Molecular Dynamics Simulation of Nano Channel as Nanopumps
Bibliographic record
Abstract
We use three-dimensional molecular dynamics simulation to investigate the driven flow between two parallel plates separated by argon atoms. Our simulations show that fluids in such channels can be continuously driven. Difference in surface wettability can cause a difference in fluid density along the nano channel. To control the nanochannel temperature walls, we use the thermal wall idea, which models the walls using atoms connected to their original positions by enforcing linear spring forces. In this study, we propose a nanochannel system in which, half of the channel has a low surface wettability, while the other half has a higher surface wettability and that the middle part of channel wall has a high temperature. In another test case, we study a channel with a high temperature at one side of channel and impose a low temperature at the other side. Imposing a high temperature at the middle of nanochannel breaks the molecular force balances and a driven flow is formed in the channel due to the difference in fluid density. Also, imposing a temperature gradient at the walls causes a momentum difference between the atoms on the opposite sides of channel, which is a reason for driving flow through the channel. We use these molecular dynamics tools to achieve better volumetric results in the 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".