Physical adsorption in disordered porous materials: Theory and computer simulations
Bibliographic record
Abstract
This thesis is aimed to gain a better understanding of adsorption phenomena, with a special emphasis on hysteresis, an effect commonly observed in adsorption experiments on porous substrates. We approach this problem using an array of statistical mechanical methods and computer simulation techniques. Using a generic model of a disordered porous material we were able to show that disorder plays an important role in confined fluid phase behavior. On the other hand there is an intrinsic link between the shape of the phase diagrams for confined fluids and corresponding adsorption isotherm. For a model of a disordered porous material we have shown that adsorption isotherms are in a good qualitative agreement with experimental data on adsorption of nitrogen and inert gases in silica xerogels, specifically in the hysteresis region. This agreement was further explored, using a molecular dynamics technique that closely mimics diffusive mass transfer mechanisms of adsorption. Using a simple model of an inkbottle pore we demonstrated that commonly assumed pore blocking effects do not play a role in hysteresis formation. We applied molecular dynamics simulation technique to a model of silica xerogel to reveal a good agreement between grand canonical Monte Carlo method and molecular dynamics approach. The nature of this agreement lies in the fundamental physics incorporated into the Metropolis algorithm. Finally, using lattice models of disordered porous media and a corresponding mean field theory, we explored various aspects of confined fluid behavior. In particular, we have shown that the developed lattice model and theory are capable of generating qualitatively correct adsorption isotherms, hysteresis loops and scanning curves. The theory also allowed us to investigate the nature of hysteresis. Hysteresis appears to be an outline of multiple metastable states confined within hysteresis loop. This creates a new angle to look at experimental data and provides a self consistent framework to study adsorption in various materials.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".