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Record W4303446393 · doi:10.1016/j.cej.2022.139608

A 98.2% energy efficiency Li-O2 battery using a LaNi-0.5Co0.5O3 perovskite cathode with extremely fast oxygen reduction and evolution kinetics

2022· article· en· W4303446393 on OpenAlexaff
Qianyuan Qiu, Zheng‐Ze Pan, Penghui Yao, Jiashu Yuan, Chun Xia, Yicheng Zhao, Yongdan Li

Bibliographic record

VenueChemical Engineering Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilKemian tekniikan korkeakoulu, Aalto-yliopistoAalto-YliopistoAcademy of Finland
KeywordsOverpotentialCathodeElectrolyteBattery (electricity)Perovskite (structure)OxygenMaterials scienceChemical engineeringLithium (medication)Oxygen evolutionChemistryElectrodeThermodynamicsPhysical chemistryElectrochemistry

Abstract

fetched live from OpenAlex

Rechargeable lithium-oxygen (Li-O2) batteries have been regarded as a promising energy storage device, but its practical use is impeded by its low energy efficiency. Herein, a bi-functional catalytic perovskite LaNi0.5Co0.5O3 (LNCO) is employed as the cathode of an efficient Li-O2 battery with a molten nitrate salt electrolyte at 160 °C. It displays a stable low charge–discharge overpotential 50 mV with a high energy efficiency (EE) 98.2 % at 0.1 mA cm−2 for over 100 cycles. The excellent performance is attributed to the extremely fast oxygen reduction and evolution kinetics on the surface of LNCO. The discharge product is Li2O with a porous and fluffy morphology which facilitates the transfer of oxygen and other intermediate species. It is noted that Li2O as a discharge product enables a theoretical specific energy density of 5200 Wh kg−1, which is superior to the Li2O2 as product giving 3500 Wh kg−1 for those ambient temperature Li-O2 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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 designSimulation or modeling
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

Citations19
Published2022
Admission routes1
Has abstractyes

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