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Record W3165315810 · doi:10.3390/ma14112930

Composite Fe3O4-MXene-Carbon Nanotube Electrodes for Supercapacitors Prepared Using the New Colloidal Method

2021· article· en· W3165315810 on OpenAlexafffund
Wenyu Liang, Igor Zhitomirsky

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorMaterials scienceComposite numberCarbon nanotubeElectrodeColloidComposite materialNanotechnologyChemical engineeringElectrochemistryChemistry

Abstract

fetched live from OpenAlex

MXenes, such as Ti3C2Tx, are promising materials for electrodes of supercapacitors (SCs). Colloidal techniques have potential for the fabrication of advanced Ti3C2Tx composites with high areal capacitance (CS). This paper reports the fabrication of Ti3C2TX-Fe3O4-multiwalled carbon nanotube (CNT) electrodes, which show CS of 5.52 F cm−2 in the negative potential range in 0.5 M Na2SO4 electrolyte. Good capacitive performance is achieved at a mass loading of 35 mg cm−2 due to the use of Celestine blue (CB) as a co-dispersant for individual materials. The mechanisms of CB adsorption on Ti3C2TX, Fe3O4, and CNTs and their electrostatic co-dispersion are discussed. The comparison of the capacitive behavior of Ti3C2TX-Fe3O4-CNT electrodes with Ti3C2TX-CNT and Fe3O4-CNT electrodes for the same active mass, electrode thickness and CNT content reveals a synergistic effect of the individual capacitive materials, which is observed due to the use of CB. The high CS of Ti3C2TX-Fe3O4-CNT composites makes them promising materials for application in negative electrodes of asymmetric SC devices.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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