Engineered Sorghum Bagasse Enables a Sustainable Biorefinery with <i>p</i>‐Hydroxybenzoic Acid‐Based Deep Eutectic Solvent
Bibliographic record
Abstract
Abstract Integrating multidisciplinary research in plant genetic engineering and renewable deep eutectic solvents (DESs) can facilitate a sustainable and economic biorefinery. Herein, we leveraged a plant genetic engineering approach to specifically incorporate C 6 C 1 monomers into the lignin structure. By expressing the bacterial ubiC gene in sorghum, p ‐hydroxybenzoic acid (PB)‐rich lignin was incorporated into the plant cell wall while this monomer was completely absent in the lignin of the wild‐type (WT) biomass. A DES was synthesized with choline chloride (ChCl) and PB and applied to the pretreatment of the PB‐rich mutant biomass for a sustainable biorefinery. The release of fermentable sugars was significantly enhanced (∼190 % increase) compared to untreated biomass by the DES pretreatment. In particular, the glucose released from the pretreated mutant biomass was up to 12 % higher than that from the pretreated WT biomass. Lignin was effectively removed from the biomass with the preservation of more than half of the β‐Ο‐4 linkages without condensed aromatic structures. Hydrogenolysis of the fractionated lignin was conducted to demonstrate the potential of phenolic compound production. In addition, a simple hydrothermal treatment could selectively extract PB from the same engineered lignin, showing a possible circular biorefinery. These results suggest that the combination of PB‐based DES and engineered PB‐rich biomass is a promising strategy to achieve a sustainable closed‐loop biorefinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".