MétaCan
Menu
Back to cohort
Record W1963685850 · doi:10.1021/la902284k

Bilayered Self-Oriented Membrane Fouling and Impact of Magnesium on CaCO<sub>3</sub> Formation during Consecutive Electrodialysis Treatments

2009· article· en· W1963685850 on OpenAlexaff
Christophe Casademont, Gérald Pourcelly, Laurent Bazinet

Bibliographic record

VenueLangmuir · 2009
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsElectrodialysisFoulingMagnesiumMembrane foulingMembraneChemical engineeringCaco-2Materials scienceChemistryChromatographyMetallurgyBiochemistryEngineering

Abstract

fetched live from OpenAlex

Fouling of membrane is the major scientific lock for electromembrane process intensification limiting their applications. The fouling evolution on ion-exchange membranes was monitored during three consecutive electrodialysis treatments of a solution containing a high magnesium/calcium ratio. Following these experiments, we proposed a mechanism to explain the change in fouling nature on the CEM from a mix of calcite, brucite, and portlandite after the first ED run to a predominant amorphous Mg(OH)(2) after the third run and the formation of calcite cubic crystal on the AEM, although Mg(2+), an inhibitor of CaCO(3) formation, was present in the solution. It was also demonstrated that the nature and structure of the AEM and CEM foulings formed were self-oriented by the formation of the CEM first layer of fouling appearing during the first run. Our findings have implications for electromembrane process fouling control as well as in the understanding of CaCO(3) crystallization phenomena.

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)0.0000.000

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueLangmuirSame topicMembrane-based Ion Separation TechniquesFrench-language works237,207