Solid-State <sup>91</sup>Zr NMR Characterization of Layered and Three-Dimensional Framework Zirconium Phosphates
Bibliographic record
Abstract
Layered and open framework zirconium phosphates (ZrPs) have many current and potential applications in the areas of catalysis, sorption, protonic conductors, solar energy storage, crystal engineering, and ion exchange. Characterization of ZrP-based materials is important because understanding the relationship between the properties of these materials and their structures is crucial for developing new uses and for improving their performances in current applications. However, local Zr environments in many ZrPs have not been characterized directly by 91 Zr solid-state NMR (SSNMR). This is because 91 Zn ( I = 5/2) is an unreceptive nucleus with many NMR unfavorable characteristics, leading to low sensitivity. In this work, the local environments of the zirconium centers in several ion-exchanged derivatives of layered α-ZrP (K + -, Li + -, Co(NH 3 ) 6 3+ -ZrP) have been probed directly using 91 Zr MAS, static quadrupolar echo, and/or quadrupolar Carr–Purcell–Meiboom–Gill NMR. Several layered and three-dimensional framework zirconium phosphates (ZrPO 4 -DES8, ZrPO 4 -DES1, ZrPO 4 -DES2, ZrPOF-pyr, ZrPOF-Q1, ZrPOF-EA, and ZrPOF-DEA) with novel structures were also examined. Theoretical calculations using the CASTEP and Gaussian model cluster approaches were also performed in order to provide insights into the observed spectra. In addition to 91 Zr SSNMR, 31 P, 13 C, and 19 F SSNMR spectroscopy was also utilized to characterize the above-mentioned materials.
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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".