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Record W4385429168 · doi:10.1080/25740881.2023.2237101

Towards a More Conductive and Stronger Cellulose-CNTs Composite Film through Controlling the Texture

2023· article· en· W4385429168 on OpenAlexaff
Hamed Arab, Mohammad Ebrahim Karkhanehchin, Maryam Mokhtarifar, Daria C. Boffito, Majid Baniadam, Morteza Maghrebi

Bibliographic record

VenuePolymer-Plastics Technology and Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSonicationCarbon nanotubeDispersion (optics)Materials scienceSuspension (topology)Composite numberComposite materialViscosityChemical engineeringCellulose

Abstract

fetched live from OpenAlex

Dispersion state of carbon nanotubes (CNTs) in cellulose nanofibrils (CNFs) film at variant sonication time in the presence of sodium dodecyl sulfate (SDS) has been studied in this work. The sonication was extended up to 120 min, and the CNF-CNTs-SDS suspension’s viscosity was measured at different times. Overall, viscosity of CNF-CNTs-SDS suspension decreased continuously as sonication continued except for the period of 60–75 min, where an increase in the viscosity was observed. Based on morphological investigations, the partial dispersion of particles at the early stage of sonication (i.e. up to 60 min) was followed by coagulation or reagglomeration of the particles during 60–75 min and increasing sonication time by 120 min led to improved dispersion of CNTs in CNFs. Likewise, the electrical conductivity and tensile strength obeyed the same trend due to improved CNTs-CNFs film’s network (30–60 and 75–120 min) and the corresponding reduction (60–75 min) caused by the partial agglomeration of CNT and CNF moieties. These findings highlight that sonication time is not a straightforward parameter and should be elaborated to reach the desired dispersion.

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 score0.007
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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