Boosting the oxidase mimicking activity of nanoceria by fluoride capping: rivaling protein enzymes and ultrasensitive F<sup>−</sup>detection
Bibliographic record
Abstract
Nanomaterial-based enzyme mimics (nanozymes) are currently a new forefront of chemical research. However, the application of nanozymes is limited by their low catalytic activity and low turnover numbers. Cerium dioxide nanoparticles (nanoceria) are among the few with oxidase activity. Herein, we report an interesting finding addressing their limitations. The oxidase activity of nanoceria is improved by over 100-fold by fluoride capping, making it more close to real oxidases. The turnover number reached 700 in 15 min, drastically improved from ∼15 turnovers for the naked particles. The mechanism is attributed to surface charge modulation and facilitated electron transfer by F(-) capping based on ζ-potential and free radical measurements. Ultrasensitive sensing of fluoride was achieved with a detection limit of 0.64 μM F(-) in water and in toothpastes, while no other tested anions can achieve the activity enhancement.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".