An adhesive cellulose nanocrystal-reinforced nanocomposite hydrogel electrolyte for supercapacitor applications
Bibliographic record
Abstract
Hydrogel electrolytes were applied in various energy storage devices, including supercapacitors. However, they still suffer from disadvantages such as low mechanical performance and poor adhesion of the interfaces between electrolytes and electrodes. Herein, an adhesive hydrogel electrolyte with promising mechanical strength and electrochemical performance was designed by introducing hydrophobic carbon chains as long-range physical cross-linkers and cellulose nanocrystal (CNC) as biopolymer nano-reinforcement, and soak-loading liquid electrolytes such as KOH into the hydrogel matrix. The hydrogel electrolyte loaded with 1 M KOH demonstrated the best tensile stress of 362.31 kPa and an elongation of 2479 %, and exhibited self-repairability by applying stimuli on the cut interface. The hydrogel electrolyte showed excellent adhesion on various surfaces, including nonconductive and conductive materials such as cardboard, leather, carbon film, and carbon cloth. Regarding electrochemical properties, the hydrogel electrolyte showed the largest conductivity of 0.207 ± 0.005 S/cm when soak-loading in 1 M KOH for 24 hrs. Moreover, the hydrogel electrolyte exhibited promising electrochemical performance when assembled into coin-cell supercapacitors using free-standing activated carbon sheets as electrodes. A capacitance of 67.31 F/g at 0.05A/g, and almost 100% capacitance retention at 0.1 A/g after 2200 cycles was achieved.
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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".