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Record W4392877242 · doi:10.1038/s41598-024-56961-y

Fabrication of pore-filling cation-exchange membrane from waste polystyrene and Spunbond Meltblown Spunbond (SMS) non-woven polypropylene fabric as the substrate

2024· article· en· W4392877242 on OpenAlexaff
H. Asgari, Farideh Ghavipanjeh, Mohammad Reza Sabour, Daryoush Emadzadeh

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials sciencePolypropyleneMembraneComposite materialUltimate tensile strengthFabricationPolystyreneSubstrate (aquarium)NafionChemical engineeringPolymerChemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Commercial ion-exchange membranes are typically thick, possessing limited mechanical strength, and have relatively high fabrication costs. In this study, we utilize a three-layer polypropylene fabric known as Spunbond Meltblown Spunbond (SMS) as the substrate. This choice ensures that the resulting membrane exhibits high strength and low thickness. SMS substrates with various area densities, including 14.5, 15, 17, 20, 25, and 30 g/m2, were coated with different concentrations of waste polystyrene solution (ranging from 5 × 104 to 9 × 104 mg/l) before undergoing sulfonation using concentrated sulfuric acid. The physicochemical and mechanical properties of the membrane were characterized and compared with those of commercial Neosepta CMX and Nafion-117 cation-exchange membranes. Remarkably, the fabricated membrane exhibited good performance compared to commercial ones. The cation-exchange capacity (2.76 meq/g) and tensile strength (37.15 MPa) were higher, and the electrical resistance (3.603Ω) and the thickness (130 μm) were lower than the commercial membranes.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.235
Teacher spread0.225 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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