Molecularly Imprinted Nanozymes with Substrate Specificity: Current Strategies and Future Direction
Bibliographic record
Abstract
Molecular imprinting technology (MIT) stands out for its exceptional simplicity and customization capabilities and has been widely employed in creating artificial antibodies that can precisely recognize and efficiently capture target molecules. Concurrently, nanozymes have emerged as promising enzyme mimics in the biomedical field, characterized by their remarkable stability, ease of production scalability, robust catalytic activity, and high tunability. Drawing inspiration from natural enzymes, molecularly imprinted nanozymes combine the unique benefits of both MIT and nanozymes, thereby conferring biomimetic catalysts with substrate specificity and catalytic selectivity. In this review, the latest strategies for the fabrication of molecularly imprinted nanozymes, focusing on the use of organic polymers and inorganic nanomaterials are explored. Additionally, cutting-edge techniques for generating atom-layer-imprinted islands with ultra-thin atomic-scale thickness is summarized. Their applications are particularly noteworthy in the fields of catalyst optimization, detection techniques, and therapeutic strategies, where they boost reaction selectivity and efficiency, enable precise identification and quantification of target substances, and enhance therapeutic effectiveness while minimizing adverse effects. Lastly, the prevailing challenges in the field and delineate potential avenues for future progress is encapsulated. This review will foster advancements in artificial enzyme technology and expand its applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".