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Record W4281666694 · doi:10.1002/cssc.202200612

Engineering Amorphous/Crystalline Structure of Manganese Oxide for Superior Oxygen Catalytic Performance in Rechargeable Zinc–Air Batteries

2022· article· en· W4281666694 on OpenAlexaff
Zihao Zhou, Xiaoying Zheng, Manna Liu, Peng Liu, Shengbo Han, Ying-Ru Chen, Bang Lan, Ming Sun, Lin Yu

Bibliographic record

VenueChemSusChem · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsManganeseCatalysisZincAmorphous solidOxygenManganese oxideInorganic chemistryOxygen evolutionMaterials scienceChemistryChemical engineeringMetallurgyElectrochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Although amorphous materials are popular in oxygen electrocatalysis, their performance requires further improvement to meet the need for rechargeable zinc–air batteries. In this work, an amorphous/crystalline layered manganese oxide (ACMO) was designed, and its unique amorphous/crystalline homogeneous structure activated its oxygen reduction activity with a positive half‐wave potential of 0.81 V and oxygen evolution activity with a moderate overpotential of 407 mV at 10 mA cm −2 . Moreover, the amorphous/crystalline structure endowed ACMO with excellent stability. While employed as the air‐electrode material for rechargeable zinc–air batteries, ACMO overcame the poor cycling stability of manganese oxide and cycled stably for 1000 cycles (≈17 days) at 10 mA cm −2 . Besides, it delivered a high power density of 159.7 mW cm −2 and a narrow voltage gap of 0.66 V. This work gives an insight into designing oxide materials with amorphous/crystalline structure and feasible guidance for harmonizing electrochemical activity and stability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations24
Published2022
Admission routes1
Has abstractyes

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