Comparative study of acid- and alkali-catalyzed 1,4-butanediol pretreatment for co-production of fermentable sugars and value-added lignin compounds
Bibliographic record
Abstract
BACKGROUND: Organosolv pretreatment is one of the most efficient methods for delignification and boosting biomass saccharification. As compared to typical ethanol organosolv pretreatments, 1,4-butanediol (BDO) organosolv pretreatment is a high-boiling-point solvent pretreatment, which can generate low pressure in the reactor during high temperature cooking that improves the operation safety. Although several studies showed that organosolv pretreatment can lead to effective delignification and enhancement in glucan hydrolysis, there has been no studies on acid- and alkali-catalyzed BDO pretreatment, as well as their comparison on promoting biomass saccharification and lignin utilization. RESULTS: It was shown that BDO organosolv pretreatment was more effective in removing lignin from poplar as compared with typical ethanol organosolv pretreatment under the same pretreatment conditions. HCl-BDO pretreatment with 40 mM acid loading led to 82.04% of original lignin removed from biomass, as compared to the lignin removal of 59.66% in HCl-Ethanol pretreatment. Besides, acid-catalyzed BDO pretreatment was more effective in improving the enzymatic digestibility of poplar than alkali-catalyzed BDO pretreatment. As a result, HCl-BDO with acid loading of 40 mM provided a good enzymatic digestibility of cellulose (91.16%) and the maximum sugar yield of 79.41% from original woody biomass. The linear correlations between physicochemical structure (e.g., fiber swelling, cellulose crystallinity, crystallite size, surface lignin coverage and cellulose accessibility) changes of BDO pretreated poplar and enzymatic hydrolysis were plotted to figure out the main factors that influenced biomass saccharification. Moreover, acid-catalyzed BDO pretreatment mainly brought about the phenolic hydroxyl (PhOH) groups formation in lignin structure, while alkali-catalyzed BDO pretreatment mostly led to the lower molecular weight of lignin. CONCLUSIONS: Results indicated that the acid-catalyzed BDO organosolv pretreatment could significantly improve enzymatic digestibility of the highly recalcitrant woody biomass. The great enzymatic hydrolysis of glucan resulted from increased cellulose accessibility, which mostly associated with the higher degree of delignification and hemicellulose solubilization, as well as the more increase in fiber swelling. Besides, lignin was recovered from the organic solvent, which could be used as natural antioxidants. The formation of phenolic hydroxyl groups in lignin structure and the lower molecular weight of lignin contributed to its greater radical scavenging capacity.
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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".