Surface Ligand Engineering Ruthenium Nanozyme Superior to Horseradish Peroxidase for Enhanced Immunoassay
Bibliographic record
Abstract
Abstract Nanozymes have great potential to be used as an alternative to natural enzymes in a variety of fields. However, low catalytic activity compared with natural enzymes limits their practical use. It is still challenging to design nanozymes comparable to their natural counterparts in terms of the specific activity. In this study, a surface engineering strategy is employed to improve the specific activity of Ru nanozymes using charge‐transferrable ligands such as polystyrene sulfonate (PSS). PSS‐modified Ru nanozyme exhibits a peroxidase‐like specific activity of up to 2820 U mg−1, which is twice that of horseradish peroxidase (1305 U mg−1). Mechanism studies suggest that PSS readily accepts negative charge from Ru, thus reducing the affinity between Ru and ·OH. Importantly, the modified Ru‐peroxidase nanozyme is successfully used to develop an immunoassay for human alpha‐fetoprotein and achieves a 140‐fold increase in detection sensitivity compared with traditional horseradish‐peroxidase‐based enzyme‐linked immunosorbent assay. Therefore, this work provides a feasible route to design nanozymes with high specific activity that meets the practical use as an alternative to natural enzymes.
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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 | 0.001 | 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".