A Local Structure Analysis of Defects in UiO-66: Insights from Solid-State Nuclear Magnetic Resonance and X-ray Absorption Fine Structure
Bibliographic record
Abstract
Defect engineering in metal–organic frameworks (MOFs) offers a promising approach to modify material properties by introducing controlled structural imperfections. Zr-based MOFs, particularly the well-known UiO-66, hold significant potential for diverse applications. Defects in UiO-66 can be generated using monocarboxylic acids as modulators, among other methods. However, resolving the atomic-level local structures of these defects remains a considerable challenge. In this study, the local structures of these defects are carefully characterized by multinuclear solid-state NMR spectroscopy (SSNMR) in combination with X-ray absorption fine structure (XAFS). In situ heating XAFS analyses at Zr K-edge reveal critical changes in the local structure of Zr during the removal of trifluoroacetic acid (TFA), including the decreased Zr–O coordination numbers and alterations in Zr–Zr bond distances. Multinuclear 1 H, 13 C, 19 F, 35/37 Cl, 17 O solid-state NMR methods are used to identify capping species and defect-associated species. Subsequently, the engineered defects are found to significantly improve the catalytic performance of Pt nanoparticles (NPs) integrated into the defective UiO-66 framework. Pt-UiO-66 with defects exhibits much improved hydrogen evolution reaction (HER) activity and stability compared to the Pt-UiO-66 without defects.
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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".