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Record W4315647605 · doi:10.1002/cjce.24836

Investigation of the effect of functionalized carbon nanotube and graphene oxide in guar gum‐based mixed matrix membrane for gas separation application

2023· article· en· W4315647605 on OpenAlexvenueno aff
Neha Pal, Madhu Agarwal

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneFourier transform infrared spectroscopyChemical engineeringMaterials scienceGrapheneCarbon nanotubeGas separationPolymerScanning electron microscopeOxideMatrix (chemical analysis)Polymer chemistryNanotechnologyChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The present study deals with preparing mixed matrix membranes (MMMs), a new polysaccharide‐based natural polymer used as a matrix with functionalized carbon nanotubes (FCNTs) and graphene oxide (GO) used as an inorganic filler. This work identified the effect of the inorganic fillers (FCNTs or GO) with naturally occurring polymer for gas separation. The incorporation of fillers improves the gas separation performance of MMMs. In GG/FCNTs MMMs, the selectivities of CO 2 /N 2 and CO 2 /H 2 were enhanced by 55.24% and 57.89%, respectively. Moreover, in GG/GO MMMs, the selectivities of CO 2 /N 2 and CO 2 /H 2 were improved by 99.50% and 50%, respectively. The membrane was characterized by scanning electron microscopy (SEM) and Fourier‐transform infrared spectroscopy (FTIR). The SEM analysis of GG/GO MMMs reveals layered structure, and GG/FCNTs MMMs create passages to transport gases. The Universal testing machine (UTM) is used to analyze the mechanical properties of pristine and modified membranes.

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 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 score0.090
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

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 head0.007
GPT teacher head0.207
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 teacher head, 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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