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Record W3157163300 · doi:10.1002/batt.202000198

Chloride Ion as Redox Mediator in Reducing Charge Overpotential of Aprotic Lithium‐Oxygen Batteries

2020· article· en· W3157163300 on OpenAlexaff
Qi Zhang, Yin Zhou, Wenrui Dai, Xinhang Cui, Zhiyang Lyu, Zheng Hu, Wei Chen

Bibliographic record

VenueBatteries & Supercaps · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMinistry of Education and Child Care
FundersNational Research Foundation Singapore
KeywordsOverpotentialElectrolyteRedoxLithium (medication)ChemistryBattery (electricity)Energy storageInorganic chemistryChemical engineeringElectrochemistryElectrodePhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The aprotic lithium‐oxygen (Li−O2) battery with a high theoretical energy density has been considered as a promising candidate for next‐generation energy storage devices. However, the formation of insulating Li2O2 products is a major obstacle for realizing the high energy efficiency and long cycle life. Here, we report a new Cl−/Cl3− redox mediator to reduce the charge overpotential by a facile introduction of chloride ion (Cl−) additives into the organic electrolyte. The redox mediator can effectively promote the formation of the LiOH discharge product, and facilitate efficient LiOH decomposition. Therefore, the cell with the Cl− additives possesses a significantly low charge overpotential of 0.29 V, an extended cycle life (up to 71 cycles) at a rate of 500 mA g−1 with a fixed capacity of 500 mAh g−1, and an enhanced rate capability. This study offers an effective approach to modulate discharge products from Li2O2 to LiOH and provides new insights toward the role of redox mediators through the addition of Cl− in Li−O2 battery systems.

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 score0.002

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 head0.010
GPT teacher head0.205
Teacher spread0.194 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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