Synthesis and Characterization of Xylazine Hydrochloride Polymorphs, Hydrates, and Cocrystals: A <sup>35</sup>Cl Solid-State NMR and DFT Study
Bibliographic record
Abstract
Xylazine HCl (X) is a veterinary analgesic with many known solid forms, making it an ideal system for studying the noncovalent interactions, such as hydrogen bonding, that provide stability to polymorphs, solvates/hydrates, and cocrystal of pharmaceuticals. Herein, we report methods for the reliable preparation and interconversion of polymorphs of X (including mechanochemical pathways), the discovery of a novel polymorph, and the synthesis of three cocrystals with coformers containing amide and carboxylic acid moieties. An understanding of ball milling protocols is essential for optimizing these reactions and ensuring clean and reproducible syntheses of the products in high yields. All materials were characterized using thermal analysis, powder and single-crystal X-ray diffraction (PXRD and SCXRD), and multinuclear solid-state NMR (SSNMR) spectroscopy. 35 Cl SSNMR is highlighted for its versatility for fingerprinting polymorphs, hydrates, and cocrystals (including the detection of impurity phases that are not always evident from PXRD and offering an avenue for optimizing synthetic protocols) and providing molecular-level structural information. The 35 Cl electric field gradient (EFG) tensor is extremely sensitive to the unique hydrogen-bonding network in each solid form of X, resulting in distinct powder patterns. Dispersion-corrected plane-wave density functional theory (DFT) structural refinements yield better models of the hydrogen-bonding environments of the chloride ions than is possible through XRD methods alone. Calculations employing the refined structures yield 35 Cl EFG tensors that agree well with experiment. PXRD and 35 Cl SSNMR, in tandem with reliable calculations of EFG tensors, are essential for the development of NMR crystallographic and crystal structure prediction protocols and crucial for future studies involving HCl salts and their concomitant solid forms.
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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".