Biomedical Applications of Nanozymes: An Enzymology Perspective
Bibliographic record
Abstract
Abstract Nanozymes are catalytic nanomaterials that transform enzyme substrates into their corresponding products, offering enhanced stability and a cost‐effective alternative to traditional enzymes. As nanomaterials, they possess unique physicochemical properties and catalytic mechanisms distinct from those of enzymes. Such differences have profound, yet often neglected, implications in biomedical applications. In the context of enzymology, this review compares nanozymes and enzymes, with a focus on redox reactions. This review begins with the classification of nanozymes based on the types of reactions they catalyze, with the ability to exhibit multiple catalytic activities being a prevalent characteristic. The use of the Michaelis‐Menten model for both enzymes and nanozymes is discussed in detail, and the Michaelis constant, maximum reaction rate, and turnover number values are compared. The performance of nanozymes in crowded environments and under extreme conditions is also compared to that of enzymes. We discuss the kinetic factors influencing nanozyme performance, the impact of active site shielding, and the activity under non‐physiological conditions. We then compiled recent trends in the biomedical applications of nanozymes, focusing on both the production and scavenging of reactive oxygen species. This review links fundamental enzymology to nanozyme catalysis, providing a key reference for the rational use of nanozymes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".