Recyclable High-Entropy Oxide Catalysts Unlock Lignocellulose Recalcitrance toward High Yield Xylochemicals
Bibliographic record
Abstract
The development of catalysts that are both highly active and structurally stable is critical for addressing the inherent challenges posed by the recalcitrance of lignocellulose, which arises from the complex interconnections and varied chemical properties of lignocellulosic feedstocks. Consequently, there is a pressing need for efficient biorefinery strategies that facilitate the hierarchical separation of these components and their subsequent conversion to valuable chemicals. In this article, we introduce a two-step catalytic process that employs a spinel high-entropy oxide (HEO). This HEO benefits from a “cocktail effect”, wherein its high entropy confers numerous intrinsic oxygen vacancies, enhancing its oxygen adsorption and activation capabilities for oxidation catalysis. This approach allows for the efficient refinement of lignocellulose into fine chemicals and exhibits an enhanced performance under mild reaction conditions. The robustness and stability of HEO enable them to sustain a high catalytic activity and effectiveness through multiple catalytic cycles. Finally, we have successfully fractionated lignin into aromatic monomers with a yield of 42 wt % and achieved an impressive total carboxylic acid yield of 150 wt % relative to the original hemicellulose content. Additionally, the cellulose solid fraction, preserved during the HEO treatment, yielded 98.93 wt % glucose when chemically saccharified using a solvent mixture of zinc chloride and formic acid. This strategy represents a significant advance in biomass upgrading, demonstrating markedly higher chemical efficiency in comparison to previously reported methodologies.
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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) | 0.001 | 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".