Nanocellulose-Based Conductive Membranes for Free-Standing Supercapacitors: A Review
Bibliographic record
Abstract
There is currently strong demand for the development of advanced energy storage devices with inexpensive, flexibility, lightweight, and eco-friendly materials. Cellulose is considered as a suitable material that has the potential to meet the requirements of the advanced energy storage devices. Specifically, nanocellulose has been shown to be an environmentally friendly material that has low density and high specific strength, Young's modulus, and surface-to-volume ratio compared to synthetic materials. Furthermore, it can be isolated from a variety of plants through several simple and rapid methods. Cellulose-based conductive composite membranes can be assembled into supercapacitors to achieve free-standing, lightweight, and flexible energy storage devices. Therefore, they have attracted extensive research interest for the development of small-size wearable devices, implantable sensors, and smart skin. Various conductive materials can be loaded onto nanocellulose substrates to endow or enhance the electrochemical performance of supercapacitors by taking advantage of the high loading capacity of nanocellulose membranes for brittle conductive materials. Several factors can impact the electronic performance of a nanocellulose-based supercapacitor, such as the methods of loading conductive materials and the types of conductive materials, as will be discussed in this review.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".