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Record W4414553623 · doi:10.1021/acsomega.5c05592

Dual-Nozzle Electrospinning for Janus Membranes in Membrane Distillation of Highly Saline and Oil-Contaminated Waters

2025· article· en· W4414553623 on OpenAlexaff
Michaela Olisha S. Lobregas, Ratthapol Rangkupan, Hsiu‐Po Kuo, Yuming Tu, Chalida Klaysom

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsInstitute of Particle Physics
FundersChulalongkorn University
KeywordsMembraneMembrane distillationElectrospinningJanusPorosityFoulingLayer (electronics)PermeationSuperhydrophilicity

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Electrospun nanofibrous membranes are highly valued for membrane distillation (MD) due to their inherent porosity and tunable structure. This study introduces a novel dual-nozzle electrospinning method to fabricate a multilayered Janus membrane, combining an omniphobic layer of PVDF fibers and fluorinated TiO 2 -PVDF microclusters with a hydrophilic layer of hydrolyzed PMA fibers. The resulting membrane demonstrated exceptional performance and durability. In a 21 h MD operation against oily saline solutions, it retained 80% of its initial flux while producing high-purity water (conductivity <5 μS/cm). Notably, the membrane exhibited complete underwater oil repellence, preventing fouling from oil droplet adhesion. Furthermore, in a 7 h test with highly saline feed, the membrane maintained 99% of its initial flux and permeate conductivity below 60 μS/cm. The success of the membrane is attributed to the dual-nozzle setup, which provides both improved processing efficiency and strong interlayer adhesion, enhancing structural integrity during prolonged MD operations and cleaning cycles. This work presents a robust and scalable method for fabricating high-performance Janus membranes, offering a significant advancement for treating challenging, oil-contaminated water via membrane distillation.

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 score0.022
Threshold uncertainty score0.466

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 head0.243
Teacher spread0.236 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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