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Record W2077920045 · doi:10.1039/c3ta12458d

Surface modification of MnO2 and carbon nanotubes using organic dyes for nanotechnology of electrochemical supercapacitors

2013· article· en· W2077920045 on OpenAlexaff
Yaohui Wang, Yangshuai Liu, Igor Zhitomirsky

Bibliographic record

VenueJournal of Materials Chemistry A · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSupercapacitorCarbon nanotubeDispersantElectrophoretic depositionMaterials scienceAdsorptionChemical engineeringComposite numberDispersion (optics)ElectrochemistryElectrodeNanoparticleChemical bath depositionNanotechnologyComposite materialThin filmChemistryOrganic chemistryCoating

Abstract

fetched live from OpenAlex

Efficient dispersion and electrophoretic deposition (EPD) of multiwalled carbon nanotubes (MWCNTs) was achieved using organic dyes, such as pyrocatechol violet (PV) and m-cresol purple (CP). The problem of MnO2 nanoparticle dispersion in concentrated suspensions was addressed by the use of PV as a dispersant. The analysis and comparison of experimental data for PV and CP provided insight into the influence of chemical structures of the dyes on their adsorption on MWCNTs and MnO2. The adsorption of PV on MWCNTs and MnO2 was attributed to π–π interactions and catecholate type bonding, respectively. The EPD yield can be varied by the variation of the PV concentration in the suspensions, deposition voltage and time. It was found that PV can be used as a co-dispersant for EPD of MWCNTs and MnO2 and the fabrication of MnO2–MWCNT composites. The proposed approach offers advantages of uniform distribution of individual components and low binder content in the composite. MnO2–MWCNT films were prepared by EPD for thin film electrodes of electrochemical supercapacitors (ES). Bulk MnO2–MWCNT electrodes with a material loading of 40 mg cm−2 were obtained by the impregnation of Ni foam current collectors. The highest specific capacitance of 5.9 F cm−2 (148 F g−1) was achieved. The composite materials are promising for ES applications.

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 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 score0.005
Threshold uncertainty score0.546

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 science0.0000.000
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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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

Citations60
Published2013
Admission routes1
Has abstractyes

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