Dynamic Nuclear Polarization NMR Enables the Analysis of Sn-Beta Zeolite Prepared with Natural Abundance <sup>119</sup>Sn Precursors
Bibliographic record
Abstract
The catalytic activity of tin-containing zeolites, such as Sn-Beta, is critically dependent on the successful incorporation of the tin metal center into the zeolite framework. However, synchrotron-based techniques or solid-state nuclear magnetic resonance (ssNMR) of samples enriched with (119)Sn isotopes are the only reliable methods to verify framework incorporation. This work demonstrates, for the first time, the use of dynamic nuclear polarization (DNP) NMR for characterizing zeolites containing ~2 wt % of natural abundance Sn without the need for (119)Sn isotopic enrichment. The biradicals TOTAPOL, bTbK, bCTbK, and SPIROPOL functioned effectively as polarizing sources, and the solvent enabled proper transfer of spin polarization from the radical's unpaired electrons to the target nuclei. Using bCTbK led to an enhancement (ε) of 75, allowing the characterization of natural-abundance (119)Sn-Beta with excellent signal-to-noise ratios in <24 h. Without DNP, no (119)Sn resonances were detected after 10 days of continuous analysis.
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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) | 0.000 | 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 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".