Hybrid enzymatic and nanozymatic biofuel cells for wearable and implantable biosensors
Bibliographic record
Abstract
The increasing demand for wearable and implantable devices presents unprecedented opportunities for advancing self-powered systems (SPSs). Enzymatic biofuel cells (BFCs), which harvest energy through biochemical reactions, hold great potential in SPSs. However, their practical applications are constrained by challenges, including low power output and limited long-term stability. Integrating the advantages of enzymes with nanomaterials , hybrid enzymatic BFCs achieve improved electron transfer efficiency, operational stability, and mechanical flexibility. Nanozymes , as nanomaterial-based artificial enzymes, provide promising approaches to these limitations with their lost cost, high stability, and tunable properties. This review highlights recent research advances in hybrid enzymatic and nanozymatic BFCs for wearable and implantable biosensors, including applications in detecting small molecules, biomacromolecules, cells, and systems integrating diagnosis and treatment. Specifically, the advantage of nanomaterials in signal amplification strategies for SPSs is emphasized. Finally, a personal perspective on challenges and future opportunities for advancing BFCs in wearable and implantable biosensors is discussed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".