Molecular-Level Characterization of Oxygen Local Environments in a Pristine and Post-Synthetically Modified Metal–Organic Framework via <sup>17</sup>O Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
Porous metal–organic frameworks (MOFs) have found many technological applications in fields such as carbon capture and storage, catalysis, and selective guest adsorption. Post-synthetic modification (PSM) approaches can influence MOF properties by introducing new functional groups or metals. MIL-121 is a prototypical aluminum MOF containing free uncoordinated carboxylic acid groups, which can act as adsorption sites for metal exchange as they are accessible to guests from within the pores. The introduction of metal species has been proven to enhance the gas adsorption capacity and catalytic properties of MIL-121. A deeper understanding of how MOF carboxylic acid groups interact with metals is imperative for the development of advanced industrially relevant materials. In this work, we demonstrate the remarkable capability of 17 O solid-state NMR at 35.2 and 19.6 T to assign each 17 O resonance in MIL-121 to its chemical/crystallographic oxygen site, provide site-specific structural information, and probe both the location and binding mode of metal guests within the framework. A series of 1D and 2D 17 O NMR experiments on 17 O-enriched MIL-121 supported by computational methods have been employed to study changes in the local environments of oxygen upon the activation of the material as well as upon metal loading. These results clearly show that the high spectral resolution achieved via high-field magic-angle spinning (MAS), REDOR (rotational-echo double-resonance), multiple-quantum MAS (MQMAS), and D-HMQC (dipolar heteronuclear multiple-quantum coherence) experiments yields unprecedented insight into this MOF. The 17 O NMR parameters provide molecular-level information regarding the local oxygen environment, intermolecular interactions, and host–guest connectivity. This experimental approach can be applied to a wide variety of oxygen-containing MOFs.
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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".