Thermal properties of structure one hydrates using density functional theory
Bibliographic record
Abstract
Under certain conditions, water and small gaseous molecules form solid, crystalline gas hydrates, members of a larger class of structures called inclusion compounds or clathrates. Gas hydrates have previously been examined in the petroleum industry due to their propensity to cause flow assurance and safety issues and currently are examined as potential natural gas and hydrogen containers, as well as in separation processes to selectively capture flue gases. Thermal properties research has been centered around heat capacity and thermal expansion coefficient, experientially and theoretically. By using the Vienna ab initio Simulation Package (VASP) to solve the Schrödinger Equation in the context of Density Functional Theory, this research brought the gap between simulation and experiment by calculating the thermal properties of sI gas hydrates, using Phonopy paired with VASP, and to provide nanoscale insight into macroscale behavior.The constant volume heat capacity, the constant pressure heat capacity, the volumetric thermal expansion coefficient, and the Grüneisen parameter of methane, ethane, carbon dioxide, and empty sI hydrates, and of hexagonal ice, as functions of temperature from 0 to 300 Kelvin were calculated using DFT. The constant volume heat capacity was lower than when compared with literature values calculated with MD. DFT replicated experimental values of constant pressure heat capacity for hydrates and ice well at low temperatures. DFT underestimated the thermal expansion coefficient in all cases. The ethane and carbon dioxide hydrates demonstrated behavior that was markedly different when compared to methane and empty hydrates, and hexagonal ice. The Grüneisen parameter was calculated for all systems. DFT overestimated the value of the parameter for filled hydrates and hexagonal ice when compared to experimental hexagonal ice values
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".