Bibliographic record
Abstract
Abstract CeO2 nanoparticles or nanoceria is a very interesting enzyme mimic (nanozyme) with a diverse range of catalytic activities. Most of the previous studies focused on its redox chemistry for mimicking oxidase, peroxidase, and catalase‐like activities, and as a scavenger of reactive oxygen species. Considering CeO2 has both Ce(III) and Ce(IV) species and both interact strongly with inorganic phosphate, nanoceria has also been studied for its phosphatase‐like activity. We herein compared these species along with alkaline phosphatase (ALP). First, CeO2 and Ce(IV) have good activity using p‐nitrophenylphosphate (p‐NPP) as a substrate, while other metal ions failed to show this activity. Since a reaction product is inorganic phosphate and we observed an interesting enzyme kinetic profile, the effect of phosphate was studied, which inhibited both CeO2 and ALP. The inhibition constant was about 5‐fold smaller for CeO2. The inhibition effect of polyphosphates was even stronger. Due to the product inhibition effect, CeO2 is unlikely to be a typical multiple turnover nanozyme, and the system is self‐limited. For the other anions, only F− showed a moderate inhibition effect on the cerium species, while adsorption of DNA did not inhibit the activity. Finally, heat treated ALP lost the activity, but CeO2 and Ce(IV) remained active. This study has deepened our understanding of CeO2 as a phosphatase mimicking nanozyme, which could be useful for biosensing and chemical biology applications.
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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".