MétaCan
Menu
Back to cohort
Record W4311688203 · doi:10.3390/membranes12121270

Novel Polyelectrolyte-Based Draw Solute That Overcomes the Trade-Off between Forward Osmosis Performance and Ease of Regeneration

2022· article· en· W4311688203 on OpenAlexaff
Daryoush Emadzadeh, Amirsajad Atashgar, Boguslaw Kruczek

Bibliographic record

VenueMembranes · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsForward osmosisAqueous solutionMembraneDistilled waterChemical engineeringPolyelectrolyteUltrafiltration (renal)WastewaterSeawaterBrackish waterDesalinationMaterials scienceMonomerReverse osmosisChemistryChromatographyPolymerEnvironmental engineeringOrganic chemistryEnvironmental scienceSalinity

Abstract

fetched live from OpenAlex

Forward osmosis (FO) is an emerging technology for seawater and brackish desalination, wastewater treatment, and other applications, such as food processing, power generation, and protein and pharmaceutical enrichment. However, choosing a draw solute (DS) that provides an appropriate driving force and, at the same time, is easy to recover, is challenging. In this study, water-soluble poly(styrene sulfonate) (PSS) was modified by a high-electrical-conductivity 3,4-ethylenedioxythiophene (EDOT) monomer to fabricate a novel draw solute (mPSS). FO tests with the CTA membrane in the active layer facing the feed solution (AL-FS) orientation, using a 50 mS/cm aqueous solution of synthesized solute and distilled water as a feed solution exhibited a water flux of 4.2 L h−1 m−2 and a corresponding reverse solute flux of 0.19 g h−1 m−2. The FO tests with the same membrane, using a 50 mS/cm NaCl control draw solution, yielded a lower water flux of 3.6 L h−1 m−2 and a reverse solute flux of 4.13 g h−1 m−2, which was more than one order of magnitude greater. More importantly, the synthesized draw solute was easily regenerated using a commercial ultrafiltration membrane (PS35), which showed over 96% rejection.

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 score0.144
Threshold uncertainty score0.563

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 studies0.0010.000
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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMembranesSame topicMembrane Separation TechnologiesFrench-language works237,207