Apical Anchoring and Cofactor Customizing To Achieve Ultrahigh Active Nanoenzymes for Removing O<sub>2</sub> Interference in Glucose Electro-oxidation
Bibliographic record
Abstract
Noble metal nanozymes (NMs) are promising alternatives to the fragile glucose oxidase (GOD), however, in all previously reported NMs, O 2 consumes electrons from glucose by competing with electrodes, which remarkably limits the Faraday efficiency. The low NM utilization rate and sluggish mass transfer severely limit the electrocatalytic activity. Herein, we report the first Au nanozyme that can catalyze glucose electro-oxidation (GEO) via a distinctive O 2 immune pathway with record-breaking mass activity based on apical anchoring and cofactor customization. Strategically, we design a cofactor of lipoic acid (ALA) that can accept electrons from glucose in preference to O 2 for Au nanoparticles, and anchor AuNPs/ALA on top of sheared hydrophilic carbon nanotubes ( T -SCNT/AuNPs/ALA). Mechanistically, ALA has highly reversible redox activity, and its reduction state is insensitive to O 2, thus, it can mediate direct electron transfer between the electrode and AuNPs. In addition, compared to CNT/AuNPs, T -SCNT/AuNPs/ALA has a larger electrochemical surface area, lower charge transfer resistance, and superior hydrophilicity, which are favorable for improving the reaction rate and efficiency. Notably, this strategy can be used to design bilirubin oxidase mimics ( T -SCNT/AuNPs/rutin), whose oxygen reduction activity significantly surpasses that of bilirubin oxidase. Consequently, compared to CNT/AuNPs, T -SCNT/AuNPs/ALA boosts the Faraday efficiency of GEO from 50% to 98%, shows a 755-fold increase in mass activity, and enables glucose biofuel cells to offer a 118-fold increase in power density. To the best of our knowledge, this is the first study to achieve a non-O 2 -interference GEO nanozyme by synergistically regulating the cofactor and catalytic interface and will guide the engineering of demand-specific electrocatalysts.
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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".