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Record W2720351797 · doi:10.5004/dwt.2017.20735

Structure and properties of membrane at different ages in drinking water treatment

2017· article· en· W2720351797 on OpenAlexaff
Lun‐Feng Cui, Zhe Feng, Carl R. Goodwin, Weimin Gao, Baoqiang Liao

Bibliographic record

VenueDesalination and Water Treatment · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsThunder Bay Regional Health Sciences CentreLakehead University
Fundersnot available
KeywordsMembraneMembrane foulingPorosityFoulingScanning electron microscopePermeability (electromagnetism)Membrane permeabilityMembrane structureChemical engineeringMaterials scienceChemistryFiltration (mathematics)Composite materialBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Effects of membrane aging and chemical cleaning on hollow fiber (HF) membrane properties (morphology, permeability, porosity, hydrophilicity, and physical strength) were systematically investigated by using membrane samples at different ages from a full-scale drinking water membrane filtration plant. Scanning electron microscopic images showed that the extent of fouling on membrane surfaces increased and the diameter of HF membrane decreased with membrane age extended. Membrane permeability, porosity, and break strength were also decreased with an increase in membrane age. Dextran rejection results and scanning electron microscope images showed the same trend in a decrease in membrane pore size with the membrane operational time extended. Chemical cleaning studies showed that organic fouling was important fouling mechanism. The membrane permeability, porosity, and hydrophilicity improved after chemical cleaning. The deterioration of membrane performance with an increase in membrane age correlated well with the changes in membrane properties and fouling.

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 score0.045
Threshold uncertainty score0.289

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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
Published2017
Admission routes1
Has abstractyes

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