Polymeradsorption onto a micro-sphere from optical tweezers electrophoresis
Bibliographic record
Abstract
We explore the design and operation of an optical-tweezers electrophoresis apparatus to resolve polymer adsorption dynamics onto a single micro-sphere in a micro-fluidic environment. Our model system represents a broader class of micro-fluidic electrophoresis experiments for biosensing and fundamental colloid and surface science diagnostics. We track the adsorption of 100 kDa poly(ethylene oxide) homopolymer onto a colloidal silica sphere that is optically trapped in a crossed parallel-plate micro-channel. The adsorption dynamics are probed on the ∼1 μm particle length scale with ∼1 s temporal resolution. Because the particle electrophoretic mobility and channel electro-osmotic flow are exquisitely sensitive to the polymer layer hydrodynamic thickness, particle dynamics can be complicated by polymer adsorption onto the micro-channel walls. Nevertheless, using experiments and a theoretical model of electro-osmotic flow in channels with non-uniform wall ζ-potentials, we show that such influences can be mitigated by adopting a symmetrical flow configuration. The equilibrium hydrodynamic layer thickness of 100 kDa poly(ethylene oxide) on colloidal silica is ∼10 nm at polymer concentrations ≳10 ppm (weight percent), with the dynamics reflecting polymer solution concentration, flow rate, and polydispersity.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".