Advances in layer manganese dioxide for energy conversion and storage: mechanisms, strategies and prospects
Bibliographic record
Abstract
Layer manganese dioxide with its special structure, low price and large theoretical specific capacitance/capacity is considered a competitive candidate for various energy conversion and storage devices, such as supercapacitors and batteries (Li-ion, Na-ion, and Zn-ion) However, challenges such as low electronic/ionic conductivity, sluggish diffusion kinetics, and structural collapse during cycling are still the main factors limiting its practical application. A solid understanding of the correlation between structure and performance will greatly promote the performance and the further application of layer manganese dioxide. In this review, the energy storage mechanism of layer manganese dioxide in different energy storage devices is discussed in detail. Additionally, considering the current difficulties and challenges, recent advances in strategies for electrochemical performance improvement are systematically summarized, including synthetic methods, structure design, and interlayer engineering. Finally, suggestions for the future directions and developments in preparing layer manganese dioxide cathodes with high electrochemical performance are put forward.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
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".