Recent advances in synthesis of MXene-based electrodes for flexible all-solid-state supercapacitors
Bibliographic record
Abstract
Various energy storage sources have been developed so far, among which supercapacitors are more important for the forthcoming generations due to their small size and portability. Supercapacitors as good alternatives to batteries have recently attracted more attention because they have higher power and excellent charging-discharging rate which is considered as a challenging issue that limits the use of batteries. Supercapacitors also have other advantages over batteries, including higher reversibility and cycle life, lower maintenance costs, and safer electrode materials. MXenes have emerged as a new class of 2D composites in electrode materials for supercapacitors as low-cost and environment-friendly carbides and nitrides. MXenes are suitable inorganic compounds with excellent electrochemical properties and mechanical integrity to improve supercapacitor energy density at a new interval. This review presents new synthesis strategies to prevent the self-accumulation of MXene layers. First, the fundamental working theories of different supercapacitors are outlined. Next, an overview of the electrode material based on MXenes is outlined, and the latest solutions for increasing the active sites and improving the ion transfer rate have been collected. Hybridization and doping of MXenes change the properties of the composite, leading to a transformation in the structure and an increase in the capacitance. Furthermore, the utilization of double-transition metal MXenes solves challenges such as structural destruction and short life spans in multiple charge-discharge cycles. Then evaluation of the new MXene-based electrode materials in all-solid-state supercapacitors has been summarized. Finally, an overview of the latest developments in the creation of all-solid-state flexible supercapacitors as well as our predictions for future lines of inquiry is provided.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".