Molecular Dynamics Simulation of Injection Flow in a Nano-Meter Syringe by Consideration of Extra Pairwise Interactions for Positive and Negative Ions
Bibliographic record
Abstract
The average concentration of ions in a liquid depends on the size of a channel if the charges on solid surface do not change. The relation between them is that the concentration of ions is inversely proportional to the channel size. When a channel decreases from a micro to a nano size, the concentration of ions will increase 1000 times. In this case, the ion’s distribution in liquid may not be considered as dilute if the charges on solid surface is large, and interactions among ions have to take into account. In this paper, molecular dynamics is applied to study the effect of extra-pairs of positive/negative ions on liquid transport properties in a nano syringe under a constant injection flow rate boundary condition. In simulations, the Coulomb’s law and 12-6 Lennard-Jones potential are used to govern the interaction between ion-ion, ion-liquid, ion-solid, liquid-liquid and liquid-solid molecules. Four different cases (no ions, counter-ions, and counter-ions combining with small and large extra-pairs of positive/negative ions in liquid) are carried out. The non-equilibrium molecular dynamics (NEMD) simulation results show that the concentration of extra-pairs of positive/negative ions has significant influence on liquid velocity profile and ion distributions. For liquid flow without ions, a quasi-parabolic velocity distribution was obtained. When the counter-ions and extra-pairs of positive/negative ions are considered, the flow approaches a plug flow as the number of extra-pair of ions increases. We also found that charges in liquid do not follow the Poisson-Boltzmann distribution, especially for the net charges which have a valley located at about 1.5 molecular sizes away from the solid surface.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".