Predicting active, selective and stable Mo2C–based bimetallic carbides for direct deoxygenation and hydrogenation reactions: A computational screening
Bibliographic record
Abstract
Transition metal carbides (TMCs), especially molybdenum carbide (Mo2C), represent an economical and attractive alternative to precious noble metal catalysts for processes involving hydrogenation (HYD) and direct deoxygenation (DDO) reactions such as hydrodeoxygenation (HDO). Although Mo2C has good activity for DDO, it is not selective as it displays similar activity towards HYD as well. Moreover, stability against oxygen poisoning is a critical issue as it is the major reason of catalyst deactivation. Herein, using model compounds with different oxygen functionalities (phenol, guaiacol, and 5-HMF), we employed density functional theory (DFT) calculations to investigate three key reactions (C-O dissociation, oxygen removal and ring hydrogenation) representing activity, stability and selectivity which are critical to the HDO process on 45 Mo2C-based bimetallic carbide catalysts (Co, Cr, Fe, Mn, Nb, Ni, Ti and Zr) obtained from the Materials Project database. Among these catalysts, different dopants, stoichiometric ratios and terminations were considered. First, reaction energies of oxygen removal were used to identify stable catalysts. Subsequently, with the energy barriers of DDO and HYD for activity and their differences for selectivity, best candidates for DDO (Mo-terminated Fe3Mo3C, Mn-terminated Mn3Mo3C and Ni-terminated Ni3Mo3C) and HYD (triclinic Mo-terminated Fe11MoC4 and Ni-terminated Ni6Mo6C) were identified. Since strong oxygen binding favors DDO but hinders oxygen removal, an optimal range of oxygen binding energy (OBE) between −100 kJ/mol to −200 kJ/mol has been proposed. In addition, a parameter encompassing the three critical aspects (activity, stability, and selectivity) of a catalyst was proposed. Using OBEas a descriptor and the proposed parameter, a qualitative trend was obtained which can be used for preliminary screening of large databases of catalysts, including TMCs other than Mo2C-based, for processes involving DDO and HYD 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".