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Record W2546814243 · doi:10.1021/acs.iecr.6b03117

Zeolite ZSM5-Filled PVDF Hollow Fiber Mixed Matrix Membranes for Efficient Carbon Dioxide Removal via Membrane Contactor

2016· article· en· W2546814243 on OpenAlexaff
M. Rezaei-DashtArzhandi, Ahmad Fauzi Ismail, Pei Sean Goh, Ihsan Wan Azelee, M. Abbasgholipourghadim, Ghani Ur Rehman, Takeshi Matsuura

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Ottawa
FundersUniversiti Teknologi Malaysia
KeywordsPermeanceMembraneMaterials scienceWettingChemical engineeringAbsorption (acoustics)PorosityContact angleMass transferSurface roughnessAbsorption of waterPermeationComposite materialChromatographyChemistry

Abstract

fetched live from OpenAlex

ZSM5 zeolite-filled PVDF mixed matrix membranes (MMMs) were wet spun and used for CO 2 absorption in a contactor system. The properties of ZSM5 were analytically characterized. SEM images revealed the fully asymmetric structure of membranes, in which the creation of finger-like macrovoids was promoted with increasing filler loading. A significant increase in gas permeance was observed, which was associated with the porosity increase of the membrane surface despite the decrease in surface pore size. The surface roughness, wettability resistance, and mechanical stability of membranes were also considerably improved by filler loading. A CO 2 absorption test with water revealed a higher CO 2 flux of MMMs than that of the plain membrane. Peak absorption flux of 5.80 × 10 –3 mol m –2 s –1 was achieved at a liquid velocity of 1.2 m s –1 for 5 wt % ZSM5/PVDF membrane (MZ5), which was nearly 177% higher than that of neat PVDF and also surpassed that of several commercial and in-house made membranes. The mass transfer resistance of the MMMs was also considerably lower than that of PVDF.

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)0.001
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 score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.296
Teacher spread0.251 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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