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Concentration-dependent sedimentation and diffusion coefficient in analytical ultracentrifugation experiments

2019· article· en· W4400576686 on OpenAlexaff
Maximilian J. Uttinger, Simon E. Wawra, Johannes Walter, Wolfgang Peukert

Bibliographic record

VenueDiffusion fundamentals. · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsAnalytical UltracentrifugationSedimentationSedimentation coefficientDiffusionUltracentrifugeChromatographyChemistryThermodynamicsGeologyPhysicsGeomorphologyBiochemistry

Abstract

fetched live from OpenAlex

Experimental data of analytical ultracentrifugation (AUC) experiments is defined by sedimentation and diffusion transport of molecules in solution.While the sedimentation properties define the position of the measured sedimentation boundaries, information on the diffusion is included in the broadening.Both effects are then analysed with well-established finite element solutions of Lamm's equation The results from this multidimensional analysis provide e.g.molecular mass or size distributions or information on core-shell structures [1,2].Moreover, it is known that the properties of macromolecules and particles in AUC experiments are influenced by concentration-dependent sedimentation and diffusion coefficients, which are described by two interaction terms k s and BM.While the Gralen coefficient k s represents hydrodynamic interactions, the second virial coefficient B is a thermodynamic quantity and is used to correct for non-ideal diffusional properties throughout AUC experiments [3].Here, we show that by analyzing these parameters via AUC, information of the global interaction of particles in solution can be retrieved.Hence, the second virial coefficient was determined for a lysozyme model system as a function of the pH of the solution from AUC experiments Figure 1 (left) presents the retrieved values for BM from the AUC measurements as black bars.It can be concluded that the interaction term BM is a measure for stability and the tendency of the system to aggregate as it follows the trend of the zetapotential representing charge-charge interactions.Theoretical considerations from DLVO theory show that with increasing pH of the solution, the contribution from an electrostatic potential decreases and thus predict a decreasing interaction term with increasing pH, as can be seen in Figure 1 (right).This approach paves the way for a direct correlation between global interaction potentials in solution and parameters obtained from AUC experiments.Figure 1: Left: Second virial coefficient from SV AUC experiments (grey bars), osmometric measurements (red bars) alongside the Gralen-coefficients (Grey bars).Right: Second virial coefficient from DLVO theory (black bars) alongside the values from SV-AUC experiments (red bars).

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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.993

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)0.0080.001

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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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.

Study designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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