Inter-cage dynamics in structure I, II, and H fluoromethane hydrates as studied by NMR and molecular dynamics simulations
Bibliographic record
Abstract
Prospective industrial applications of clathrate hydrates as materials for gas separation require further knowledge of cavity distortion, cavity selectivity, and defects induction by guest-host interactions. The results presented in this contribution show that under certain temperature conditions the guest combination of CH3F and a large polar molecule induces defects on the clathrate hydrate framework that allow intercage guest dynamics. (13)C NMR chemical shifts of a CH3F/CH4/TBME sH hydrate and a temperature analysis of the (2)H NMR powder lineshapes of a CD3F/THF sII and CD3F/TBME sH hydrate, displayed evidence that the populations of CH4 and CH3F in the D and D' cages were in a state of rapid exchange. A hydrogen bonding analysis using molecular dynamics simulations on the TBME/CH3F and TBME/CH4 sH hydrates showed that the presence of CH3F enhances the hydrogen bonding probability of the TBME molecule with the water molecules of the cavity. Similar results were obtained for THF/CH3F and THF/CH4 sII hydrates. The enhanced hydrogen bond formation leads to the formation of defects in the water hydrogen bonding lattice and this can enhance the migration of CH3F molecules between adjacent small cages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".