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Record W3026740637 · doi:10.1149/ma2019-01/45/2133

CO<sub>2</sub>-Derived Manganese-Oxide Carbon Composites As Electrode Materials for Fuel Cells and Supercapacitors

2019· article· en· W3026740637 on OpenAlexaff
Jae Hyun Park, Won Yeong Choi, Yeeun Kim, Jae Wook Lee

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsSupercapacitorMaterials scienceManganeseCarbon fibersNanocompositeSodium borohydrideElectrochemistryOxideBoronBoron oxideCatalysisElectrodeInorganic chemistryComposite materialChemical engineeringChemistryComposite numberMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Boron-doped porous carbon impregnated with manganese oxide were synthesized from sodium borohydride (NaBH4) and carbon dioxide (CO2) under a moderate experimental condition (1 bar and 500 ℃) by a facile two-step method. The boron-manganese oxide-carbon nanocomposites exhibited enhanced electrochemical properties in both electrocatalysts for oxygen reduction reaction (ORR) and electrode materials for supercapacitors. The composites showed a four electron transfer pathway and had electrocatalytic activity comparable to that of commercial platinum-carbon catalyst. They also provided a high capacitance (150 F g-1 at 1.0 A g-1 and 136 F g-1 at 10.0 A g-1) compared to that of bare boron-doped carbon (58 F g-1 at 1.0 A g-1 and 15 F g-1 at 10.0 A g-1) due to generation of new active sites, increase in specific surface area, and reduction of overall resistance by efficient interaction within the composites.

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 head0.222
Teacher spread0.212 · 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
Published2019
Admission routes1
Has abstractyes

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