Bibliographic record
Abstract
Nanoparticles and proteins are similar in a number of aspects, and using nanoparticles to mimic the catalytic function of enzymes is an interesting yet challenging task. Impressive developments have been made over the past two decades on this front. The term nanozyme was coined to refer to nanoparticlebased enzyme mimics. To date, many different types of nanozymes have been reported to catalyze a broad range of reactions for chemical, analytical, and biomedical applications. Since chemical reactions happen mainly on the surface of nanozymes, an interesting aspect for investigation is surface modification. In this review, we summarize three types of nanozyme materials catalyzing various reactions with a focus on their surface chemistry. For metal oxides, cerium oxide and iron oxide are discussed as they are the most extensively studied. Then, gold nanoparticles and graphene oxide are reviewed to represent metallic and carbon nanomaterials, respectively. Types of modifications include ions, small molecules, and polymers mainly by physisorption, while in a few cases, covalent modifications were also employed. The functional aspect of such modification is to improve catalytic activity, substrate specificity, and stability. Future perspectives of this field are speculated at the end of this review.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".