Effect of Energetical and Geometrical Heterogeneity of Kerogen on BET Surface Area Characterization and Methane Adsorption
Bibliographic record
Abstract
Surface area is an important parameter for the estimation of methane (CH 4 ) adsorption in shale nanoporous media. Kerogen, as the main constituent of shale organic matters, has exceptionally high surface area due to extensive nanoscale pores. The Brunauer–Emmett–Teller (BET) method has been extensively used to characterize the surface area of various porous materials. However, its applicability for the surface area characterization of kerogen mesopores has not been investigated yet. In this work, the effect of geometrical and energetical heterogeneity on N 2 adsorption isotherms and the subsequent BET surface area ( S BET ) characterization is studied by using grand canonical Monte Carlo simulations. We find that N 2 adsorption sites are mainly within the “basin” and “valley” regions on kerogen surfaces, while in the “ridge” regions, its adsorption rarely takes place at 77 K from 0.005 to 0.05 bar. On the other hand, surface chemistry shows a significant effect on external potential and N 2 adsorption amount. In addition, while S BET agrees well with geometric surface area ( S geo ) in graphite mesopores, in kerogen and pseudokerogen mesopores, S BET is generally lower than S geo . Interestingly, for our samples, S BET correlates well with CH 4 excess adsorption in kerogen mesopores at 333.15 K and 300 bar, outperforming S geo . This work provides some crucially important fundamental understanding about the S BET characterization of kerogen mesopores which can guide the prediction of CH 4 adsorption capacity in kerogen nanoporous media and the estimation of shale gas-in-place.
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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 | 0.000 |
| 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) | 0.001 | 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".