Polystyrene Sulfonate with Well-Defined Structure as Lignosulfonate Analogue for Promoting Lignocellulose Enzymatic Saccharification
Bibliographic record
Abstract
Abstract Background: Water-soluble lignin (particularly lignosulfonate, LS) has been well documented for its positive impact on enzymatic saccharification for lignocellulose. Even though, the promotion mechanism of LS hasn’t been fully understood. All researches paid all attentions on the natural lignin or its derivatives. Whereas the structure of natural lignin is too complex and not easily to be tailored functional groups. To further our understanding on the promotion mechanism of water-soluble lignin to enzymatic saccharification for lignocellulose and also to pursue better alternatives with different skeleton structure other than natural lignin or its derivatives, therein we reported a synthetic soluble linear aromatic polymer- sodium polystyrene sulfonate (PSS) with well-defined structure to mimic LS for enhancing the enzymatic saccharification efficiency. Results: At the cellulase loading of 10 FPU/g-glucan, the glucose yield of green liquor pretreated poplar increased from 39.8% for the control to 60.3% with PSS addition of 0.1 g/g-substrate. It outperformed LS with addition of 0.2 g/g-substrate by 4.6%. The underlying mechanism was unveiled using Quartz Crystal Microbalance and the results confirmed that the as-formed complexes of cellulase-PSS, which effectively reduced non-productive binding and eventually improving the saccharification efficiency, were only half thickness and with much lower shear moduli than those of LS. Conclusions: The synthetic lignin mimics with controllable structures offer us more opportunities to understand the promotion mechanism of soluble lignins on lignocellulose enzymatic saccharification.
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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".