Long- and Short-Range Constraints for the Structure Determination of Layered Silicates with Stacking Disorder
Bibliographic record
Abstract
Layered silicates have important applications as host materials, supports for catalysis, and zeolite precursors. However, their local structures are often challenging to establish due to disorder of the sheet assemblies. We present a new protocol that combines long- and short-range structural constraints from diffraction and solid-state NMR techniques, respectively, to determine the molecular structure of layered silicates in the presence of various extents of stacking disorder. Solid-state 29 Si NMR data are largely insensitive to the incomplete extent of three-dimensional (3D) crystallinity that limits the interpretation of diffraction data alone to the identification of possible unit cells and space groups. State-of-the-art NMR crystallography techniques consequently provide a simplified view of materials from which candidate framework structures can be built and evaluated based on local structural constraints, including interatomic distances, Si site numbers and multiplicities, and Si–O–Si connectivities, and refined using density functional theory. This protocol was applied to a new layered silicate material named CLS-1, of composition [Si 5 O 11 H][C 9 N 2 H 15 ]·1.9(H 2 O), synthesized by using a fluoride-based protocol and cationic alkylaminopyridinium as a structure-directing agent (SDA). Despite the intrinsic complexity and partial ordering of the intersheet arrangements and organic–inorganic interactions, this led to the identification of a single space group that is compatible with both NMR and diffraction data, from which the silicate framework structure could be established,. The remarkable similarities between the layered framework structures of CLS-1, HUS-2 (Tsunoji et al. J. Mater. Chem. 2012, 22, 13682), and another layered silicate material with a radically different morphology and extent of stacking order and interlayer dynamics, established by using a similar approach (Brouwer et al. J. Am. Chem. Soc. 2013, 135, 5641), point to the remarkable robustness of this previously unknown silicate framework type.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".