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Record W1979758197 · doi:10.1149/2.0221505jes

Electrochemical Performance of Carbon/MnO<sub>2</sub>Nanocomposites Prepared via Molecular Bridging as Supercapacitor Electrode Materials

2015· article· en· W1979758197 on OpenAlexaff
Claudia Ramirez-Castro, Olivier Crosnier, Laurence Athouël, R. Retoux, Daniel Bélanger, Thierry Brousse

Bibliographic record

VenueJournal of The Electrochemical Society · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsNanocompositeSupercapacitorManganeseMaterials scienceElectrochemistryAmorphous carbonCarbon fibersElectrodeOxideInorganic chemistryChemical engineeringCapacitanceManganese oxideAmorphous solidNanotechnologyChemistryComposite numberComposite materialOrganic chemistryMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

The chemical binding of amorphous manganese oxide and carbon particles was achieved with the diazonium chemistry. The synthesis was performed in two steps, with a first step consisting in the surface functionnalization of carbon particles with aminophenyl groups and the subsequent attachment of amorphous manganese oxide particles through generated phenyl groups. The bond between carbon and MnO 2 particles is believed to occur between the carbon from the phenyl groups attached to carbon particles, and the oxygen atoms from the manganese oxide lattice. The capacitance of the carbon/MnO 2 grafted nanocomposite electrode is doubled compared to a simple mixture of its two components. The capacitance of the nanocomposite electrode is also retained for faster cycling rates, thus highlighting the role of intimate coupling of carbon and MnO 2 .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

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

Citations49
Published2015
Admission routes1
Has abstractyes

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