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Record W2324082659 · doi:10.1021/nn400768p

Fabrication of Three-Dimensional Carbon Nanotube and Metal Oxide Hybrid Mesoporous Architectures

2013· article· en· W2324082659 on OpenAlexafffund
Mahyar Mazloumi, Samaneh Shadmehr, Yverick Rangom, Linda F. Nazar, Xiaowu Tang

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMesoporous materialCarbon nanotubeNanotechnologyFabricationOxideElectrodeGrapheneNanotubeCrystallinityPorositySupercapacitorHybrid materialCapacitanceChemical engineeringComposite materialCatalysis

Abstract

fetched live from OpenAlex

Three-dimensional (3D) vertically aligned carbon nanotube (CNT) patterns were utilized as templates for fabricating mesoporous hybrid architectures composed of CNTs and various crystalline metal oxide (MO; M = Co, Zn, Mn) nanoparticles by a microwave-assisted chemical approach. Post-synthesis thermal treatment of the CNT/MO patterns culminated in structural reorganization, depending on the treatment conditions. In air, CNTs were removed by oxidation. The remaining MO architectures preserved the shape and alignment of the original 3D CNT patterns, but with different porosity characteristics and improved MO crystallinity. Elastocapillary condensation and bending were demonstrated to be useful tools for further architecture alternation. The mesoporous nature of the CNT/MO hybrids and the MO materials were confirmed by N2-BET measurements. CNT/Co3O4 aligned strips were used as an example to demonstrate the potential application of the CNT/MO architectures as electrode materials for supercapacitive storage. Galvanostatic measurements showed that the CNT/Co3O4 strips were stable up to 1000 charge-discharge cycles at a current density of 377 μA/cm(2) with a specific capacitance as high as 123.94 F/g.

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)0.000
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 score0.034
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations73
Published2013
Admission routes2
Has abstractyes

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