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Record W2284016961 · doi:10.1149/06919.0023ecst

A Bifunctional Air Electrode Catalyzed by Lead Ruthenate for Li-Air Batteries

2015· article· en· W2284016961 on OpenAlexafffund
Xiao‐Zi Yuan, Mathieu Toupin, Wei Qu, Alexis Laforgue, Jason Fahlman, Max Jiang

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsNational Research Council Canada
FundersDivision of Electrical, Communications and Cyber SystemsNatural Resources Canada
KeywordsBifunctionalElectrocatalystMaterials scienceElectrodeChemical engineeringLithium (medication)NanosheetCatalysisInorganic chemistryChemistryNanotechnologyElectrochemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The performance of a catalyst enforced commercially available gas diffusion layer (GDL) electrode was evaluated at different discharge rates and depths in non-aqueous Li-O2 batteries. The lead ruthenate (PbRuO) powder with a pyrochlore structure was employed as the catalyst. The lead ruthenate pyrochlore was prepared using a direct soft template method and characterized by X-ray diffraction (XRD), nitrogen physisorption techniques and transmission electron microscopy (TEM). The charge/discharge capabilities of the PbRuO/Ketjenblack (KB) composite were studied in tetraethyleneglycol dimethyl ether (TEGDME) containing 1M lithium triflate. The results show that PbRuO/KB can enhance the cell performance at high charge/discharge rates. Moreover, at a current density of 0.1mA/cm2 it significantly increases the cell cyclability. With a cut-off capacity of 1000 mAh/g (PbRuO/KB only) the cell reached 100 cycles. As such, lead ruthenate has the potential to be used as an efficient bifunctional electrocatalyst for non-aqueous 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 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 score0.003

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)0.0010.001

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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
Published2015
Admission routes2
Has abstractyes

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