Pore Size Distribution Analysis of Selected Hexagonal Mesoporous Silicas by Grand Canonical Monte Carlo Simulations
Bibliographic record
Abstract
We combine here a regularization procedure with individual adsorption isotherms obtained from grand canonical Monte Carlo simulations in order to obtain reliable pore size distributions. The methodology is applied to two hexagonal high-ordered silica materials: SBA-15 and PHTS, synthesized in our laboratory. Feasible pore size distributions are calculated through an adaptable procedure of deconvolution over the adsorption integral equation, with two necessary inputs: the experimental adsorption data and individual adsorption isotherms, assuming the validity of the independent pore model. The application of the deconvolution procedure implies an adequate grid size evaluation (i.e., numbers of pores and relative pressures to be considered for the inversion, or kernel size), the fulfillment of the discret Picard condition, and the appropriate choice of the regularization parameter (L-curve criteria). Assuming cylindrical geometry for both porous materials, the same set of individual adsorption isotherms generated from molecular simulations can be used to construct the kernel to obtain the PSD of SBA-15 and PHTS. The PSD robustness is measured imposing random errors over the experimental data. Excellent agreement is found between the calculated and the experimental global adsorption isotherms for both materials. Molecular simulations provide new insights into the studied systems, pointing out the need of high-resolution isotherms to describe the presence of complementary microporosity in these materials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".