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Record W2162403033 · doi:10.1002/aenm.201400867

Towards a Stable Organic Electrolyte for the Lithium Oxygen Battery

2014· article· en· W2162403033 on OpenAlexafffund
Brian D. Adams, Robert W. Black, Zack Williams, Russel Fernandes, Marine Cuisinier, Erik J. Berg, Petr Novák, Graham K. Murphy, Linda F. Nazar

Bibliographic record

VenueAdvanced Energy Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNatural Resources CanadaU.S. Department of Energy
KeywordsElectrolyteDimethoxyethaneLithium (medication)Materials scienceBattery (electricity)Inorganic chemistryCathodeChemical engineeringChemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

A new lithium‐ether‐derived chelate ionic liquid is synthesized to serve as an electrolyte for the Li‐O 2 battery that is stable to metallic lithium, and whose ethereal framework is much more inherently stable to superoxide‐initiated hydrogen abstraction than the simple glyme, dimethoxyethane (DME). Reactions of chemically generated superoxide with this electrolyte show that virtually no decomposition products such as lithium formate are generated. When the electrolyte is employed in a Li‐O 2 battery, a ten‐fold decrease in CO 2 evolution is evident on charge by comparison to DME and greatly enhanced cycling stability is observed with TiC as a cathode support. A mechanism is proposed to account for the lowered reactivity, offering new insight into the stability of organic electrolytes in Li‐O 2 batteries. This approach for electrolyte design is presented here for the first time, and it can be extended to other organic systems to provide a platform for the design of advanced electrolyte 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 score0.001
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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.193
Teacher spread0.188 · 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

Citations225
Published2014
Admission routes2
Has abstractyes

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