Modelling textural and mass transfer properties for gamma‐alumina catalysts using randomly generated pore networks
Bibliographic record
Abstract
Abstract A Monte Carlo approach is used to generate 2D and 3D networks of randomly connected cylindrical pores with a variety of configurations. These networks are created to represent the gamma‐alumina supports of hydrotreating catalysts. Textural properties from generated pore networks are compared with experimental values of porosity, specific surface area, and specific pore volume. The experimental properties were estimated using a helium pycnometer and nitrogen sorption isotherms for five gamma‐alumina samples. Simulated and experimental textural properties concur. Internal diffusion is simulated by 1D Fick diffusion within each pore of the network. A macroscopic diffusion parameter for vacuum distillate type molecules, previously obtained by inverse liquid chromatography and by pulsed‐field gradient nuclear magnetic resonance experiments on alumina samples, is predicted and confronted with experimental values. Diffusional properties are in good agreement when considering two hierarchically organized porous domains.
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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".