Rietveld refinement and NMR crystallographic investigations of multicomponent crystals containing alkali metal chlorides and urea
Bibliographic record
Abstract
New mechanochemical preparations of three multicomponent crystals (MCCs) of the form M Cl:urea· x H 2 O ( M = Li, Na and Cs) are reported. Their structures were determined by an NMR crystallography approach, combining Rietveld refinement of synchrotron powder X-ray diffraction data (PXRD), multinuclear ( 35 Cl, 7 Li, 23 Na and 133 Cs) solid-state NMR (SSNMR) spectroscopy and thermal analysis. The mechanochemical syntheses of the three MCCs, two of which are novel, were optimized for maximum yield and efficiency. 35 Cl SSNMR is well suited for the structural characterization of these MCCs since it is sensitive to subtle differences and/or changes in chloride ion environments, providing a powerful means of examining H...Cl − bonding environments. Alkali metal NMR is beneficial for identifying the number of unique magnetically and crystallographically distinct sites and enables facile detection of educts and/or impurities. In the case of NaCl:urea·H 2 O, 23 Na magic-angle spinning NMR spectra are key, both for identifying residual NaCl educt and for monitoring NaCl:urea·H 2 O degradation, which appears to proceed via an autocatalytic decomposition process driven by water (with a rate constant of k = 1.22 × 10 −3 s −1 ). SSNMR and PXRD were used to inform the initial structural models. Following Rietveld refinement, the models were subjected to dispersion-corrected plane-wave density functional theory geometry optimizations and subsequent calculations of the 35 Cl electric field gradient tensors, which enable the refinement of hydrogen-atom positions, as well as the exploration of their relationships to the local hydrogen-bonding environments of the chloride ions and crystallographic symmetry elements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".