MétaCan
Menu
Back to cohort
Record W2002534939 · doi:10.1021/bm060655d

Conformational Changes and Aggregation of Alginic Acid as Determined By Fluorescence Correlation Spectroscopy

2006· article· en· W2002534939 on OpenAlexaff
Fabrice Avaltroni, Marianne Seijo, Serge Ulrich, Serge Stoll, Kevin J. Wilkinson

Bibliographic record

VenueBiomacromolecules · 2006
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsChemistryRadius of gyrationFluorescence correlation spectroscopyPersistence lengthMolar massIonic strengthDissociation (chemistry)DiffusionHydrodynamic radiusAnalytical Chemistry (journal)FluorescenceFluorescence spectroscopyMonomerPolymerPhysical chemistryChromatographyAqueous solutionThermodynamicsMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

Insight into the conformations and aggregation of alginic acid was gained by measuring its diffusion coefficient at very dilute concentrations using fluorescence correlation spectroscopy. Both the pH and ionic strength (I) had an important influence on the diffusion coefficient of the polysaccharide. For pH, three effects were isolated: (i) below pH 4, the charge density decreased causing increased aggregation; (ii) between pH 4 and 8, a molecular expansion was observed with increasing pH, whereas (iii) above pH 8 some dissociation of the polymer was observed. Increasing I from 0.001 to 0.1 M resulted in a ca. 20% increase in the diffusion coefficient. By coupling these measurements to molar mass determinations obtained by size exclusion chromatography and monomer size estimations determined from ab initio calculations, it was possible to determine the radii of gyration via de Gennes renormalization theory. From diffusion coefficients and radii of gyration obtained as a function of ionic strength, persistence lengths (total, electrostatic, and intrinsic) were calculated from the Benoit-Doty relationship.

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 score0.005
Threshold uncertainty score0.431

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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

Citations34
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueBiomacromoleculesSame topicSurfactants and Colloidal SystemsFrench-language works237,207