Study of Cut-Off Radius and Temperature Effects on Water Molecular Behavior Using Molecular Dynamics Method
Bibliographic record
Abstract
Water molecules are one of the important molecules in nanofluidics. Its structure and its behavior can change with Temperature and cut-off distance parameters. In this study temperature and cut-off distance effects on the nano-scale water molecules behavior are investigated by molecular dynamics simulations. Many water molecular models have been developed in order to help discover the structure of water molecules. In this study, the flexible three centered (TIP3P-C) water potential is used to model the inter- and intramolecular interactions of the water molecules. In this simulation, we have been studied 512 water molecules with periodic boundary conditions and in a simulation box with 25 angstrom dimensions, which gives water density about 0.99 g/cm3. To examine of accuracy of TIP3P-C model, Radial distribution function of remarkable water model has been compared with experimental data. In this paper, to study temperature effect on water behavior, mentioned system with 300, 450 and 600 K have been considered and compared. The results have showed that with decreasing temperature, the tetrahedrality of the distribution of the water molecules around the central water molecule is enhanced, and the hydrogen bonds become more linear. It is found that as the temperature rises, kinetic energy rises too, and it makes that the average number of hydrogen bonds per water molecule decrease. In addition to temperature effects, cut-off radius parameter effects have been considered too, and four different cut-off radiuses 7.5, 9.0, 10.5, and 12.0 angstrom have been studied.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".