Further Molecular Insights into Formation of Tetra-<i>n</i>-Butyl Bromide Semi-Clathrate Hydrates
Bibliographic record
Abstract
In this research, melting temperature and the growth process of tetra- n -butyl phosphonium bromide (TBPB) and tetra- n -butyl ammonium bromide (TBAB) hydrates with memory effect phenomenon at the microscopic level are investigated. All the simulation runs are carried out using optimized potentials for liquid simulations-all-atom (OPLS-AA) force field under NPT conditions (a constant number of atoms, pressure, and temperature). In addition, at a constant pressure of 1 MPa, the effectiveness of different thermostat algorithms (including Berendsen, Nose–Hoover, and velocity-rescale) and effect of temperature (250–350 K) are assessed in the formation of TBPB and TBAB semiclathrate hydrates. The melting temperatures of 280 and 283 K (at P = 0.1 MPa) correspond to the TBPB and TBAB semiclathrate hydrates, respectively, by following the peaks in the Z -density profile. Instantaneous temperature and potential energy results during simulation runs indicate that the Berendsen thermostat is very efficient for relaxing a system to the target temperature and damping the temperature oscillations. A reasonable match is observed between the results of the current research and previous studies available in the literature. According to the simulation results, growth of TBAB and TBPB semiclathrate hydrates is observed at 250 K over the simulation runs, and most of the molecules form a semiclathrate gas hydrate structure. At a higher temperature, the balance of hydrogen bond between water molecules in the semiclathrate hydrate network becomes weak. Thus, the TBPB and TBAB hydrates are completely decomposed. This research offers promising details about the growth of semiclathrate hydrates through the memory effect phenomenon, which can be used for better management of gas hydrate production operations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".