Nanozyme's catching up: activity, specificity, reaction conditions and reaction types
Bibliographic record
Abstract
Nanozymes aim to mimic enzyme activities. In addition to catalytic activity, nanozymes also need to have specificity and catalyze biologically relevant reactions under physiological conditions to fit in the definition of enzyme and to set nanozymes apart from typical inorganic catalysts. Previous discussions in the nanozyme field mainly focused on the types of reactions or certain analytical, biomedical or environmental applications. In this article, we discuss efforts made to mimic enzymes. First, the catalytic cycles are compared, where a key difference is specific substrate binding by enzymes versus non-specific substrate adsorption by nanozymes. We then reviewed efforts to engineer and surface-modify nanomaterials to accelerate reaction rates, strategies to graft affinity ligands and molecularly imprinted polymers to achieve specific catalysis, and methods to bring nanozyme reactions to neutral pH and ambient temperature. Most of the current nanozyme reactions used a few model chromogenic substrates of no biological relevance. Therefore, we also reviewed efforts to catalyze the conversion of biomolecules and biopolymers using nanozymes. By the efforts to close the gaps between nanozymes and enzymes, we believe nanozymes are catching up rapidly. Still, challenges exist in materials design to further improve nanozymes as true enzyme mimics and achieve impactful 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".