Reversibly Compressible Silanated Cellulose Nanofibril Aerogel for Triboelectric Taekwondo Scoring Sensors
Bibliographic record
Abstract
Abstract The integration of bio‐based materials into triboelectric nanogenerators (TENGs) for energy harvesting from human body motions has sparked considerable research attention. Here, a silanated cellulose nanofibril (SCNF) aerogel is reported for structurally reliable TENGs and reversely compressible Taekwondo scoring sensors under repeated impacts. The preparation of the aerogel involves silanizing cellulose nanofibers (CNFs) with vinyltrimethoxysilane (VTMS), following by freeze–drying and post‐heating treatment. The SCNF aerogel with crosslinked physico‐chemical bonding and highly porous network is found to exhibit superior mechanical strength and reversible compressibility as well as enhanced water repellency and electron‐donating ability. The TENG having a tribo‐positive SCNF layer exhibits exceptional triboelectric performances, generating a voltage of 270 V, current of 11 µA, and power density of 401.1 mW m −2 under an applied force of 8 N at a frequency of 5 Hz. With its inherent merits in material composition, structural configuration, and device sensitivity, the SCNF TENG demonstrates the capability to seamlessly integrate into a Taekwondo protection gear, serving as an efficient self‐powered sensor for monitoring hitting scores. This study highlights the significant potential of a facilely fabricated SCNF aerogel for the development of high‐performance, bio‐friendly, and cost‐effective Bio‐TENGs, enabling their application as self‐powered wearable devices and sports engineering sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".