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Record W3123843274 · doi:10.1002/app.50511

Feasibility of using polycarbonate as a substrate of thin film composite membrane in forward osmosis

2021· article· en· W3123843274 on OpenAlexaff
Z. Alihemati, Seyed Abdollatif Hashemifard, Takeshi Matsuura, Ahmad Fauzi Ismail

Bibliographic record

VenueJournal of Applied Polymer Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
FundersUniversiti Teknologi MalaysiaIran National Science Foundation
KeywordsMaterials scienceMembraneThin-film composite membranePolycarbonateContact angleComposite materialForward osmosisPorositySubstrate (aquarium)Permeability (electromagnetism)Composite numberChemical engineeringPolymerPolymer chemistryReverse osmosisChemistry

Abstract

fetched live from OpenAlex

Abstract In the present study, thin film composite (TFC) forward osmosis (FO) membranes with polycarbonate (PC (and polyether sulfone (PES) as substrates were fabricated to investigate the impact of the structural parameters of substrate on the performance of the membranes. Firstly, the substrates were prepared by Loeb‐Sourirajan method. Characterization techniques including FESEM, contact angle measurement, pure water flux, gas permeability test, and tensile test were applied to investigate the properties of the substrates. After preparing suitable substrates, active layers were fabricated via interfacial polymerization (IP) technique. The performance and characterization test showed that PC is a relatively hydrophilic polymer with a good property for using as a substrate of FO TFC membrane but as the result of gas permeability test show, this membrane has large surface pore size in comparison with PES membrane. Mean pore size of PC and PES membrane is 378 and 139 nm, respectively. Also, the results show that the effective surface porosity of PC (285, 1/m) is more than PES (213, 1/m) substrate.

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)0.001
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 score0.009
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.280
Teacher spread0.257 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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