Solute Dispersion in Soft Nanochannel under Streaming Potential-Mediated Pressure-Driven Flow
Bibliographic record
Abstract
The present study investigates the dispersion of uncharged solutes in a soft nanochannel, an engineered device, with a particular emphasis on the influence of streaming potential under pressure-driven flow. A soft-step model is employed to account for the nonuniform distribution of monomers and the accompanying volume charge within the polyelectrolyte layer (PEL). The rigid channel walls are assumed to be hydrophobic and charged. Notably, ion-steric effects become significant for moderate to high charge conditions. Besides, the dielectric permittivity of the PEL is generally lower than that of the electrolyte solution, and hence, the ion partitioning effect becomes relevant. Based on these coupled electrostatic and hydrodynamic factors, we first analyzed the generation of streaming potential and the associated electrokinetic phenomena. The subsequent impact of streaming potential induced flow on the solute dispersion is then examined. Three models are adopted for this purpose: a general two-dimensional (2D) model for concentration distribution and a one-dimensional (1D) Gill model and a late-time Taylor–Aris (TA) model for area-averaged concentration. Dispersion is characterized by spatiotemporal concentration distributions, as well as the effective dispersion coefficient. A finite-difference based numerical method is used to solve the governing equations, and the results are validated against available experimental data as well as theoretical predictions under weak charge limits. To quantify the role of streaming potential-mediated axial flow, the findings are systematically compared to those for purely pressure-driven flow. We observe that the hydrodynamic dispersion depends strongly on the electrokinetic effects that directly regulate the generation of the streaming field. We have observed that the enhanced streaming potential field attenuates the overall fluid flow strength due to an increased opposing electroosmotic flow (EOF), resulting in less band dispersion. An increase in the PEL thickness enhances the flow resistance across the PEL, which also leads to a reduced fluid flow and hence reduces the convective dispersion of the solute band. The impact of the ion partition effect operational at the PEL-to-electrolyte interface, as well as the softness parameter of the PEL, is further illustrated. An enhanced flow strength is achieved while reducing the Debye–Hückel parameter due to less impact of streaming field-mediated opposing EOF, which further leads to an enhanced dispersion coefficient. Besides, the rise in hydrodynamic slippage can lead to a larger impact of the streaming field, which in turn leads to less dispersion of the solute band. Impact of the Péclet number on the dispersion process is further illustrated. This extensive study will thus be useful in practice to design robust nanofluidic separation systems.
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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 | 0.000 |
| 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 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".