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 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) | 0.001 | 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".