Applicability of Consistency Criteria To Calculate the Brunauer–Emmett–Teller Surface Area of Graphite and Kerogen Mesopores
Bibliographic record
Abstract
The Brunauer–Emmett–Teller (BET) method has been widely used to characterize surface area of various porous materials. As a result of some oversimplified assumptions, the performance of BET method can be compromised in micro- and mesopores. Thus, the consistency criteria were introduced to improve its performance. However, the applicability of the consistency criteria for kerogen mesopores has not yet been assessed. In this work, N 2 adsorption in graphite and kerogen mesopores is conducted by grand canonical Monte Carlo (GCMC) simulations at 77 K, while the accuracy of the standard BET method (BET_STD) and the BET considering consistency criteria (BET_CC) for surface area estimation is assessed. For graphite slit mesopores, BET_CC can better describe N 2 adsorption behaviors in graphite mesopores than BET_STD. For kerogen mesopores, the surface area from BET_CC has a better linear correlation with CH 4 excess/total adsorption under in situ conditions than that from BET_STD. In other words, the surface area from BET_CC is a better indicator than that from BET_STD to predict CH 4 adsorption behaviors. Our work provides important insights into BET surface area characterization in kerogen mesopores and optimization of characterization processes to better assess gas-in-place estimation in shale media.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".