Molybdenum Carbide Nanocatalysts at Work in the in Situ Environment: A Density Functional Tight-Binding and Quantum Mechanical/Molecular Mechanical Study
Bibliographic record
Abstract
Heterogeneous reactions catalyzed by transition-metal-containing nanoparticles represent a crucial type of reaction in chemical industry. Because of the existing gap in understanding heterogeneous catalysis between a cluster of a few atoms and a bulk model of periodic slabs, reactions catalyzed by transition-metal-containing nanoparticles are still not well understood. Herein, we provide a multiscale modeling approach to study the benzene hydrogenation reactions on molybdenum carbide nanoparticles (MCNPs) in the process of in situ heavy oil upgrading. By coupling the quantum mechanical (QM) density functional tight-binding (DFTB) method with a molecular mechanical (MM) force field, a 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 under 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".