Construction of a Cascade Catalyst of Nanocoupled Living Red Blood Cells for Implantable Biofuel Cell
Bibliographic record
Abstract
The broad applications of implantable glucose biofuel cells (GBFCs) have become very attractive in biomedical sciences. The key challenge of GBFCs is eliminating the inevitable product H 2 O 2 generated from the oxidation of glucose when glucose oxidase (GOx) is used as a catalyst while improving the performance of GBFCs. In this work, the cascade electrocatalyst, RBCs@NPDA was obtained through the in situ polymerization of dopamine to form nanopolydopamine (NPDA) on the surface of red blood cells (RBCs). The RBCs@NPDA can catalyze both fuels of H 2 O 2 and O 2, so as to generate a high cathodic current (0.414 mA cm –2 ). Furthermore, when RBCs@NPDA was used as a cathodic catalyst in the membraneless GBFC, it exhibited the cascade catalytic activity in the reduction of O 2 –H 2 O 2 and minimized the damage to RBCs caused by the high concentration of H 2 O 2 . The mechanism research indicates that RBCs@NPDA integrates the property of NPDA and RBCs. Specifically, NPDA plays a catalase-like role in catalyzing the decomposition of H 2 O 2, while RBCs play a laccase-like role in electrocatalyzing the O 2 reduction reaction. This work offers the cascade catalyst for improving the performance of implantable GBFC and presents a strategy for constructing catalysts using living cells and nanomaterials to replace deformable and unstable enzymes in other biofuel cells.
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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".