Thermal Characteristics of Lignin Residue from Industrial Processes
Bibliographic record
Abstract
Many pulp mills and biorefineries today are focusing on the utilization of their residual lignin for economic return. Although lignin can be burned to produce energy, it also has the potential for the production of value-added products. Technical lignins have modified structure and contain different impurities, which depend on the original material, as well as the extraction process. Among the various techniques for lignin extraction, kraft and steam explosion processes are the most commonly used in the pulping and biorefinery industries, respectively. The objective of this work was to compare the thermal behavior of industrial lignins produced from kraft pulping and steam explosion, with that of their chemically extracted, purified forms. It was found that the purified lignins have very similar thermal properties to one another, while impurities in the industrial lignins significantly alter their thermal behavior, and hence their potential in value-added applications. The percentage of degradation from 200 to 600 °C and glass transition temperature of original steam-exploded lignin was 68.5% and 149.16 °C, while of original kraft lignin was 26.0% and 109.82 °C. These values were altered after purification to 61.0% and 158.99 °C for steam-exploded lignin; and to 40.0% and 129.82 °C for kraft lignin, respectively.
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 | 0.001 |
| 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".