Ionic salt cocrystals studied via multinuclear solid-state magnetic resonance: a case study of lithium 4-methoxybenzoate:L-proline polymorphs
Bibliographic record
Abstract
Lithium salts continue to find pharmaceutical applications, particularly as psychiatric medications. As with any active pharmaceutical ingredient, structural polymorphism is an important concern for lithium-based medications that can influence solubility and other physicochemical properties. Here we report a 13C, 1H, and 7Li magic-angle spinning solid-state nuclear magnetic resonance (MAS SSNMR) study of two 1:1 polymorphic ionic cocrystals of lithium 4-methoxybenzoate and L-proline (L4MPRO(α) and L4MPRO(β)). One-dimensional 13C cross-polarization MAS and two-dimensional heteronuclear correlation NMR spectra hint at differential mobilities of the proline and benzoate moieties for the two polymorphs. Five key resonances differ in 13C chemical shift by more than 1 ppm between the two polymorphs, clearly distinguishing between them. Gauge-including projector-augmented-wave density functional theory calculations of 13C and 1H magnetic shielding constants correlate strongly with the experimental chemical shifts for both polymorphs. R2 and root-mean-square deviation metrics are shown to be insufficient in the case of 13C, but sufficient in the case of 1H, for differentiating between the polymorphs. 7Li satellite-transition MAS NMR of both polymorphs are identical, as are the computed lithium magnetic shielding constants, demonstrating the insensitivity of 7Li NMR to polymorphism in these samples. This work highlights the utility of solid-state NMR spectroscopy for examining ionic salt cocrystals and also highlights some caveats in this regard.
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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".