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Record W4409542857 · doi:10.1039/d5sc00932d

Advances in layer manganese dioxide for energy conversion and storage: mechanisms, strategies and prospects

2025· review· en· W4409542857 on OpenAlexaff
Yadi Zhang, Hong‐Kun Xu, Manal S. Ebaid, Xinjie Zhang, Kaixin Jiang, Xuehua Zhang, Zhanhu Guo, Ben Bin Xu

Bibliographic record

VenueChemical Science · 2025
Typereview
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
FundersScience and Technology Program of Gansu ProvinceNational College Students Innovation and Entrepreneurship Training ProgramEngineering and Physical Sciences Research CouncilNorthern Borders University
KeywordsSupercapacitorManganeseEnergy storageCapacitanceLayer (electronics)Materials scienceNanotechnologyChemistryElectrodePhysicsMetallurgy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.333
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.291
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2025
Admission routes1
Has abstractyes

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