Liposome‐Boosted Peroxidase‐Mimicking Nanozymes Breaking the pH Limit
Bibliographic record
Abstract
Abstract Peroxidase‐mimicking nanozymes such as Fe3O4 nanoparticles are promising substitutes for natural enzymes like horseradish peroxidase. However, most such nanozymes work efficiently only in acidic conditions. In this work, the influence of various liposomes on nanozyme activity was studied. By introducing negatively charged liposomes, peroxidase‐mimicking nanozymes achieved oxidation of 3,3′,5,5′‐tetramethylbenzidine (TMB) in neutral and even alkaline conditions, although the activity towards anionic 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS) was inhibited. The Fe3O4 nanoparticles adsorbed on the liposomes without disrupting membrane integrity as confirmed by fluorescence quenching, dye leakage assays, and cryo‐electron microscopy. Stabilization of the blue‐colored oxidized products of TMB by electrostatic interactions was believed to be the reason for the enhanced activity. This work has introduced lipids to nanozyme research, and it also has practically important applications for using nanozymes at neutral pH, such as the detection of hydrogen peroxide and glucose.
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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) | 0.001 | 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".