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Record W4214718935 · doi:10.3390/nano12050803

Colloidal Processing of Mn3O4-Carbon Nanotube Nanocomposite Electrodes for Supercapacitors

2022· article· en· W4214718935 on OpenAlexafffund
Wenjuan Yang, Igor Zhitomirsky

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorCarbon nanotubeMaterials scienceElectrodeDispersantCapacitanceElectrolyteChemical engineeringCyclic voltammetryNanotechnologyNanocompositeHorizontal scan rateNanotubeNanoparticleCathodeCarbon fibersAdsorptionDispersion (optics)ElectrochemistryComposite materialChemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

This investigation addresses the challenges in the development of efficient nanostructured Mn3O4 cathodes for supercapacitors. A high areal capacitance and the ability to avoid a time-consuming activation procedure for electrodes with high active mass loading of 40 mg cm−2 are reported. This facilitates practical applications of Mn3O4 based electrodes. The highest capacitance of 6.11 F cm−2 (153 F g−1) is obtained from cyclic voltammetry at a scan rate of 2 mV s−1 and 6.07 F cm−2 (151.9 F g−1) from the chronopotentiometry at a current density of 3 mA cm−2 in a potential window of 0.9 V in a neutral Na2SO4 electrolyte. The new approach is based on the application of rhamnolipids (RL) as a capping agent for the synthesis of Mn3O4 particles and a co-dispersant for Mn3O4 and carbon nanotubes, which are used as conductive additives. The size and shape of the Mn3O4 particles are influenced by RL. The enhanced performance of the electrodes is linked to the chemical structure and properties of RL molecules, which exert influence on Mn3O4 particle size and shape during synthesis, reduce agglomeration, facilitate RL adsorption on Mn3O4 and carbon nanotubes, and influence their co-dispersion and mixing at the nanometric scale.

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 score0.004
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.243
Teacher spread0.227 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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