Selective Hydrodeoxygenation of Lignin‐Derived Vanillin via Hetero‐Structured High‐Entropy Alloy/Oxide Catalysts
Bibliographic record
Abstract
The chemoselective hydrodeoxygenation of natural lignocellulosic materials plays a crucial role in converting biomass into value‐added chemicals. Yet their complex molecular structures often require multiple active sites synergy for effective activation and achieving high chemoselectivity. Herein, it is reported that a high‐entropy alloy (HEA) on high‐entropy oxide (HEO) hetero‐structured catalyst for highly active, chemoselective, and robust vanillin hydrodeoxygenation. The heterogenous HEA/HEO catalysts were prepared by thermal reduction of senary HEOs (NiZnCuFeAlZrOx), where exsolvable metals (e.g., Ni, Zn, Cu) in situ emerged and formed randomly dispersed HEA nanoparticles anchoring on the HEO matrix. This catalyst exhibits excellent catalytic performance: 100% conversion of vanillin and 95% selectivity toward high‐value 2‐methyl‐4 methoxy phenol at low temperature of 120 °C, which were attributed to the synergistic effect among HEO matrix (with abundant oxygen vacancies), anchored HEA nanoparticles (having excellent hydrogenolysis capability), and their intimate hetero‐interfaces (showing strong electron transferring effect). Therefore, our work reported the successful construction of HEA/HEO heterogeneous catalysts and their superior multifunctionality in biomass conversion, which could shed light on catalyst design for many important reactions that are complex and require multifunctional active sites.
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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 | 0.000 | 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".