Stepwise Ethanol-Water Fractionation of Enzymatic Hydrolysis Lignin to Improve Its Performance as a Cationic Dye Adsorbent
Bibliographic record
Abstract
In this work, lignin fractionation is proposed as an effective approach to reduce the heterogeneity of lignin and improve the adsorption and recycle performances of lignin as a cationic dye adsorbent. By stepwise dissolution of enzymatic hydrolysis lignin in 95% and 80% ethanol solutions, three lignin subdivisions (95% ethanol-soluble subdivision, 80% ethanol-soluble subdivision, and 80% ethanol-insoluble subdivision) were obtained. The three lignin subdivisions were characterized by gel permeation chromatography (GPC), FTIR, 2D-NMR and scanning electron microscopy (SEM), and their adsorption capacities for methylene blue were compared. The results showed that the 80% ethanol-insoluble subdivision exhibited the highest adsorption capacity and its value (396.85 mg/g) was over 0.4 times higher than that of the unfractionated lignin (281.54 mg/g). The increased adsorption capacity was caused by the enhancement of both specific surface area and negative Zeta potential. The maximum monolayer adsorption capacity of 80% ethanol-insoluble subdivision by adsorption kinetics and isotherm studies was found to be 431.1 mg/g, which was much higher than most of reported lignin-based adsorbents. Moreover, the 80% ethanol-insoluble subdivision had much higher regeneration yield (over 90% after 5 recycles) compared with the other two subdivisions. Consequently, the proposed fractionation method is proved to be a novel and efficient non-chemical modification approach that significantly improves adsorption capacity and recyclability of lignin.
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) | 0.000 | 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 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".