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Record W4321607181 · doi:10.1021/acsenergylett.3c00056

Temperature-Dependent Discharge of Li-O<sub>2</sub> and Na-O<sub>2</sub> Batteries

2023· article· en· W4321607181 on OpenAlexfundno aff
Graham Leverick, Gabriela Alvarez Perez, Ryan Stephens, Yang Shao‐Horn

Bibliographic record

VenueACS Energy Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSiebel Scholars FoundationShell Exploration and Production CompanyNational Science Foundation
KeywordsDisproportionationOxygenChemistrySolubilityAtmospheric temperature rangeCapacity lossChemical engineeringMaterials scienceElectrochemistryThermodynamicsElectrodePhysical chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Li-O 2 and Na-O 2 batteries offer the promise of increased energy densities compared to Li-ion batteries but suffer from poor power and cycle life. Despite considerable research on the oxygen reduction reaction (ORR) in these cells at room temperature, limited work has been performed to understand the influence of temperature on the discharge characteristics. In this Letter, we show that the discharge capacity of Li-O 2 cells increases with increasing temperature while the discharge capacity of Na-O 2 cells decreases over the same temperature range. We show that the discharge behavior of Na-O 2 cells is dominated by increasing superoxide solubility with decreasing temperature. On the other hand, increasing Li + -O 2 – coupling strength with decreasing temperature promotes the formation of insoluble Li 2 O 2 through either disproportionation or a 2e – reduction mechanism, leading to reduced discharge capacity in Li-O 2 cells. Such findings highlight the complex and important effect that temperature has on ORR in Li-O 2 and Na-O 2 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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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