Simultaneous Enhancement of Thermoelectric and Mechanical Properties in Recyclable Transparent Iongels via In-Situ Phase Separation
Bibliographic record
Abstract
Wearable thermoelectric energy harvesting has garnered significant attention owing to its potential for powering flexible and self-sustaining devices. A major challenge in this field is the design of ionogels that balance mechanical strength with high ionic conductivity. Current ionogels often compromise one of these properties, which limits their practical use in flexible electronics. Addressing this gap is critical for the advancement of wearable thermoelectric technologies. Herein, we present a novel ionogel fabrication method that embeds hydroxypropyl cellulose (HPC) fibers into a biocompatible polyvinyl alcohol (PVA) matrix, which is then loaded with an ionic liquid (IL). This design significantly improved the mechanical properties of the ionogel by leveraging the reinforcing effect of the HPC fibers, which also created an IL-rich spherical structure through in-situ microphase separation that promoted efficient ion migration. Ionogel-8515, which is the optimized formulation, exhibits superior performance, achieving an ionic conductivity of 36.79 mS cm⁻¹ and an ionic Seebeck coefficient of 2.783 mV K⁻¹, while maintaining an excellent mechanical flexibility. Our results demonstrate that Ionogel-8515 not only meets the mechanical and conductive requirements for wearable applications, but is also recyclable because of its reversible crosslinking network. This advancement bridges the current gap in ionogel design and offers a sustainable and efficient solution for future thermoelectric technologies.
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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".