Water flow in a cylindrical nanopore with an object
Bibliographic record
Abstract
Understanding the physics of water movement through a nanopore with an object is critical for better control of water flow and object translocation. It should help in the design of nanopores as molecular and viral sensors. We evaluated how the external electric field and ion concentrations, pore wall charge density, disk radius and charge density, and ion mobility influence the water flow in a charged cylindrical nanopore using Poisson–Nernst–Planck–Navier–Stokes simulations. We dissected water flow induced by the external electric field (“external” component) from that generated by the field induced by the fixed and mobile charges (“charge” component). The velocity and direction of the axial flow “external” component were controlled directly by the external electric field. The pore wall charges also influenced them indirectly by altering the density and distribution of mobile charges. Higher external concentrations enhanced the axial water flow by lowering its charge component. The ion mobility and disk charge slightly influenced the axial water flow. The axial body forces near the wall drive the axial water flow near the pore wall. If the disk is large, water also flows axially in the opposite direction near the pore center. Local forces near the disk do not control the radial water flow near the disk. The axial body force and water flow near the pore wall do. If an annulus replaces a disk, the axial forces near the pore wall control the radial flow near the annulus and the axial flow within its hole.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".