Optimization, Standardization, and Testing of a New NMR Method for the Determination of Zeolite Host−Organic Guest Crystal Structures
Bibliographic record
Abstract
An optimized and automated protocol for determining the location of guest sorbate molecules in highly siliceous zeolites from (29)Si INADEQUATE and (1)H/(29)Si cross polarization (CP) magic-angle spinning (MAS) NMR experiments is described. With the peaks in the (29)Si MAS NMR spectrum assigned to the unique Si sites in the zeolite framework by a 2D (29)Si INADEQUATE experiment, the location of the sorbate molecule is found by systematically searching for sorbate locations for which the measured rates of (1)H/(29)Si cross polarization of the different Si sites correlate linearly with (1)H/(29)Si second moments calculated from H-Si distances. Due to the (1)H/(29)Si cross polarization being in the "slow CP regime" for many zeolite-sorbate complexes, it is proposed that the CP rate constants are best measured by (1)H/(29)Si cross polarization drain experiments, if possible, to avoid complications that may arise from fast (1)H and (29)Si T(1)rho relaxations. An algorithm for determining the sorbate molecule location is described in detail. A number of ways to effectively summarize and display the large number of solutions which typically result from a prediction of the structure from the CP MAS NMR data are presented, including estimates of the errors involved in the structure determinations. As a working example throughout this paper, the structure of the low loaded p-dichlorobenzene/ZSM-5 complex is determined under different conditions from solid-state (1)H/(29)Si CP MAS NMR data, and the solutions are shown to be in excellent agreement with the known single-crystal X-ray diffraction structure. This structure determination approach is shown to be quite insensitive to the use of relative rate constants rather than absolute values, to the detailed structure of the zeolite framework, and relatively insensitive to temperature and motions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".