In Situ Multinuclear Magic-Angle Spinning NMR: Monitoring Crystallization of Molecular Sieve AlPO<sub>4</sub>-11 in Real Time
Bibliographic record
Abstract
Molecular sieves are crystalline three-dimensional frameworks with well-defined channels and cavities. They have been widely used in industry for many applications such as gas separation/purification, ion exchange, and catalysis. Obviously, understanding the formation mechanisms is fundamentally important. High-resolution solid-state NMR spectroscopy is a powerful method for the study of molecular sieves. However, due to technical challenges, the vast majority of the high-resolution solid-state NMR studies on molecular sieve crystallization are ex situ. In the present work, using a new commercially available NMR rotor that can withhold high pressure and high temperature, we examined the formation of molecular sieve AlPO4-11 under dry gel conversion conditions by in situ multinuclear (1H, 27Al, 31P, and 13C) magic-angle spinning (MAS) solid-state NMR. In situ high-resolution NMR spectra obtained as a function of heating time provide much insights underlying the crystallization mechanism of AlPO4-11. Specifically, in situ 27Al and 31P MAS NMR along with 1H → 31P cross-polarization (CP) MAS NMR were used to monitor the evolution of the local environments of framework Al and P, in situ 1H → 13C CP MAS NMR to follow the behavior of the organic structure directing agent, and in situ 1H MAS NMR to unveil the effect of water content on crystallization kinetics. The in situ MAS NMR results lead to a better understanding of the formation of AlPO4-11.
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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".