Identification of Nonequivalent Framework Oxygen Species in Metal–Organic Frameworks by <sup>17</sup>O Solid-State NMR
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) are a class of novel nanoporous materials with many potential applications. Structural characterization is important because understanding the relationship between the properties of these industrially relevant materials and their structures allows one to develop new applications and improve current performance. Oxygen is one of the most important elements in many MOFs and exists in various forms. Ideally, 17 O solid-state NMR (SSNMR) should be an excellent tool for characterizing various oxygen species. However, the major obstacles that prevent applying 17 O SSNMR to MOF characterization are the synthetic effort needed for 17 O isotopic enrichment and the associated high cost. In this work, we successfully prepared several prototypical 17 O-enriched MOFs, including Zr-UiO-66, MIL-53(Al), CPO-27-Mg (or Mg-MOF-74), and microporous α-Mg 3 (HCOO) 6 . Depending on the target MOF, different isotopic enrichment methods were used to effectively incorporate 17 O from 17 O-enriched H 2 O. Using these 17 O-enriched MOFs, we were able to acquire 17 O SSNMR spectra at a magnetic field of 21.1 T. They provide distinct spectral signatures of various key oxygen species commonly seen in representative MOFs. We demonstrate that 17 O SSNMR can be used to differentiate chemically and, under favorite circumstances, crystallographically nonequivalent oxygens and to follow the phase transitions. The synthetic approaches for preparation of 17 O-enriched sample described in this paper are fairly simple and cost-effective.
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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".