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Record W2318133792 · doi:10.1021/jp212343g

Diffusion and Partitioning of Cations in an Agarose Hydrogel

2012· article· en· W2318133792 on OpenAlexaff
Mahmood Golmohamadi, Thomas A. Davis, Kevin J. Wilkinson

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAgaroseDiffusionChemistryChemical engineeringChemical physicsMaterials scienceBiophysicsChromatographyThermodynamicsBiologyPhysicsEngineering

Abstract

fetched live from OpenAlex

Self- and mutual diffusion were measured in a low-melt 1.5% agarose hydrogel as a function of ionic strength (0.1-100 mM) and pH (3-7) for Cd(2+) and a charged rhodamine derivative. Self-diffusion was measured by fluorescence correlation spectroscopy, whereas mutual diffusion was evaluated using a diffusion cell. In contrast to the results observed for the diffusion cell, self-diffusion of rhodamine 6G increased from 50 to 90% of that found in water as the ionic strength increased from 0.1 to 100 mM (pH = 6). The combined observations of decreasing diffusive flux in parallel with an increasing diffusion coefficient were attributed to the gel's Donnan potential. Donnan potentials obtained voltammetrically using a Au amalgam microelectrode varied from -30 to 0 mV as the ionic strength increased from 0.1 to 100 mM (pH = 6). At the low ionic strengths, Donnan potentials of this magnitude accounted for a 13× enhancement of Cd(2+) concentrations in the hydrogel, which was consistent with measurements obtained by a nitric acid extraction of the gel (15×) and able to explain the apparent discrepancy between mutual and self-diffusion measurements. The overall diffusion of the positively charged substrates decreased as the pH was decreased from 12 to 3.

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 score0.002
Threshold uncertainty score0.196

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations42
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry ASame topicHydrogels: synthesis, properties, applicationsFrench-language works237,207