Role of Adsorption-Induced Deformation on Gas Self-Diffusivity in a Flexible Microporous Coal Matrix
Bibliographic record
Abstract
Adsorption-induced deformation has long been underappreciated in gas transport studies of microporous coal, yet it strongly influences pore configurations and diffusive pathways. Here, a hybrid grand canonical Monte Carlo (GCMC)/molecular dynamics (MD) approach and equilibrium MD (EMD) simulations are employed to investigate how matrix flexibility reshapes pore structures and, in turn, impacts CH 4 and CO 2 self-diffusion in connected pore networks under various gas loadings. The results show that coal matrix deformation enhances adsorption, with CO 2 exhibiting greater uptake and volumetric strain than CH 4 . A universal linear relationship emerges among gas loading, free volume ratio, and self-diffusion coefficients for both rigid and flexible matrices. In flexible matrices, this linearity features a gentler slope, indicating reduced diffusion sensitivity to diminishing free volume with loadings. By comparing geometrical and effective tortuosity, it is revealed that strongly adsorbing CO 2 induces significant swelling and complex local rearrangements at elevated loadings, pushing geometrical tortuosity far beyond rigid-matrix levels, whereas CH 4 ─with weaker adsorption─drives smaller, more uniform structural adjustments that only mildly increase geometrical tortuosity. These differences in tortuosity directly reflect changes in path complexity, which in turn governs self-diffusion behavior. Collectively, the findings clarify the dynamic coupling between gas adsorption, matrix deformation, and self-diffusivity in microporous coal, offering critical guidance for enhanced gas recovery and CO 2 sequestration strategies that rely on accurate modeling of gas transport in deformable media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".