A Study on Synthesis and Characterization of Biobased Carbon Nanoparticles from Lignin
Bibliographic record
Abstract
Carbon nanoparticles were synthesized using lignin as a renewable feedstock by employing a freeze-drying process followed by thermal carbonization. The effect of adding various amounts of KOH to a lignin solution on the solubility of the lignin, the freeze-drying process, the thermal stabilization of the freeze-dried lignin, and carbon nanoparticle formation was investigated through FTIR, DSC, SEM, TEM and surface area analysis. SEM investigations confirmed that the freeze-drying process caused the formation of lignin with a porous microstructure. TEM analysis indicates that the thermal stabilization of freeze-dried lignin prevented the formation of agglomerated carbon nanoparticles during the carbonization process. The smallest carbon nanoparticles were found to be 25nm and were prepared from the lignin precursor with 15% KOH.
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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".