Native Defects in α-Mo<sub>2</sub>C: Insights from First-Principles Calculations
Bibliographic record
Abstract
Molybdenum carbide is a promising material for replacing the hydrogenation catalysts used in industry. It has been synthesized using different sources of molybdenum oxides and carbon, such as molybdenum heptamolybdate, molybdenum oxides, glucose, alkanes, and toluene. Nonstoichiometric material is produced, normally forming carbon vacancies or molybdenum vacancies, depending on the synthetic route. An approach for calculating the Helmholtz free energy of vacancy formation has been proposed, taking as reference the carbon and molybdenum atomic energies in the solid. The Helmholtz free energy for the carbon and molybdenum vacancies for different temperatures has been calculated. The results show that at 650 K, 9.6% of molybdenum vacancies and 10.2% of carbon vacancies exist. For temperatures below 610 K, the concentration of molybdenum vacancies is larger than that of carbon vacancies. The electron localization function (ELF) has been calculated for both defective materials, showing that the carbon vacancy presented trapped electrons forming a network that extends throughout the crystal with nearly homogeneous density. For the molybdenum vacancies, the electron density is completely depleted, leading to an electron-deficient site. The Lewis acid and base sites formed upon the presence of vacancies must be of real importance for understanding the catalytic properties of such materials. The presence of both vacancies is predicted to exist at a temperature of about 620 K.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".