Pretreatment of Wheat Straw with Phosphoric Acid and Hydrogen Peroxide to Simultaneously Facilitate Cellulose Digestibility and Modify Lignin as Adsorbents
Bibliographic record
Abstract
Effective valorization of lignin is crucial to achieve a sustainable, economic and competitive biorefinery of lignocellulosic biomass. In this work, an integrated process was proposed based on a concentrated phosphoric acid plus hydrogen peroxide (PHP) pretreatment to simultaneously facilitate cellulose digestibility and modify lignin as adsorbent. As a dominant constitutor of PHP pretreatment, H2O2 input and its influence on the overall fractionation/lignin modification performance was thoroughly investigated. Results indicated that wheat straw was fractionated more efficiently by increasing the H2O2 input. H2O2 input had a significant influence on the digestibility of the obtained cellulose-rich fraction whereby almost 100.0% cellulose-glucose conversion can be achieved even with only 0.88% H2O2 input. Besides, the adsorption capacity of lignin on MB was improved (74.3 to 210.1 mg g−1) due to the oxidative-modification in PHP pretreatment with H2O2 inputs. Regression analysis indicated that –COOH groups mainly governed the lignin adsorption (R2 = 0.946), which displayed the considerable adsorption capacities for typical cationic substances. This work shows a promising way to integrate the lignin modification concept into the emerging PHP pretreatment process with the dual goal of both cellulose utilization and lignin valorization.
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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".