In-situ Heavy Oil Upgrading with Molybdenum Carbide Nanoparticles: A Multiscale Modelling Approach
Bibliographic record
Abstract
Heterogeneous reactions catalyzed by transition-metal-related nanoparticles represent a crucial type of reaction in chemical industry. This thesis provides a multiscale modelling approach to study the benzene hydrogenation reactions on molybdenum carbide nanoparticles (MCNPs) in the process of in-situ heavy oil upgrading in Alberta. To clarify the debate on the benzene hydrogenation mechanism, density functional theory (DFT) calculations are performed with cluster models in Chapter 3. From the DFT thermodynamic data, together with the experimental information gathered in the literature, the benzene hydrogenation mechanism on molybdenum carbide was identified as the Horiuti-Polanyi type Langmuir-Hinshelwood mechanism. Benzene adsorbs horizontally on the molybdenum carbide, and the hydrogenation process causes the gradual tilting up of the C6 ring, to form 12-dihydrobenzene and 1234-tetrahydrobenzene, and finally the product cyclohexane, and causes the crossover from chemisorption to physisorption. Topological analysis of the electron localization function (ELF) in Chapter 4 provides a deeper understanding of the interactions between the unsaturated hydrocarbons and the MCNPs. The chemisorption of unsaturated hydrocarbons on the MCNPs involves strong chemical interactions of a covalent nature, and is dominated by multi-center electron sharing interactions. The building up of a multiscale model starts with the parameterization of the quantum mechanical (QM) density functional tight-binding (DFTB) method for Mo, C, H, O, and Si. The QM calculations show that the MCNPs are highly metallic nanoparticles. The topology of the active sites is more important than the sizes of the MCNPs for the catalytic activity. By coupling the QM DFTB method with an MM force field, a quantum mechanical/molecular mechanical (QM/MM) model was built to describe the reactants, the nanoparticles and the surroundings. Umbrella sampling (US) was used to calculate the free energy profiles of the benzene hydrogenation reactions in a model aromatic solvent in the in-situ heavy oil upgrading conditions. By comparing with the traditional method in computational heterogeneous catalysis, the results reveal new features of the metallic MCNPs. Rather than being rigid, they are very flexible in the working condition due to the entropic contributions of the MCNPs and the solvent, which greatly affect the free energy profiles of these nanoscale heterogeneous reactions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".