Using Green γ-Valerolactone/Water Solvent To Decrease Lignin Heterogeneity by Gradient Precipitation
Bibliographic record
Abstract
Lignin heterogeneity, involving complex structure and high polydispersity, is a key challenge that restricts its value-added applications. Fractionation of heterogeneous lignin into several homogeneous subdivisions is an attractive and practical strategy to overcome this limitation. In this work, γ-valerolactone (GVL), a sugar-derived product, was used as a green solvent for lignin fractionation when mixed with water. The enzymatic hydrolysis lignin (EHL) was subdivided into three different fractions (F1, F2, and F3) by dissolving it completely in 60% aqueous GVL and then following gradient precipitation in 40%, 30%, and 5% aqueous GVL solutions, sequentially. Detailed characterization techniques were conducted to provide a comprehensive evaluation of the three obtained lignin fractions. Moreover, the proposed fractionation mechanism was further investigated on the basis of Kamlet–Taft parameters. The gel permeation chromatography (GPC) analyses showed that the three fractions presented lower polydispersity than the parent EHL and, furthermore, a gradual decreasing molecular weight due to the different solubility of various molecular weight lignins in aqueous GVL solvents. The structural analyses revealed that with the decrease of molecular weight, the guaiacyl unit content in lignin fractions decreased, with significant increases of functional groups (i.e., aromatic/aliphatic hydroxyl and carboxyl groups). The solvent recycling study showed that the aqueous GVL had a high recovery, and the recycled GVL had the same lignin fractionation performance as fresh GVL. Overall, compared with traditional fractionation using multiple organic solvents, the present work provides a green and efficient route to fractionate lignin and, therefore, significantly decreases its molecular weight polydispersity and structural heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".