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Record W1950035747 · doi:10.1002/mame.201400361

Investigation of Chaotic Mixing for MWCNT/Polymer Composites

2015· article· en· W1950035747 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Materials and Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsMicromolding Solutions (Canada)University of Calgary
Fundersnot available
KeywordsMaterials scienceMixing (physics)Composite materialCarbon nanotubeChaoticNanocompositeElectromagnetic shieldingChaotic mixingEMIElectromagnetic interferencePolymerElectronic engineeringComputer science

Abstract

fetched live from OpenAlex

A chaotic mixer is developed and optimized in order to overcome challenges associated with mixing polymers with high aspect ratio nano‐particulates. The chaotic mixing system utilizes two cylindrical rotors to uniformly mix multi‐walled carbon nanotubes (MWCNTs) with a thermoplastic. Results of the electrical conductivity and electromagnetic interference (EMI) shielding effectiveness of the chaotic mixed nanocomposites were higher than ones mixed via a commercial HAAKE mixer. MWCNTs’ length was investigated and it was observed that the MWCNTs in chaotic mixed nanocomposites are longer compared to HAAKE mixer. To investigate the effects of MWCNTs’ length on the electrical properties, a 3D electrical model based on random walk method was developed and examined. Obtained results suggest that the chaotic mixer has a higher potential for mixing nano particulates into thermoplastics without breaking the nanotubes and improved electrical properties, compared to other types of melt mixing techniques.

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.

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 score0.007
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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