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Record W2162133421 · doi:10.7202/705430ar

Nanofiltration de solutions de nitrate d'ammonium. Etude des paramètres influents

2005· article· fr· W2162133421 on OpenAlexaff
Lydie Paugam, Samir Taha, J. Cabon, Nicolas Gondrexon, G. Dorange

Bibliographic record

VenueRevue des sciences de l eau · 2005
Typearticle
Languagefr
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChemistryHumanitiesPhysicsNanofiltrationMembranePhilosophy

Abstract

fetched live from OpenAlex

Cet article propose une étude de la rétention du nitrate d'ammonium par une membrane commerciale de nanofiltration (Nanomax 50). Les effets de la pression, de la concentration et de la vitesse d'écoulement tangentiel ont été étudiés avec le souci d'une meilleure compréhension du mécanisme de transport des ions nitrate et en vue d'une optimisation de la rétention. Le taux de rétention des ions nitrate augmente dans un premier temps avec la pression, atteint un maximum puis diminue. La rétention, pour des pressions élevées, peut cependant être améliorée en augmentant la vitesse d'écoulement tangentiel. La séparation résulterait d'un rapport de différentes forces : une force d'entraînement radial dans le pore (illustrée par l'effet de la pression transmembranaire), une force d'entraînement tangentiel vers le rétentat (illustrée par l'effet de la vitesse d'écoulement tangentiel) et une force de surface traduisant les interactions membrane-soluté (illustrée par l'effet de la concentration). L'équation de Spiegler et Kedem est proposée en première approche de modélisation pour une valeur limite de pression.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.007
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.104
GPT teacher head0.327
Teacher spread0.223 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

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