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Record W2788289359 · doi:10.1007/s41918-018-0002-3

A Review of Carbon-Composited Materials as Air-Electrode Bifunctional Electrocatalysts for Metal–Air Batteries

2018· review· en· W2788289359 on OpenAlexaff
Yan-Jie Wang, Baizeng Fang, Dan Zhang, Aijun Li, David P. Wilkinson, Anna Ignaszak, Lei Zhang, Jiujun Zhang

Bibliographic record

VenueElectrochemical Energy Reviews · 2018
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsNational Research Council CanadaUniversity of New BrunswickUniversity of British Columbia
Fundersnot available
KeywordsBifunctionalCarbon fibersOxygen evolutionCommercializationMaterials scienceNanotechnologyCatalysisOxygen reduction reactionElectrodeChemistryElectrochemistryBusinessComposite material

Abstract

fetched live from OpenAlex

Abstract Metal–air batteries (MABs), particularly rechargeable MABs, have gained renewed interests as a potential energy storage/conversion solution due to their high specific energy, low cost, and safety. The development of MABs has, however, been considerably hampered by its relatively low rate capability and its lack of efficient and stable air catalysts in which the former stems mainly from the sluggish kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) and the latter stems from the corrosion/oxidation of carbon materials in the presence of oxygen and high electrode potentials. In this review, various carbon-composited bifunctional electrocatalysts are reviewed to summarize progresses in the enhancement of ORR/OER and durability induced by the synergistic effects between carbon and other component(s). Catalyst mechanisms of the reaction processes and associated performance enhancements as well as technical challenges hindering commercialization are also analyzed. To facilitate further research and development, several research directions for overcoming these challenges are also proposed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations216
Published2018
Admission routes1
Has abstractyes

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