Impact of lignin polymer backbone esters on ionic liquid pretreatment of poplar
Bibliographic record
Abstract
BACKGROUND: to reduce lignin content or modify its composition to overcome the inherent recalcitrance that it imposes on lignocellulosic biomass during processing. Here, transgenic poplar lines in which monolignol ferulate conjugates were synthesized during cell wall development to introduce, during lignification, readily cleavable ester linkages into the lignin polymer backbone (i.e., "zip lignin"), along with wild-type (WT) controls, were pretreated with different ionic liquids (ILs). RESULTS: The strategic introduction of ester bonds into the lignin backbone resulted in increased pretreatment efficiency and released more carbohydrates with lower energy input. After pretreatment with any of three different ILs, and after limited saccharification, the transgenic poplars, especially those with relatively higher amounts of incorporated monolignol ferulate conjugates, yielded up to 23% higher sugar levels compared to WT plants. CONCLUSION: Our findings clearly demonstrate that the introduction of ester linkages into the lignin polymer backbone decreases biomass recalcitrance in poplar has the potential to reduce the energy and/or amount of IL required for effective pretreatment, and could enable the development of an economically viable and sustainable biorefinery process.
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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".