Statistical physics modelling of adsorption isotherms of water vapour on shale: Stereographic, energetic and thermodynamic investigations
Bibliographic record
Abstract
Abstract Understanding the mechanisms of shale–water interaction by water vapour adsorption is crucial for predicting shale gas productivity. In this study, equilibrium adsorption data of water vapour on four different shales of the Sichuan Basin at three temperatures (298, 308, and 318 K) were measured using static gravity techniques. The water vapour adsorption isotherms were simulated by three statistical physics models. Steric parameters, including the number of water vapour molecules adsorbed per site (n), monolayer adsorption amount (q0), and adsorption energy (ΔEa), and thermodynamic parameters such as configuration entropy (Sa), internal energy (Eint), and free energy (Ga) derived from the selected model were used to explain the adsorption mechanism. The model analyses suggest that the adsorbed water vapour molecules are attached to the shale surface in a multi‐anchorage manner. The adsorption of the first layer shows a Type I characteristics, while the adsorption of the subsequent layer is of Type III. The calculated adsorption energies indicate that the physical adsorption takes place on the water vapour molecules on the shale, and the main interaction forces are hydrophilic bonding forces and van der Waals forces. Negative Eint and Ga values indicate that the spontaneous properties are for water vapour adsorption and that the system requires the release of energy to capture the water vapour molecules.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".