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Record W4412994076 · doi:10.1002/aic.70025

Engineering and scale‐up of pervaporation membrane with an intermediate <scp>PEBA</scp> layer and an optimized <scp>PDMS</scp> layer

2025· article· en· W4412994076 on OpenAlexaff
Danyang Song, Jie Li, Peng Cai, Yike Wang, Jiashu Liu, Naixin Wang, Hong Meng, Xianshe Feng

Bibliographic record

VenueAIChE Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPervaporationLayer (electronics)Scale (ratio)MembraneMaterials scienceChemistryNanotechnologyPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract The fabrication and scale‐up of pervaporation composite membranes are often challenged by pore penetration into microporous substrate. This study presents an innovative composite membrane featuring an intermediate poly(ether‐block‐amide) (PEBA) layer formed at a liquid–liquid interface, effectively preventing pore penetration and enabling uniform deposition of the selective polydimethylsiloxane (PDMS) layer. The PDMS layer was precisely controlled through dynamic monitoring of the membrane solution droplets spreading, optimizing the PDMS spreading thermodynamic and kinetic parameters involved. SEM confirmed the dense top‐layer structure of the PDMS‐PEBA/polytetrafluoroethylene (PTFE) membrane, with controllable thicknesses of individual layers. The appropriate thicknesses for the PEBA and PDMS layers were investigated through both the resistance model analysis and pervaporation test results. Based on this trilayer structure, a scale‐up 600 cm 2 PDMS‐PEBA/PTFE membrane demonstrated a separation factor of 22.4 and a flux of 1.9 kg/m 2 /h for concentrating n ‐butanol (60°C, 1 wt.% n ‐butanol/water), highlighting its potential for industrial applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.987

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 communication0.0000.001
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

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