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Record W2308649583 · doi:10.1149/ma2016-03/2/751

Reduction of Charge and Discharge Polarization By Cobalt Nanoparticles-Embedded Carbon Nanofibers for Lithium-Oxygen Batteries

2016· article· en· W2308649583 on OpenAlexaff
Yun‐Jung Kim, Hongkyung Lee, Dong Jin Lee, Hyungjun Noh, Jin Hong Lee, Jung-Ki Park, Hee‐Tak Kim

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsOverpotentialMaterials scienceNanoparticleAmorphous solidLithium (medication)NanotechnologyIonic conductivityChemical engineeringCobaltElectrodeChemistryElectrochemistryPhysical chemistryCrystallographyMetallurgyElectrolyte

Abstract

fetched live from OpenAlex

The battery material community has a persistent consensus that Li–O2 batteries would be the most promising next generation batteries due to its extraordinarily high energy densities; from a practical view point, at least 2~3 times higher than those of conventional lithium ion batteries (LIBs).[1] However, Li–O2 batteries suffer from their chronically low round trip efficiency due to poor reversibility of lithium peroxide, Li2O2, which is main product of discharged Li–O2 batteries. This truly originates from its intrinsically poor electron and ionic conductivity.[2] Therefore, the reduction of charging overpotential is in urgent need for economical progress of Li–O2 batteries. In an effort to achieve high round trip efficiency, recent studies have attracted great attention that the charge behaviors of Li–O2 cells can be manipulated by controlling the phase structure and morphology of Li2O2 formed during discharge. In particular, at a low current density, the large toroidal structured, crystalline Li2O2 was primarily formed. In contrast, the formation of film-like amorphous Li2O2 is favored at high current density. [3,4] Owing to its structural advantages of enhanced ionic and electronic transport[5], the oxidation of less crystalline Li2O2 is more efficient, resulting in significant decrease on charge overpotential. Consequently, the controlling phase and morphology of Li2O2 is an effective strategy for manipulating charge behavior of Li–O2 cells. As a possible approach for controlling Li2O2, we designed an effective electrochemical catalyst of oxygen reduction reactions in Li-O2 batteries, a cobalt nanoparticles (Co NPs) embedded carbon nanofibers (Co-CNFs). As a result, embedded Co NPs leads to the formation of uniform film-like amorphous Li2O2, and thus, the Li–O2 battery employing the Co-CNF provides a round trip efficiency of > 77% and extends cycling stability by more than 6 times in comparison with a CNF electrode. These change of Li2O2 mainly is derived from a charge delocalization originating from the electron transfer from Co NPs to C-atoms. According to HSAB theory, highly concentrated Li+ ions near electrode resulting from the preferable interaction with Co-CNFs could lead to the rapid precipitation of Li2O2 and prevent the crystallization of Li2O2. The findings suggest a novel electrode design strategy of combining inexpensive metal and carbons for modulating the phase of discharge product. References [1] Nat. Mater. 2012, 11, 19-29 [2] J. Phys. Chem. Lett. 2013, 4, 93−99 [3] J. Am. Chem. Soc, 2013, 135, 15364−15372 [4] Energy Environ. Sci., 2013, 6, 1772 [5] Chem. Mater, 2014, 26, 2952−2959

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)0.0010.000

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 head0.227
Teacher spread0.217 · 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
Published2016
Admission routes1
Has abstractyes

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