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Record W2518671699 · doi:10.1002/cjce.22679

Preparation, characterization, and application of vertically aligned CNT sheets through template assisted pyrolysis of PBI‐Kapton

2016· article· en· W2518671699 on OpenAlexvenueno aff
Hamed Azami, Mohammadreza Omidkhah

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposite Synthesis and Irradiation
Canadian institutionsnot available
FundersIran Nanotechnology Initiative Council
KeywordsMaterials scienceKaptonCrystallinityCarbon nanotubeNanocompositeGraphitePyrolysisChemical engineeringAnodizingComposite materialNanotechnologyAluminiumPolyimideLayer (electronics)

Abstract

fetched live from OpenAlex

This article investigates the synthesis of vertically aligned carbon nanotube (CNT) sheets through the pyrolysis of polybenzimidazole (PBI)‐Kapton inside the pores of anodized aluminium oxide (AAO). The nanocomposites of CNT/Alumina obtained were characterized by several techniques. It was found that the resulting carbon structures presented higher crystallinity compared with graphite. All of the CNT sheets had high antibacterial activity that was proportional to degree of CNT crystallinity. Moreover, the desalination of salty water using CNT sheets was performed. The results demonstrated that the vertically aligned CNT sheets could be used as an effective adsorbent for salty water desalination due to their very high adsorption capacity without CNT leakage into water.

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 score0.002

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 integrity0.0000.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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