Molecular Functionalization of Graphene Oxide for Next-Generation Wearable Electronics
Bibliographic record
Abstract
Acquiring reliable and efficient wearable electronics requires the development of flexible electrolyte membranes (EMs) for energy storage systems with high performance and minimum dependency on the operating conditions. Herein, a freestanding graphene oxide (GO) EM is functionalized with 1-hexyl-3-methylimidazolium chloride (HMIM) molecules via both covalent and noncovalent bonds induced by esterification reactions and electrostatic πcation-π stacking, respectively. Compared to the commercial polymeric membrane, the thin HMIM/GO membrane demonstrates not only slightest performance sensitivity to the operating conditions but also a superior hydroxide conductivity of 0.064 ± 0.0021 S cm(-1) at 30% RH and room temperature, which was 3.8 times higher than that of the commercial membrane at the same conditions. To study the practical application of the HMIM/GO membranes in wearable electronics, a fully solid-state, thin, flexible zinc-air battery and supercapacitor are made exhibiting high battery performance and capacitance at low humidified and room temperature environment, respectively, favored by the bonded HMIM molecules on the surface of GO nanosheets. The results of this study disclose the strong potential of manipulating the chemical structure of GO to work as a lightweight membrane in wearable energy storage devices, possessing highly stable performance at different operating conditions, especially at low relative humidity and room temperature.
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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".