Surface amination modification of cellulose hydrogels for enhancing triboelectric performance of extreme environment-resistant triboelectric sensors
Bibliographic record
Abstract
Multifunctional wearable flexible electronic devices based on hydrogels have received extensive research in recent years. Despite their promising applications, a significant challenge persists in terms of efficiently powering these devices. Triboelectric nanogenerators (TENGs) assembled by surface-modified hydrogels may be one of the promising strategies to address this challenge. This study presents the development of a multifunctional composite hydrogel, which is synthesized through the amino surface modification of glycerin-cellulose hydrogel (3-aminopropyltriethoxysilane-glycerin-cellulose, A-GC). The resulting composite hydrogel is utilized in the fabrication of electrodes of TENGs, which can effectively harvest mechanical energy to power flexible sensors. By using cellulose and glycerin as primary raw materials and 3-aminotriethoxysilane as surface modification components, the composite hydrogel exhibits excellent mechanical properties, coupled with good electrical conductivity (2.83 S/m). More importantly, it exhibits a high triboelectric output performance of 205.3 V, maintains stable long-term triboelectric output, and achieves a maximum triboelectric power density of 732.1 mW/m 2 . Furthermore, the introduction of glycerin into the cellulose hydrogel enhances its mechanical properties and triboelectric output performance even under extreme environmental conditions (–24 and 60 °C). The A-GC-TENG demonstrates significant potential in various applications, including mechanical energy harvesting and conversion, writing recognition, wireless signal transmission, and human-computer interaction, showing great application prospects in flexible wearable sensors and self-powered electronic devices. The development of the composite cellulose hydrogel offers a novel approach for the fabrication of high-performance flexible wearable electronic devices, which is capable of functioning effectively in harsh environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".