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Record W3113159991 · doi:10.1177/0021998320977643

Highly conductive multi-walled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) nanocomposite for microfluidic applications

2020· article· en· W3113159991 on OpenAlexaff
Nasim Jan Mohammadi Dashtaki, Amir Hossein Nassajpour-Esfahani, Morteza Bayareh, Pouya Rezai, Ali Doostmohammadi

Bibliographic record

VenueJournal of Composite Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsYork University
Fundersnot available
KeywordsMaterials scienceNanocompositeCarbon nanotubeComposite materialPolydimethylsiloxaneComposite numberElectrical conductorScanning electron microscopeElectrical resistivity and conductivityUltimate tensile strengthMicrofluidicsNanotechnology

Abstract

fetched live from OpenAlex

Conductive materials are required for sensing and actuation purposes in microfluidic devices. Electrical and mechanical properties of aligned CNTs/PDMS nanocomposites fabricated in AC and quasi-AC electric fields were measured. Field emission scanning electron microscope and elemental mapping were used to evaluate the microstructural properties of fabricated specimens. Results showed that nanocomposite properties were dependent on CNT concentration. A homogenized and nonagglomerated composite was obtained using a quasi-AC electric field with the voltage of 1075 V zero to peak at a frequency of 100 Hz and current of 35 milliamps when applied to suspension for 2 hours at 80 °C. The aligned nanocomposite with 1 wt% of CNT exhibited an electrical conductivity, Young’s modulus, and tensile strength of 10-6 S/cm, 7.23 MPa, and 1.02 MPa, respectively.

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 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 score0.014
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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