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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".