Highly Porous, Ultralight, Biocompatible Silk Fibroin Aerogel-Based Triboelectric Nanogenerator
Bibliographic record
Abstract
This study presents the fabrication of an ultralight, porous, and high-performance triboelectric nanogenerator (TENG) utilizing silk fibroin (SF) aerogels and PDMS sponges as the friction layer. The transition from two-dimensional film friction layers to three-dimensional porous aerogels significantly increased the specific surface area, offering an effective strategy for designing high-performance SF aerogel-based TENGs. The TENG incorporating the porous SF aerogel exhibited optimal output performance at a 3% SF concentration, achieving a maximum open circuit voltage of 365 V, a maximum short-circuit current of 11.8 μA, and a maximum power density of 7.52 W/m 2 . In comparison to SF-film-based TENGs, the SF-aerogel based TENG demonstrated a remarkable 6.5-fold increase in voltage and a 4.5-fold increase in current. Furthermore, the power density of our SF-based TENG surpassed the previously reported optimal values for SF-based TENGs by 2.4 times. Leveraging the excellent mechanical stability and biocompatibility of TENGs, we developed an SF-based TENG self-powered sensor for the real-time monitoring of subtle biological movements. The SF-based TENG exhibits promising potential as a wearable bioelectronic device for health monitoring.
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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".