Competition between electroosmotic and chemiosmotic flow in charged nanofluidics
Bibliographic record
Abstract
In electrolyte solutions, charged nanoscale pores or channels with overlapping electrical double layers are charge selective, thereby benefiting a wide range of applications such as desalination, bio-sensing, membrane technology, and renewable energy. As an important forcing mechanism, a gradient of electrolyte concentration along a charged nano-confinement can drive flow without an external electrical field or applied pressure difference. In this paper, we numerically investigate such a diffusioosmotic nanoflow, particularly for dilute electrolyte concentrations (0.01 mM–1 mM), and calculate the corresponding electrical and concentration fields in a charged nanochannel connecting two reservoirs of different salt concentrations—a typical fluidic configuration for a variety of experimental applications. Under a wide range of parameters, the simulation results show that the flow speed inside the nanochannel is linearly dependent on the concentration difference between the two reservoir solutions, Δc, whereas the flow direction is primarily influenced by three key parameters: nanochannel length (l), height (h), and surface charge density (σ). Through a comparison of the chemiosmotic (due to ion-concentration difference) and electroosmotic (as a result of the induced electric field) components of this diffusioosmotic flow, a non-dimensional number (C=h/lλGC) has been identified to delineate different nanoscale flow directions in the charged nanochannel, where λGC is a characteristic (so-called Gouy–Chapman) length associated with surface charge and inversely proportional to σ. This critical dimensionless parameter, dependent on the above three key nanochannel parameters, can help in providing a feasible strategy for flow control in a charged nanochannel.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".