Probing sedimentation non-ideality of particulate systems using analytical centrifugation
Why this work is in the frame
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Bibliographic record
Abstract
Analytical centrifugation is a versatile technique for the quantitative characterization of colloidal systems including colloidal stability. The recent developments in data acquisition and evaluation allow the accurate determination of particle size, shape anisotropy and particle density. High precision analytical centrifugation is in particular suited for the study of particle interactions and concentration-dependent sedimentation coefficients. We present a holistic approach for the quantitative determination of sedimentation non-ideality via analytical centrifugation for polydisperse, plain and amino-functionalized silica particles spanning over one order of magnitude in particle size between 100 nm and 1200 nm. These systems typically behave as neutral hard spheres as predicted by auxiliary lattice Boltzmann simulations. The extent of electrostatic interactions and their impact on sedimentation non-ideality can be quantified by the repulsion range, which is the ratio of the Debye length and the average interparticle distance. Experimental access to the repulsion range is provided through conductivity measurements. With the experimental repulsion range at hand, we estimate the effect of polydispersity on concentration-dependent sedimentation properties through a combination of lattice Boltzmann and Brownian dynamics simulations. Finally, we determine the concentration-dependent sedimentation properties of charge-stabilized, fluorescently-labeled silica particles with a nominal particle size of 30 nm and reduced interparticle distance, hence an elevated repulsion range. Overall, our results demonstrate how the influence of hard-sphere type and electrostatic interactions can be quantified when probing sedimentation non-ideality of particulate systems using analytical centrifugation even for systems exhibiting moderate sample heterogeneity and complex interactions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it