects of bismuth chemical shift anisotropy can be clearly observed. The (209)Bi electric field gradient (EFG) and chemical shift (CS) tensor parameters extracted from these spectra are correlated to the molecular structures at the bismuth sites, via first principles calculations of (209)Bi EFG and CS tensors performed using CASTEP for periodic solids and Gaussian 03 for molecular clusters. The rapidity with which (209)Bi SSNMR spectra can be acquired at ultrahigh fields, the sensitivity of the (209)Bi NMR parameters to the bismuth environment, and the predictive power of theoretically calculated NMR interaction tensors suggest that (209)Bi SSNMR may be useful for the characterization of a variety of Bi-containing materials and compounds.
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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 | 0.000 | 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) | 0.001 | 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".