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Study of Membrane Transport for Hemodiafilter and Reverse Osmosis Module

2013· article· en· W2153929549 on OpenAlexvenueno aff
Masaaki Sekino

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsReverse osmosisConcentration polarizationMembraneChemistryMass transferForward osmosisDesalinationOsmosisMass transfer coefficientThermodynamicsMechanicsPhysicsChromatography

Abstract

fetched live from OpenAlex

As a typical membrane technology by which the solute substances are separated from a solution, hemodiafilter and reverse osmosis module are studied, in particular regarding the solute transport through the membrane. The solute transport equation, as a key point of this paper, could be derived from the modified Kedem-Katchalsky equation combined with Film theory model. As the next key point for the numerical analysis, the solute transport equation was inserted into the previous module model, and the differential equations composing this module model were converted to the difference equations calculated with a nonlinear secant method. Then the solute concentration profiles of the membrane and the boundary layers in the hemodiafilter and the reverse osmosis module were obtained to visually illustrate in Figures 3 to 6. Finally, it was clear that the occurrence of concentration polarization in the boundary layer is dependent on values of the membrane parameters, including solute permeability (Pm) and reflection coefficient (σ), the ultrafiltration flux (Jv) and the mass transfer coefficient (k).

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 score0.003
Threshold uncertainty score0.005

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 communication0.0000.001
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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