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Record W4388478855 · doi:10.1021/acsaenm.3c00550

Superhydrophilic Electrospun Polyamide-Imide Membranes for Efficient Oil/Water Separation under Gravity

2023· article· en· W4388478855 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Engineering Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of CanadaCanada's Oil Sands Innovation AllianceUniversity of Alberta
KeywordsMembraneElectrospinningMaterials sciencePolyamideChemical engineeringSuperhydrophilicityPorosityEmulsionFoulingFiltration (mathematics)PolymerPolymer chemistryComposite materialContact angleChemistry

Abstract

fetched live from OpenAlex

The efficient separation of oil/water mixtures by membrane technology is highly dependent on the performance of membrane materials. The emergence of electrospinning has opened up opportunities for designing super porous structures with excellent oil/water separation efficiency. Herein, we prepared electrospun nanofibrous membranes using a highly hydrophilic and thermomechanically stable polyamide-imide (PAI) polymers. The fabricated membranes by a set of optimized electrospinning processes were examined in the gravity-driven separation of various oil/water emulsions. The effect of polymer concentration on fiber diameter and porosity of membranes was evaluated. It was observed that increasing the PAI concentration in the electrospinning dope resulted in the fabrication of membranes with larger fibers and a larger pore size. The fiber diameter and thus, the porosity and pore size of the membrane influenced the flux and separation efficiency during filtration. The membrane with a 12% PAI concentration (EM1) showed a lower flux, whereas the 21% PAI membrane (EM4) offered a higher flux but lower separation efficiency. The membranes exhibited excellent underwater antioil adhesion behavior by completely repelling n -hexane, mineral oil, n -hexadecane, and gasoline droplets from their prewetted surfaces. Compared to other membranes, the 15% PAI membrane (EM2) was found to strike a balance between water flux and oil rejection while also exhibiting strong resistance to oil fouling, as evidenced by its relatively lower flux decline (FD) and higher flux recovery ratio (FRR) when filtering various emulsions. The EM2 membrane demonstrated an excellent pure water flux of 800 to 900 L/m 2 h (LMH) and an emulsion flux of 380 to 410 LMH. The FRR and oil rejection of the membrane were 91–97 and >99%, respectively. Moreover, the prepared membrane was stable at pH 3 to 9 with a consistent separation performance. The EM2 membrane was also utilized for n -hexane/water emulsion separation in cyclic tests and provided stable oil rejection and FRR after 10 cycles, demonstrating the high potential of this membrane in real oil/water emulsion separation processes.

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.

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), Insufficient payload (model declined to judge)
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 score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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