Designing signal-on sensors by regulating nanozyme activity
Bibliographic record
Abstract
Nanozymes are nanomaterials with enzyme-like activities. Compared to natural enzymes, nanozymes are more stable and cost-effective, and they have unique properties due to their nanoscale size and surface chemistry. In this review, we summarize 'signal-on' nanozyme-based sensors for detecting metal ions, anions, small molecules and proteins. Since protein-based enzymes are already highly active, they were used to detect their inhibitors, resulting in 'signal-off' sensors. On the other hand, for nanozymes, target molecules were detected either as a promotor of nanozyme activity or for its ability to selectively remove nanozyme inhibitors. In both cases, 'signal-on' detection was achieved. We classify the commonly used nanozymes based on their composition such as metal oxide, gold nanoparticles and other nanomaterials, most of which belong to the oxidase, peroxidase and catalase mimics. The nanozymes can catalyze the oxidation of colorless or non-fluorescent substrates to produce a visual or fluorescent signal. Based on this, this article presents some typical 'turn-on' and 'turn-off-on' sensors, and we critically review their design principles. At the end, further perspectives for the nanozyme-based sensors are outlined.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".