Revealing the Potential of Bimetallic Carbide Catalysts for Upgrading Biomass‐Derived Bio‐Oil: A First Principles‐Based Investigation Using Representative Bio‐Oil Constituents
Bibliographic record
Abstract
Abstract Bimetallic carbides, especially based on molybdenum carbide, proved to be a promising hydrodeoxygenation (HDO) catalyst, with enhanced selectivity towards C─O bonds cleavage. However, catalysts are generally investigated using limited model components derived from only one of the biopolymers in biomass (either lignin or carbohydrates), therefore, HDO of raw biomass‐derived bio‐oil still remains a challenge, as it contains molecules of different functionalities. This paper presents a systematic comparison of the monometallic carbide, Mo2C, with the novel bimetallic carbide incorporating W using representative bio‐oil components of different functionalities and derived from different bio‐polymers components of biomass. We employed quantum mechanical investigation to reveal that the W‐doped Mo2C carbide (MoWC) catalyst with increased oxophilicity, owing to tungsten incorporation, can perform HDO of real bio‐oil more effectively in comparison to its monometallic counterpart (Mo2C) using six substrates representing different components of bio‐oil. We showed MoWC can selectively cleave both single/double (C─O/C═O) bonds with similar barriers in comparison to Mo2C which can only selectively cleave single C─O bonds. This observation was consistent for 5‐HMF, Acetic acid, and Methyl Glyoxal. We also showed that MoWC outperforms its metallic counterpart Mo2C in the HDO for aromatic and carbohydrates components.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".