Biospecific Affinity Chromatography: Computational Modelling via Lattice Boltzmann Method and Influence of Lattice-Based Dimensionless Parameters
Bibliographic record
Abstract
Based on a dynamic (i.e.time-dependent) one-dimensional approach, this work applied lattice Boltzmann method (LBM) to computationally model biospecific affinity chromatography (BAC).With governing equations expressed in lattice-based dimensionless form, LBM was implemented in D1Q2 lattice by assigning particle distribution functions to adsorbate concentration in both fluid and solid phases.The LBM simulator was firstly tested in view of a classic BAC work on lysozyme and the streaming step relating to adsorbate concentration in the solid-phase was suppressed from the LBM code with no loss of functionality.Expected behaviour of breakthrough curves was numerically reproduced and the influence of lattice-based dimensionless parameters was examined.The LBM simulator was next applied so as to assess lattice-based dimensionless parameters regarding an experimental BAC work on lipase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".