Spectroscopic studies and evaluation of thermorheological properties of softwood and hardwood lignin
Bibliographic record
Abstract
Abstract Fourier transform infrared spectroscopy (FTIR) was used to determine characteristic absorption peaks of softwood kraft lignin and soda hardwood lignin. Remarkable spectral differences were noticed between the different lignins due to their various chemical structures. Proton nuclear magnetic resonance spectrometry (1H NMR) was employed to analyze the structure of lignin. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF) provided important data regarding the molecular weight distribution of lignin. Thermal stability of softwood lignin was found to be remarkably higher than that of hardwood lignin. Softening temperature and glass transition temperature of lignin were measured by differential scanning calorimetry (DSC) which was useful in selecting an optimal temperature profile during extrusion process. Rheological studies provided valuable information about the viscosity of lignins. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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 | 0.001 | 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".