Amphiphilic Lignin Nanoparticles Made from Lignin-Acrylic Acid-Methyl Methacrylate Copolymers
Bibliographic record
Abstract
In this study, a novel amphiphilic KL-AA-MMA nanoparticle was prepared through the graft copolymerization of kraft lignin (KL) with acrylic acid (AA) and methyl methacrylate (MMA), using potassium persulfate as an initiator in a water/dimethyl sulfoxide solvent medium, which was followed by the nanoprecipitation technique using dimethylformamide as a solvent and deionized water as an antisolvent. The successful graft polymerization was verified by 1H-nuclear magnetic resonance (NMR), 31P-NMR, and Fourier transform infrared (FTIR) analyses; and the grafting yield of the generated KL-AA-MMA copolymer ranged from 68.2% to 96.5%. Transmission electron microscopy (TEM) observation revealed the formation of amorphous KL-AA-MMA nanoparticles. Additionally, KL-AA-MMA9 nanoparticles with the highest yield exhibited the minimum hydrodynamic diameter and polydispersity of 261 nm and 0.153, respectively. Moreover, the amphiphilicity of KL-AA-MMA nanoparticles was significantly improved by the grafting of MMA monomers. Finally, the adsorption performance of KL-AA-MMA nanoparticles at the xylene interface was evaluated by a quartz crystal microbalance with dissipation (QCM-D). The results demonstrated that the most amphiphilic sample, KL-AA-MMA9 nanoparticles, with the smallest hydrodynamic size displayed the highest adsorption on the oil/water interface. This product provides a wide range of applications in oil/water emulsions.
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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) | 0.000 | 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 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".