Extraction of Lignin from a Coproduct of the Cellulosic Ethanol Industry and Its Thermal Characterization
Bibliographic record
Abstract
Lignin was extracted from the solid coproduct of a lignocellulosic ethanol production by a solid-liquid extraction method using N,N-dimethyl formamide. This coproduct was the residue of a steam explosion pretreatment followed by enzymatic hydrolysis process. The coproduct was used as received and also after washing. Lignin content of the solid coproduct was reduced from 63% to 43% after lignin extraction. Fourier transform infrared spectroscopy (FTIR), molecular weight measurement (GPC), and elemental analysis provided information about the chemical structure and molecular weight of the fractions. The extracted lignin had lower molecular weight (~ 5000 Da.) and higher carbon content (63%) compared to the residue of extraction (Mw ≈ 8000 Da. and carbon % = 48%). Thermal stability measurements of the samples by thermogravimetry (TGA) showed that the extracted lignin had the highest carbon residue. Effects of different heating cycles on the glass transition temperature (Tg) were measured. The Tg of the soluble fraction was lower than that of the coproduct. Results of the X-ray diffraction (XRD) showed the crystalline structure of cellulose in both the coproduct and the solid residue after extraction. This extraction and material characterization is helpful for liquid processes such as solution spinning or electrospinning. The thermal properties can be used for optimization of heat treatment processes such as carbonization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".