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Record W2090731528 · doi:10.1002/elps.200700906

Enzymatic membranes for the selective transport of neutral molecules by electrophoresis

2008· article· en· W2090731528 on OpenAlexaff
Bernard Perrin, Roger Couturier, Koffi Fiaty, Catherine Charcosset, Bernard Maı̈sterrena

Bibliographic record

VenueElectrophoresis · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsMembraneDephosphorylationElectrophoresisChemistrySubstrate (aquarium)BiochemistryGlycerolHexokinaseAlkaline phosphataseGlycerol kinasePhosphataseGel electrophoresisEnzymePhosphorylationChromatographyBiophysicsBiologyGlycolysis

Abstract

fetched live from OpenAlex

The active and selective transport of glucose and glycerol was carried out using electrophoresis and artificial enzymatic membranes. These positively charged composite membranes carry, on the face adjacent to the donor compartment of an electrophoresis module, a specific kinase (hexokinase or glycerokinase) and, on the opposite face, an alkaline phosphatase (ALP). Phosphorylation of the neutral substrate (glucose or glycerol) on the donor side by the kinase generates a negatively charged phosphorylated substrate, whose transmembrane migration is promoted by an electric field and by the membrane's positive charge. Dephosphorylation of the phosphorylated substrate by ALP on the opposite face regenerates the neutral substrate, which accumulates in the receiver compartment of the electrophoresis module. Using an electrophoresis module specifically designed for this study, our experiments were carried out enabling glucose and glycerol to be concentrated approximately eight- and twelve-fold, respectively, in 8 h.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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
Published2008
Admission routes1
Has abstractyes

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