Directly Probing the Metal Center Environment in Layered Zirconium Phosphates by Solid-State<sup>91</sup>Zr NMR
Bibliographic record
Abstract
Layered zirconium phosphates (ZrPs) and their derivatives potentially have many important applications. In the past, these materials have been mainly characterized by X-ray diffraction and 31 P MAS NMR. Their metal centers have not been directly probed by solid-state NMR spectroscopy. In this work, we present solid-state 91 Zr NMR spectra acquired at several magnetic fields for several representative layered zirconium phosphates including α-Zr(HPO 4 ) 2 ·H 2 O, γ-Zr(PO 4 )(H 2 PO 4 )·2H 2 O, Zr(NH 4 PO 4 ) 2 ·H 2 O, and Zr 2 (NaPO 4 ) 4 ·6H 2 O. The NMR interaction tensors were extracted from the spectra. The results indicate that the 91 Zr spectra are sensitive not only to the relatively small distortion in ZrO 6 polyhedron but also to the difference in the geometry of Zr(OP) 6 units in these materials. We show that 91 Zr quadrupolar coupling constants ( C Q ) correlate well with several angular distortion parameters reflecting the deviation from a perfect ZrO 6 octahedron such as distortion index, shear strain, and mean O−Zr−O angle. The relationships between C Q and structural parameters related to the Zr(OP) 6 unit including the mean Zr−P distance and Zr−O−P angle also appear to exist. The theoretical calculations at both restricted Hartree−Fock and density functional levels were performed on model clusters to establish the relationships of various structural parameters with 91 Zr EFG tensors, and the calculation results are consistent with the empirical correlations. For the related layered zirconium phosphates whose structures are unknown or poorly described, we have shown that 91 Zr NMR can be used to directly obtain structural information on the local environment around the metal centers, which is complementary to that obtained from powder XRD and 31 P MAS NMR, as demonstrated by a novel meso-lamellar ZrP as an example.
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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".