Insights into the crystallization, topographical, and tribological properties of sustainable PEO-mica based triboelectric nanogenerators
Bibliographic record
Abstract
Abstract Contact electrification is the primary mechanism dictating electron transfer and surface charge density for triboelectric nanogenerators (TENGs), making intrinsic material and physical surface properties key parameters for the interfacial charge transfer phenomena. Surface properties are governed by the morphological and textural microstructural features, including tribological interactions, topographical profiling, surface roughness, and real contact area. Therefore, understanding surface morphological effects on the triboelectric performance aids development towards adapting and optimizing surface properties. Particularly, in polymer-based composites TENGs, the surface morphology relies on polymer crystallization and interactions with reinforcing additives. This comprehensive study evaluated the effects of isothermal crystallization and the incorporation and dispersibility of raw and few-layer exfoliated muscovite mica fillers, insightfully realizing and tuning polyethylene oxide’s intrinsic properties and semi-crystalline microstructure. The full material characterization presented dramatic variations in polymer growth kinetics, chain dynamics, lamellae profiling, surface roughness, and work functions, allowing the development of a constructive triboelectric surface microstructural design guide. The crystallization temperature of 65 °C with raw mica demonstrated the greatest dielectric properties and triboelectric performance resulting in a peak-to-peak voltage, peak-to-peak current density, transferred charge density, and power density of respectively, 488 V, 45.5 mA m −2 , 152 μ C m −2 , and 24.0 W m −2 at a load resistance of 6 MΩ. The TENG device demonstrated stable long-term voltage outputs over the duration of 12 000 contact-separation cycles and successfully self-powered natural resource environmental monitoring sensors.
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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 | 0.000 | 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".