A New Adsorption Equation for Nano-Porous Shale Rocks and Its Application in Pore Size Distribution Analysis
Bibliographic record
Abstract
Adsorption equations are important to analyze the pore size distribution (PSD) of shale and the adsorption behavior on it. However, the accurate description of nitrogen adsorption on shale by current adsorption equations is difficult to achieve due to the heterogeneous pore structure of shale. In our study, new adsorption isotherms that can properly depict the adsorbed amount of nitrogen were built for shale rocks considering both the processes of nitrogen adsorption and the cylindrical pore shape property of shale. When performing a regression analysis on five sets of experimental adsorption data using the developed adsorption equations, the R-square ranged from 0.739 to 0.987. Based on the pore shape determined by adsorption–desorption curves, the distinct R-square indicated that our equation is not valid for shale samples with ink-bottle pores and pores formed by schistose materials, but that it is suitable for shale samples with cylindrical pores and slit pores. Meanwhile, we precisely analyzed the PSDs of shale rocks based on the developed adsorption equations as capillary condensation volume is involved in the total adsorbed amount. Thus, the PSDs of shale rocks with cylindrical pore and slit pore were analyzed by the new adsorption equation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".