The Most Active Oxidase‐Mimicking Mn<sub>2</sub>O<sub>3</sub> Nanozyme for Biosensor Signal Generation
Bibliographic record
Abstract
Abstract Oxidase‐mimicking nanozymes are more desirable than peroxidase‐mimicking ones since H 2 O 2 can be omitted. However, only a few nanomaterials are known for oxidase‐like activities. In this work, we compared the activity of Mn 2 O 3 , Mn 3 O 4 and MnO 2 and found that Mn 2 O 3 had the highest oxidase activity. Interestingly, the activity of Mn 2 O 3 was even inhibited by H 2 O 2 . The oxidase‐like activity of Mn 2 O 3 was not much affected by the presence of proteins such as bovine serum albumin (BSA), but the physisorption of antibodies to Mn 2 O 3 was not strong enough to withstand the displacement by BSA. We then treated Mn 2 O 3 with 3‐aminopropyltriethoxysilane to graft an amine group, which was used to conjugate antibodies using glutaraldehyde as a crosslinker. A one‐step indirect competitive ELISA (icELISA) was developed for the detection of isocarbophos, and an IC 50 of 261.7 ng/mL was obtained, comparable with the results of the standard two‐step assay using horseradish peroxidase (HRP)‐labeled antibodies. This assay has the advantage of significant timesaving for rapid detection of large amounts of samples. This work has discovered a highly efficient oxidase‐mimicking nanozyme useful for various nano‐ and analytical applications.
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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) | 0.001 | 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".