Effect of Softwood Kraft Lignin Fractionation on the Dispersion of Multiwalled Carbon Nanotubes
Bibliographic record
Abstract
Dispersion of carbon nanotubes has been a major obstacle for the application and utilization in composites. In this study, it was observed that a small amount of softwood Kraft lignin (SKL) could facilitate the dispersion of multiwalled carbon nanotubes (MWCNTs) in dimethylformamide (DMF) solutions. Classification of the technical SKL by solvent fractionation revealed distinct differences in MWCNT dispersibility. Using Raman spectroscopy the efficacy of the various SKL fractions to disperse MWCNT’s in DMF was studied. Of the fractions investigated it was found that the methanol/methylene chloride (70/30, v/v) soluble fraction (F 4 SKL) performed the best. Characterization of the various fractions indicates that lignin structure and propensity to form intermolecular (π– π and hydrogen bonding) interactions is critical for MWCNT dispersion.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".