Optical and Catalytic Properties of Nanozymes for Colorimetric Biosensors: Advantages, Limitations, and Perspectives
Bibliographic record
Abstract
Abstract Detection of colorimetric signals is commonly used in various analytical methods and for testing in non‐laboratory and resource‐limited settings. The performance of colorimetric assays is largely based on nanoparticles and their unique optical properties. Multifunctional nanoparticles combining optical and enzyme‐like catalytic properties—known as nanozymes—hold great promise for analytical applications as signal‐generating labels. However, the extensive focus on the catalytic properties leaves their unique optical properties overlooked. In this article, the use of the optical and catalytic properties of nanozymes is reviewed for analytical applications relying on the inherent optical properties of nanozymes, the colorimetric detection of a catalytically‐formed product, and colorimetric changes of nanoparticles caused by the catalytically‐formed product. The impact of the extinction coefficient of nanozymes and reaction products, as well as the kinetic parameters of nanozymes on the sensitivity and limit of detection of assays, are quantitatively evaluated. Finally, the existing limitations and prospects of nanozymes for colorimetric biosensors are summarized.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".