Poly(ethylene oxide) Adsorption onto and Desorption from Silica Microspheres: New Insights from Optical Tweezers Electrophoresis
Bibliographic record
Abstract
By measuring the changing electrophoretic mobility of single optically trapped silica microspheres (radius a ≈ 0.4 μm) during poly(ethylene oxide) homopolymer adsorption and desorption, we study polymer-layer kinetics at various polymer solution flow rates, concentrations, molecular weights, and polydispersities. At polymer concentrations c ≲ 5 ppm (mg L –1 ), Péclet numbers Pe ≲ 20, and Reynolds numbers Re ≪ 1, the adsorbing layer growth is mass-transport-limited, with time scales ∼10 s that are resolved on the uniquely small, micrometer length scale of optical tweezers electrophoresis (OTE) experiments. However, during adsorption, layer growth becomes limited by surface diffusion, reconformation, and exchange processes. Two characteristic relaxation times are revealed by the OTE time series. The faster time scale increases with polymer concentration and plateaus to ∼3 s when c ∼ 10 ppm. This reflects layer development kinetics limited by surface diffusion and reconformation. The slower time scale is ∼100 s and reflects polymer exchange, which thermodynamically favors large adsorbed coils when solutions are polydisperse. Desorption is even slower but occurs faster than expected by local-equilibrium theory, possibly because of high shear rates ∼100 s –1 . The dynamic states probed by OTE are often sufficiently far from equilibrium that they cannot be adequately described by theories for equilibrium polymer adsorption, mass-transport-limited kinetics, or kinetics based on local equilibrium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".