Kinetic Profiling of Oxidoreductase-Mimicking Nanozymes: Impact of Multiple Activities, Chemical Transformations, and Colloidal Stability
Bibliographic record
Abstract
In contrast to homogeneous enzyme catalysis, nanozymes are nanosized heterogeneous catalysts that perform reactions on a rigid surface. This fundamental difference between enzymes and nanozymes is often overlooked in kinetic studies and practical applications. In this article, using 14 nanozymes of various compositions (core@shell, metal–organic frameworks, metal, and metal oxide nanoparticles), we systematically demonstrate that nontypical features of nanozymes, such as multiple catalytic activities, chemical transformations, and aggregation, need to be considered in nanozyme catalysis. Ignoring these features results in the inaccurate quantification of catalytic activity. Neglecting the multiple activities led to a six-time underestimation of Mn 2 O 3 oxidation activity and mischaracterization of this material as a low-active peroxidase-mimicking nanozyme. Additionally, overlooking chemical stability during catalytic reactions led to the reporting of high peroxidase-mimicking activity for Au@Ag nanoparticles, which, in reality, exhibited no intrinsic activity and oxidized the substrate through the leakage of Ag + ions. Ignoring the chemical stability of Au@Prussian Blue nanoparticles may lead to more than four times overestimation of peroxidase-mimicking activity after just 24 h of storage. Finally, disregarding the colloidal stability of nanozymes led to a five-time inaccuracy in catalytic activity. These findings underscore the necessity of optimizing procedures to account for these factors in nanozyme kinetic measurements, which will in turn ensure more reliable biosensors and the success of other practical 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".