One-Pot Synthesis: Polymers, Hydrogel and Nanoparticles from Natural Extracted Green Materials
Bibliographic record
Abstract
The development of green nanomaterials from renewable resources is the utmost importance in a context of sustainable development. In this work, we describe the preparation of green nanomaterials of polymer, nanoparticles and hydrogel that arise from the natural extraction of myrcene and ocimene using air/water as a catalyst in one pot. Interestingly, we noticed the polymerization of myrcene in situ condition with attaining high molecular mass and low polydispersity index. The explanation of the polymerization kinetics that were investigated through nuclear magnetic resonance (NMR) spectroscopy and gel permeation chromatography (GPC). Importantly, we elucidated the preparation of green hydrogel by using water as a crosslinking agent. Further, we also described green nanoparticle formation by using emulsion method. The nanoparticles that obtained were 130 ±10 nm in terms of size and were found to be monodispersed. With these novel ‘green nanomaterials’, we project that they will be beneficial to multiple nanotechnological applications.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".