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Record W2745658058 · doi:10.1038/s41598-017-09982-9

Fe-based hybrid electrocatalysts for nonaqueous lithium-oxygen batteries

2017· article· en· W2745658058 on OpenAlexfundno aff
Seun Lee, Gwang‐Hee Lee, Hack Jun Lee, Mushtaq Ahmad Dar, Dong‐Wan Kim

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersInstitute of Population and Public HealthNational Research Foundation of KoreaKorea Research Institute of Chemical TechnologyKing Saud UniversityNational Research Foundation
KeywordsOverpotentialMaterials scienceLithium (medication)ElectrochemistryOxygenElectrodeDielectric spectroscopyElectrocatalystChemical engineeringOxygen evolutionChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Lithium–oxygen batteries promise high energy densities, but are confronted with challenges, such as high overpotentials and sudden death during discharge–charge cycling, because the oxygen electrode is covered with the insulating discharge product, Li 2 O 2 . Here, we synthesized low–cost Fe–based nanocomposites via an electrical wire pulse process, as a hybrid electrocatalyst for the oxygen electrode of Li–O 2 batteries. Fe 3 O 4 -Fe nanohybrids–containing electrodes exhibited a high discharge capacity (13,890 mA h g c −1 at a current density of 500 mA g c −1 ), long cycle stability (100 cycles at a current rate of 500 mA g c −1 and fixed capacity regime of 1,000 mA h g c −1 ), and low overpotential (1.39 V at 40 cycles). This superior performance resulted from the good electrical conductivity of the Fe metal nanoparticles during discharge–charge cycling, which could enhance the oxygen reduction reaction and oxygen evolution reaction activities. We have demonstrated the increased electrical conductivity of the Fe 3 O 4 -Fe nanohybrids using electrochemical impedance spectroscopy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.056
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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 teacher head, 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

Citations14
Published2017
Admission routes1
Has abstractyes

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