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Record W1914015691 · doi:10.1002/ppap.201500040

Nanofluids Containing MWCNTs Coated with Nitrogen-Rich Plasma Polymer Films for CO<sub>2</sub>Absorption in Aqueous Medium

2015· article· en· W1914015691 on OpenAlexaff
Larissa Jorge, Sylvain Coulombe, Pierre‐Luc Girard‐Lauriault

Bibliographic record

VenuePlasma Processes and Polymers · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceChemical engineeringAqueous solutionPolymerCarbon nanotubeAbsorption (acoustics)NitrogenLayer (electronics)NanofluidNanotechnologyOrganic chemistryChemistryComposite materialNanoparticle

Abstract

fetched live from OpenAlex

Amine-functionalized multi-walled carbon nanotubes (MWCNTs) dispersed in water are investigated as CO2 absorbents. MWCNTs grown by chemical vapor deposition on stainless steel meshes form open forests that can be coated via a RF capacitively coupled glow discharge. When treating the MWCNTs in an atmosphere containing either pure ammonia or mixtures of ammonia and ethylene for 5 min, grafting of nitrogen functional groups or deposition of a nitrogen-rich plasma polymer layer occurs. In particular, for a 1:1 mixture, a 10 nm thick plasma polymer layer coats the MWCNTs. This layer contains about 19 N at%, and 12% of these nitrogen atoms are nucleophilic sites (such as amines) that may react with CO2. These functional groups not only enhance the absorption of CO2, but also increase the hydrophilic character of the MWCNTs, allowing them to stay suspended in water for at least three months at room temperature. The CO2 absorption capacity of this nanofluid is 36% higher than that of water, with a MWCNT concentration of about 40 mg L−1.

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)0.0010.000

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 head0.013
GPT teacher head0.214
Teacher spread0.200 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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