Engineered Lignin in Poplar Biomass Facilitates Cu-Catalyzed Alkaline-Oxidative Pretreatment
Bibliographic record
Abstract
Both untransformed poplar and genetically modified “zip-lignin” poplar, in which additional ester bonds were introduced into the lignin backbone, were subjected to mild alkaline and copper-catalyzed alkaline hydrogen peroxide (Cu-AHP) pretreatment. Our hypothesis was that the lignin in zip-lignin poplar would be removed more easily than lignin in untransformed poplar during this alkaline pretreatment, resulting in higher sugar yields following enzymatic hydrolysis. We observed improved glucose and xylose hydrolysis yields for zip-lignin poplar compared to untransformed poplar following both alkaline-only pretreatment (56% glucose yield for untransformed poplar compared to 67% for zip-lignin poplar) and Cu-AHP pretreatment (77% glucose yield for untransformed poplar compared to 85% for zip-lignin poplar). Compositional analysis, glycome profiling, and microscopy all supported the notion that the ester linkages increase delignification and improve sugar yields. Essentially no differences were noted in the molecular weight distributions of solubilized lignins between the zip-lignin poplar and the control line. Significantly, when zip-lignin poplar was utilized as the feedstock, hydrogen peroxide, catalyst, and enzyme loadings could all be substantially reduced while maintaining high sugar yields.
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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".