Lipopolymer gradient diffusion in supported bilayer membranes
Bibliographic record
Abstract
We measure the gradient diffusion coefficient of a model lipopolymer in supported lipid bilayer membranes from Fourier-transform post-electrophoresis relaxation. The experiments and accompanying quantitative interpretation furnish the concentration dependence of the gradient diffusion coefficient. In striking contrast to the recent measurements of the self-diffusion coefficient from fluorescence recovery after photobleaching, the lipopolymer gradient diffusion coefficient increases with concentration. We interpret the enhancement at small but finite concentrations using the Scalettar-Abney-Owicki (SAO) statistical mechanical theory (1988) and the Bussell-Koch-Hammer (BKH) hydrodynamic theory (1995), which are customarily adopted to model membrane protein dynamics. The SAO theory furnishes an effective disc radius and soft repulsive interaction radius that are comparable to the Flory radius of the unperturbed polyethylene glycol chains. On the other hand, the BKH theory predicts a gradient diffusion coefficient that decreases with disc/membrane protein concentration. Thus, in contrast to membrane proteins, we conclude that lipopolymer hydrodynamic interactions are weak because the principal disturbances are in the low-viscosity aqueous phase. Accordingly, lipopolymer interactions are dominated by thermodynamic interactions among polymer chains. Interestingly, our experiments suggest that increasing (decreasing) the polymer molecular weight should increase (decrease) the relaxation rate of lipopolymer concentration fluctuations.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".