Fluoride-capped nanoceria as a highly efficient oxidase-mimicking nanozyme: inhibiting product adsorption and increasing oxygen vacancies
Bibliographic record
Abstract
Nanozymes aim to mimic enzyme activities using nanomaterials. Nanoceria (CeO2 nanoparticles) is an important model nanozyme for its rich redox chemistry. In particular, its oxidase-like activity allows oxidation reactions without the need of unstable and toxic H2O2. Fluoride can significantly improve its oxidase-like activity, and this work aims to understand the mechanism of fluoride-promoted catalysis. First, fluoride can adsorb on CeO2 tighter than other halides, but not as strong as phosphate as characterized by isothermal titration calorimetry (ITC). FT-IR spectroscopy indicates adsorption of fluoride likely via exchange with surface hydroxide groups. Fluoride capping inverses the surface charge of CeO2, facilitating desorption of the ABTS oxidation product, significantly increasing the turnover number. The Raman, EPR and XPS spectroscopy results demonstrate that the concentration of Ce3+ and the accompanying oxygen vacancy significantly increased upon adding F-, which can explain the enhanced catalytic activity. Finally, the electron transfer properties of fluoride-capped CeO2 were more efficient than that of the bare CeO2 as determined by a direct electrochemical measurement on a glass carbon electrode. This study has provided new insight into nanoceria, and can also further confirm the role of nanoceria as a model for engineering the surface of nanozymes.
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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 | 0.001 | 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".