Metal‐Organic Framework Reinforced Highly Stretchable and Durable Conductive Hydrogel‐Based Triboelectric Nanogenerator for Biomotion Sensing and Wearable Human‐Machine Interfaces
Bibliographic record
Abstract
Abstract Flexible triboelectric nanogenerators (TENGs) with multifunctional sensing capabilities offer an elegant solution to address the growing energy supply challenges for wearable smart electronics. Herein, a highly stretchable and durable electrode for wearable TENG is developed using ZIF‐8 as a reinforcing nanofiller in a hydrogel with LiCl electrolyte. ZIF‐8 nanocrystals improve the hydrogel's mechanical properties by forming hydrogen bonds with copolymer chains, resulting in 2.7 times greater stretchability than pure hydrogel. The hydrogel electrode is encapsulated by microstructured silicone layers that act as triboelectric materials and prevent water loss from the hydrogel. Optimized ZIF‐8‐based hydrogel electrodes enhance the output performance of TENG through the dynamic balance of electric double layers (EDLs) during contact electrification. Thus, the as‐fabricated TENG delivers an excellent power density of 3.47 Wm – 2 , which is 3.2 times higher than pure hydrogel‐based TENG. The developed TENG can scavenge biomechanical energy even at subzero temperatures to power small electronics and serve as excellent self‐powered pressure sensors for human‐machine interfaces (HMIs). The nanocomposite hydrogel‐based TENG can also function as a wearable biomotion sensor, detecting body movements with high sensitivity. This study demonstrates the significant potential of utilizing ZIF‐8 reinforced hydrogel as an electrode for wearable TENGs in energy harvesting and sensor technology.
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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) | 0.001 | 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".