Preparation and Characterization of Various Kraft Lignins and Impact on Their Pyrolysis Behaviors
Bibliographic record
Abstract
Lignin-derived chemicals and fuel products have received much attention in the context of bio-refinery. In this study we prepared and characterized various lignin samples from black liquor (BL), a major byproduct from the pulp and paper manufacturing processes, and studied the pyrolysis behaviors of these isolated lignin samples at 600 °C under a N 2 atmosphere. Three lignin samples were isolated from the Kraft pulping BL of Loblolly pine linerboard grade process (KL, obtained by direct evaporation; L10, obtained by precipitation at pH 10; L3, obtained by precipitation at pH 3), while another three were from the Kraft pulping BL of Loblolly pine bleach grade process (KB, B10, B3). The inorganic elements present in lignin samples were analyzed using an inductively coupled plasma (ICP) analyzer. The KL and KB samples contained a larger amount of inorganic elements, especially Na and S, than did the other lignin samples. Owing to large quantities of inorganic elements, the ash contents in KL and KB were higher than in the precipitated lignin samples (L10, B10, L3, and B3). These inorganic elements have a significant effect on the subsequent pyrolysis process, as a result, more extensive depolymerization of lignin occurred in KL and KB samples, leading to the formation of fewer pyrolysis oils, with lower molecular weights. NMR analyses of pyrolysis oils show that more methoxyl groups and ether bonds were cleaved, resulting in fewer methoxyl groups and aromatic C–O bonds for the KL and KB heavy oil than for the precipitated lignin heavy oil.
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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 | 0.000 |
| 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".