From Infrared Spectra to Macroscopic Mechanical Properties of sH Gas Hydrates through Atomistic Calculations
Bibliographic record
Abstract
The vibrational characteristics of gas hydrates are key identifying molecular features of their structure and chemical composition. Density functional theory (DFT)-based IR spectra are one of the efficient tools that can be used to distinguish the vibrational signatures of gas hydrates. In this work, ab initio DFT-based IR technique is applied to analyze the vibrational and mechanical features of structure-H (sH) gas hydrate. IR spectra of different sH hydrates are obtained at 0 K at equilibrium and under applied pressure. Information about the main vibrational modes of sH hydrates and the factors that affect them such as guest type and pressure are revealed. The obtained IR spectra of sH gas hydrates agree with experimental/computational literature values. Hydrogen bond's vibrational frequencies are used to determine the hydrate's Young's modulus which confirms the role of these bonds in defining sH hydrate's elasticity. Vibrational frequencies depend on pressure and hydrate's O···O interatomic distance. OH vibrational frequency shifts are related to the OH covalent bond length and present an indication of sH hydrate's hydrogen bond strength. This work presents a new route to determine mechanical properties for sH hydrate based on IR spectra and contributes to the relatively small database of gas hydrates' physical and vibrational properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".