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Record W2067774762 · doi:10.1109/ams.2010.83

Modeling the Rejection Performance of Hollow Fiber Nanofiltration Membranes Modified by Negatively Charged-Modifying Macromolecule

2010· article· en· W2067774762 on OpenAlexaff
Nurmin Bolong, Ahmad Fauzi Ismail, Mohd Razman Salim, Dipak Rana, Takeshi Matsuura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
FundersUniversiti Malaysia SabahUniversiti Teknologi Malaysia
KeywordsMembraneSulfonateNanofiltrationPhase inversionSteric effectsMacromoleculeChemical engineeringCarboxylateMaterials scienceFiberPolyelectrolyteChemistryPolymer chemistrySodiumPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Hollow fiber membranes using Polyethersulfone (PES) were fabricated in-house using phase inversion technique by modification with synthesized charged-surface modifying macromolecules (cSMM). The cSMM comprise with end-group component of Hydroxybenzene sulfonate or Hydroxybenzene carboxylate. The electrical properties of the membranes were modeled by utilizing the combination of irreversible thermodynamic model, Steric-Hindrance Pore (SHP) model and Teorell-Meyer-Sievers (TMS) model. The negatively-charged of the modified hollow fiber membranes was calculated based on sodium chloride rejection performance. The analysis of the modeling results revealed that sulfonate induce negative 1.61 electrical properties compared to carboxylate that is negative 1.49 for the modified PES 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.232
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations1
Published2010
Admission routes1
Has abstractyes

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