Characterization of four different lignins as a first step toward the identification of suitable end‐use applications
Bibliographic record
Abstract
ABSTRACT To determine the most appropriate use of lignin, surface, structural, and thermal characteristics of lignin was investigated in this work. It was observed that kraft lignin (KL), the lignin of prehydrolysis liquor (LPHL), lignosulfonate of NSSC process (LSL), and lignosulfonates (LSs) of sulfite pulping process had 0.67, 0.25, 0.90, and 1.52–2.25 meq/g anionic charge density, and 6.3, 2.1, 10.1, and 8.8–10.1 nm hydrodynamic diameter, respectively. These results suggested that LSL and LSs could be used more effectively than other lignin as filler modifiers, flocculants, and dispersants. The combustion studies of the lignin samples suggested that KL and LPHL combusted more efficiently than other samples, as they had high heating (calorific) values of 27.02 and 19.2 MJ/kg, the apparent activation energy of 126.64 and 99.14 kJ/mol based on Flynn–Wall–Ozawa method and 122.16 and 94.73 kJ/mol based on Kissinger–Akahira–Sunose and no ash, respectively. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42336.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".