Brownian dynamics simulations of analytical ultracentrifugation experiments exhibiting hydrodynamic and thermodynamic non-ideality
Bibliographic record
Abstract
Hydrodynamic and thermodynamic non-ideality are important phenomena when studying concentrated and interacting systems in analytical ultracentrifugation (AUC). Here we present an extended Brownian Dynamics (BD) based algorithm which incorporates hydrodynamic and thermodynamic non-ideality. It can serve as an independent and versatile approach for the theoretical description of interparticulate interactions in AUC, as it allows tracking the trajectory of individual particles. Concentration dependencies of the sedimentation and diffusion coefficient have been implemented and validated for the extended BD model. For monodisperse systems, it is shown that profiles obtained by BD are in excellent agreement with well-established Lamm equation solvers. Moreover, important limits and restrictions of current Lamm equation based analysis methods are discussed. In particular, BD allows modeling and evaluation of AUC data of non-ideal polydisperse systems. This is relevant as most nanoparticulate systems are polydisperse in size. Here, a simulation for a polydisperse system including concentration effects is presented for the first time.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".