Theoretical Modeling of Zeolite Nanoparticle Surface Acidity for Heavy Oil Upgrading
Bibliographic record
Abstract
We performed periodic density functional theory (DFT) and quantum-mechanics/molecular mechanics (QM/MM) investigations of the surface acidity of zeolite nanoparticles derived from natural minerals that can be used for bitumen upgrading; in particular, in the process combining bitumen precracking with impurities removal that we recently reported. Bitumen molecules are large and cannot enter zeolite pores. These mainly adsorb on the outer surface of zeolite nanoparticles, which can be optimized for efficient bitumen upgrading and impurities removal. Two chabazite slab models obtained by (003) and (003̄) cuts that have four and two surface OH groups per unit cell, respectively, are used for the periodic DFT modeling of nanoparticle surfaces. The first model is also treated by using the QM/MM method. Bitumen molecules are represented by probing bases such as ammonia, pyridine, and 2,6-dimethylpyridine that are commonly used for experimental acidity characterization. Analysis of the model acidity characteristics, such as deprotonation energies, aluminum substitution energies, OH stretching frequencies. and Fukui functions produces very good correlations. For the deprotonated chabazite, the electrophilic Fukui functions predict the most stable Brønsted site. Our results suggest that the most reactive chabazite sites are O1 and O3. The three bases investigated become fully protonated upon adsorption to the chabazite Brønsted sites. The molecular orbital spatial distributions obtained by using the periodic and QM/MM methods are very similar, which indicates good correlations between the two modeling methods. The results of our zeolite acidity calculations are in good agreement with experimental data and other computational studies available. Our findings can be useful for the further modeling and rational design of catalytic zeolite nanoparticles for heavy oil upgrading.
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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".