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Record W4280505686 · doi:10.3390/ma15103537

A Low-Cost Porous Polymer Membrane for Gas Permeation

2022· article· en· W4280505686 on OpenAlexaff
Selim Haouari, Denis Rodrigue

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMembraneBarrerPermeationMaterials scienceChemical engineeringCalenderingGas separationPolymerExtrusionPorosityLeaching (pedology)Polymer chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

In this work, an efficient technique was used to produce porous membranes for different applications. Polyethylene (PE) was selected for the matrix, while corn starch (CS) was used to create the porous structure via leaching. The membranes were produced by continuous extrusion (blending)–calendering (forming) followed by CS leaching in a 20% aqueous acetic acid solution at 80 °C. A complete characterization of the resulting membranes was performed including morphological and mechanical properties. After process optimization, the gas transport properties through the membranes were determined on the basis of pure gas permeation including CH4, CO2, O2, and N2 for two specific applications: biogas sweetening (CH4/CO2) and oxygen-enriched air (O2/N2). The gas separation results for ideal permeability and selectivity at 25 °C and 1.17 bar (17 psi) show that these membranes are a good starting point for industrial applications since they are low-cost, easy to produce, and can be further optimized.

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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