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Record W4296737982 · doi:10.21203/rs.3.rs-2029983/v1

Designing breathing air-electrode and enhancing the oxygen electrocatalysis by thermoelectric effect for efficient Zn-air batteries

2022· preprint· en· W4296737982 on OpenAlexaff
Xuerong Zheng, Yanhui Cao, Xiaopeng Han, Haozhi Wang, Zhao Zhang, Menghan Zhao, Jihong Li, Yang Wang, Jiajun Wang, Yuesheng Wang, Li Zhang, Karim Zaghib, Yida Deng, Wenbin Hu

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsConcordia UniversityHydro-Québec
FundersTianjin UniversityNational Natural Science Foundation of China
KeywordsElectrocatalystThermoelectric effectElectrodeOxygenBreathingMaterials scienceChemistryElectrochemistryPhysicsMedicineThermodynamicsPhysical chemistryAnesthesia

Abstract

fetched live from OpenAlex

Abstract The sluggish kinetics and mutual interference of oxygen evolution and reduction reactions (OER and ORR) in the air electrode resulted in large charge/discharge overpotential and low energy efficiency of Zn-air batteries. In this work, we designed a breathing air-electrode configuration in Zn-air batteries using P-type Ca3Co4O9 and N-type CaMnO3 as charge and discharge thermoelectrocatalysts, respectively. The Seebeck voltages generated from thermoelectric effect of Ca3Co4O9 and CaMnO3 synergistically compensated the OER and ORR overpotentials. The carrier migration and accumulation on the cold surface of Ca3Co4O9 and CaMnO3 optimized the electronic structure of metallic sites and thus enhanced their intrinsic catalytic activity. The OER and ORR overpotentials were enhanced by 101 and 90 mV, respectively, at temperature gradient of 200 ℃. The breathing Zn-air battery displayed a remarkable energy efficiency of 68.1%. This work provides an efficient avenue towards utilizing waste heat for improving the energy efficiency of Zn-air batteries.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.298
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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