Recent Developments of Planar Micro‐Supercapacitors: Fabrication, Properties, and Applications
Bibliographic record
Abstract
Abstract The increasing development of wearable, portable, implantable, and highly integrated electronic devices has led to an increasing demand for miniaturization of energy storage devices. In recent years, supercapacitors, as an energy storage device, have received enormous attention owing to their excellent properties of quick charge and discharge, high power density, and long life cycle with minimal maintenance. Micro‐supercapacitors (MSCs) as a promising candidate for miniaturized energy storage components have undergone considerable theoretical and experimental investigations. Particularly, planar MSCs with a 2D architecture design have more attractive application prospects due to their flexible design and excellent electrochemical performance. However, the major drawbacks of MSCs are their intrinsically low energy density. For this reason, researchers have conducted much investigation to improve their energy density in order to promote their practical application. Herein, the recent development and progress of planar MSCs from the scope of the substrates, electrode materials, fabrication methods, electrochemical properties, and applications are discussed. Finally, the currently existing challenges and developments associated with planar MSCs are also discussed. All in all, planar MSCs have great application potential in various fields of electrochemical energy storage, self‐powered wireless sensors, and stimuli‐responsive and photoresponsive, alternating current line filtering.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".