Iron oxide nanozyme catalyzed synthesis of fluorescent polydopamine for light-up Zn<sup>2+</sup>detection
Bibliographic record
Abstract
Fluorescent polydopamine (FPD) is an interesting material with excellent biocompatibility. However, its preparation is currently a lengthy and potentially dangerous process. We herein employ magnetic iron oxide (Fe3O4) nanoparticles as a peroxidase-mimicking nanozyme to produce FPD under mild conditions. Different from previous protocols using multiple steps with up to 6% (∼2 M) H2O2, this preparation takes place in a single step with just 5 mM H2O2 at room temperature. The oxidized product shows excitation-wavelength-dependent emission peaks, similar to previous reports. The reaction kinetics, pH, temperature, and ionic strength are individually optimized. Among a diverse range of other nanomaterials tested, including Fe2O3, CeO2, CoO, Co3O4, NiO, TiO2, gold nanoparticles, and graphene oxide, Fe2O3 and graphene oxide yielded relatively weak emission, while the rest of the materials failed to produce FPD. The Fe3O4 nanoparticles retained ∼90% catalytic activity even after ten cycles of synthesis. Finally, Zn(2+) can enhance the fluorescence of FPD under 360 nm excitation but not under 480 nm excitation, leading to a sensitive light-up sensor with a detection limit of 60 nM Zn(2+). Therefore, this work has demonstrated not only a novel use of nanozymes, but also an interesting application of FPD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".