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 Mo 8 C 12 and MoC but shows significant covalent character in Mo 2 C, Mo 3 C, and Mo 28 C 14 . 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.
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 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 | 0.001 |
| 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".