Application of topological analysis of the electron localization function to the complexes of molybdenum carbide nanoparticles with unsaturated hydrocarbons
Bibliographic record
Abstract
The catalytic transformation of the heavy aromatics in bitumen into lighter components is the key to the upgrading and refining of the oil sands. To understand the chemical bonding in molybdenum carbide nanoparticle (MCNP) catalysts and the chemisorption bonds between the MCNPs and unsaturated hydrocarbons, the topological analysis of the electron localization function was applied to various MCNPs and their complexes with unsaturated hydrocarbons. For some of the smaller complexes, comparisons are made with the atoms in molecules approach, including the calculation of delocalization indices. The results are interpreted in the Lewis bonding scheme. It was found that the Mo–C bonding can be highly ionic in cases like Mo8C12 and MoC but shows significant covalent character in Mo2C, Mo3C, and Mo28C14. The chemisorption bonds between hydrocarbons and the MCNPs involve electron sharing of various types with strong covalent character. The strong three– or four–center interactions determine the adsorption configurations of the hydrocarbons on the MCNPs. Derivatives of benzene show some different bonding features, which depend strongly on the substituent or the heteroatom.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".