An Electron Transfer Regulation Strategy to Enhance the Catalytic Activity of Perovskite Fluorescent Nanozyme by Incorporation of Fe Metal‐organic Framework for Biomimetic Cascade Catalysis
Bibliographic record
Abstract
Abstract Fluorescent nanozymes allow the use of both fluorescence and catalytic functions for theragnostic and bioanalytical applications. However, few fluorescent nanozymes with both high fluorescence yield and high catalytic activities are available. CsPbX 3 perovskite nanocrystals (PNCs) have strong fluorescence but very weak catalytic activities. Here, a microenvironment electron transfer regulation strategy is proposed to enhance the nanozymatic activities and stability of PNCs through the incorporation of Fe metal‐organic framework (MOF). By in suit growth of an amphiphilic polymer (octylamine‐modified polyacrylic acid) capped PNCs on MOF, the oxidase (OXD)‐like activity of PNCs is enhanced sevenfold. X‐ray absorption near‐edge structure (XANES) and extended X‐ray absorption fine structure (EXAFS) characterizations revealed that forming the composition results in valence state shifts of the Fe atoms, indicating an electron transfer from PNCs to MOF enabled by the built‐in electric field, which improved the catalytic activities. Moreover, the nanocomposite also displays peroxidase (POD)‐like activity, and its dual enzyme‐like catalytic mechanism and activities are studied experimentally and theoretically. At last, a nanozyme cascade catalytic system based on the dual mimic enzyme of PNCs@MOF is constructed for ratiometric fluorescence biosensing of ascorbic acid with high sensitivity. This work provides an attractive fluorescent nanozyme, greatly expanding its application in bioanalysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".