Lignin-associated factors impede enzymatic hydrolysis of hydrothermally pretreated birch and poplar wood
Bibliographic record
Abstract
Pretreatment is a critical step in processing wood for various applications, particularly in the production of biofuels, biochemicals, and biomaterials. Our previous study shows that the enzymatic hydrolysis efficiency of hydrothermally pretreated birch (80.8 %) is markedly superior to that of poplar (21.5 %) treated in the same conditions (180°C, 50 min). Based on that, this study investigated the effects of lignin-related factors on the enzymatic hydrolysis of hydrothermally pretreated birch and poplar wood. The milled wood lignin (MWL) and lignin-carbohydrate complex (LCC) were extracted from birch and poplar wood before and after pretreatment. The two factors—non-productive binding and physical blocking derived from lignin that primarily influence the efficiency of enzymatic hydrolysis of biomass are specifically analyzed in this study. By the enzymatic hydrolysis test of holocellulose, it was confirmed that the physical blocking caused by surface lignin and LCC in pretreated birch is significantly lower than that in poplar either in an enzyme loading of 10 FPU/g or 25 FPU/g. The data of surface lignin coverage, LCC composition, and LCC contents in birch and poplar samples supports this result. Moreover, the addition of isolated lignin on Avicel test proved that the lignin from pretreated birch has less non-productive binding than that from poplar. These two factors together contributed to the higher efficiency of enzymatic hydrolysis of hydrothermally pretreated birch than poplar. • Lignin's physical blocking and non-productive binding can affect enzymatic hydrolysis. • Physical blocking from lignin in birch is much weaker than that from lignin in poplar. • Lignin in birch has less non-productive binding than lignin in poplar.
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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 | 0.000 |
| 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 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".