The enzyme-like catalytic activity of cerium oxide nanoparticles and its dependency on Ce<sup>3+</sup> surface area concentration
Bibliographic record
Abstract
Cerium oxide nanoparticles are known to catalyze the decomposition of reactive oxygen species such as the superoxide radical and hydrogen peroxide. Herein, we examine the superoxide dismutase (SOD) and catalase (CAT) mimetic catalytic activities of nanoceria and demonstrate the existence of generic behaviors. For particles of sizes 4.5, 7.8, 23 and 28 nm, the SOD and CAT catalytic activities exhibit the characteristic shape of a Langmuir isotherm as a function of cerium concentration. The results show that the catalytic effects are enhanced for smaller particles and for the particles with the largest Ce3+ fraction. The SOD-like activity obtained from the different samples is found to superimpose on a single master curve using the Ce3+ surface area concentration as a new variable, indicating the existence of particle independent redox mechanisms. For the CAT assays, the adsorption of H2O2 molecules at the particle surface modulates the efficacy of the decomposition process and must be taken into account. We design an amperometry-based experiment to evaluate the H2O2 adsorption at nanoceria surfaces, leading to the renormalization of the particle specific area. Depending on the particle type the amount of adsorbed H2O2 molecules varies from 2 to 20 nm-2. The proposed scalings are predictive and allow the determination of the SOD and CAT catalytic properties of cerium oxide solely from physicochemical features.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".