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Record W2742287241 · doi:10.1080/10426914.2017.1364850

Electrochemical deposition of polypyrrole–carbon nanotube films using steroid dispersants

2017· article· en· W2742287241 on OpenAlexaff
J. Li, Patrick Wojtal, Cameron J. Wallar, Igor Zhitomirsky

Bibliographic record

VenueMaterials and Manufacturing Processes · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceCarbenoxolonePolypyrroleElectrochemistryCarbon nanotubeChemical engineeringDispersantSalt (chemistry)PolymerizationDispersion (optics)NanotechnologyElectrodeComposite materialOrganic chemistryChemistryPolymer

Abstract

fetched live from OpenAlex

Polypyrrole (PPR)–carbon nanotube (CNT) films were prepared by an electrodeposition method, combining PPR electropolymerization and anaphoresis of CNT. PPR polymerization experiments showed advantages of a dopant from the catechol family for the deposition of PPR films at reduced electrode potentials. The method allowed the formation of adherent films on stainless steel substrates. The amphiphilic molecules with steroid-like structures, such as carbenoxolone disodium salt, glycyrrhizic acid, ammonium salt, and sodium taurodeoxycholate, were used for dispersion and charging of CNT. The new dispersing agents showed outstanding dispersion ability. In addition to dispersing properties, electrodeposition experiments revealed film-forming properties of carbenoxolone and ability to form pure carbenoxolone or carbenoxolone–CNT films. The PPR–CNT films formed using carbenoxolone disodium salt, glycyrrhizic acid ammonium salt, and sodium taurodeoxycholate showed diverse microstructural features. The dispersion and deposition mechanisms were discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2017
Admission routes1
Has abstractyes

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