Liquefaction of bamboo biomass and production of three fractions containing aromatic compounds
Bibliographic record
Abstract
Depolymerization of lignin to produce value-added aromatic monomers has attracted increasing attention since these monomers can be used as phenol replacement in production of phenolic resins. Here a one-pot depolymerization of bamboo lignin was investigated to obtain aromatic platforms with low molecular weight using acidic catalyst and ethanol. Three fractions (1#, 2#, and 3#) containing different molecular weight distributions of aromatic compounds could be efficiently extracted using water-organic solvent system via a stepwise fractionation process by gradual removal of solvent. The fractions distribution was found to be primarily dependent on the reaction temperature and time. When the temperature was increased from 160 °C to 200 °C, the yield of fractions containing aromatic products increased significantly from 19.1 wt% to 27 wt%, the same change trend was found by changing the time, and the yield of aromatic products increased from 22.4% to 26.7% with an increase of time from 10 min to 30 min. The bioproducts were characterized by using gas chromatography/mass spectrometry (GC–MS), gel permeation chromatography (GPC) and two-dimensional heteronuclear single-quantum coherence (2D HSQC NMR). As evidenced by GC–MS spectra, the three fractions were mainly comprised of phenolic derivatives, and the relative contents of phenolic compounds took up about 80% of the total area of each fraction. With the similar physiochemical properties of the fractions, aromatic platforms could provide a new paradigm of bamboo lignin utilization for renewable energy and value-added biochemicals.
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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".