Microbubble-enhanced cold plasma activation (MB-CPA) for surface functionalization of polymer nanoparticles from nanoprecipitation
Bibliographic record
Abstract
Cold plasma activation has been considered as a green and effective technological approach for functionalization of solid surfaces. In the current study, we introduce a versatile technique known as microbubble-enhanced cold plasma activation (MB-CPA) for surface modification of polymer plates and nanoparticles in flowing liquid phase. Corona plasma is generated at the inlet of a venturi tube, thereby creating a highly excited state in the air. Reactive nitrogen and oxygen species (RNOS) are transferred from the gas phase to the suspension flow in the form of plasma microbubbles. Two types of polymer-based nanoparticles in the suspension, namely polymethyl methacrylate (PMMA) of 16–154 nm and polycaprolactone (PCL) of 30–225 nm, are synthesized using nano-precipitation method. Through the degradation of model compounds, the polar organic solvent, acetone and ethanol, is found to promote or suppress the production of active species. In addition, the efficiency of the MB-CPA treatment depends on the solvent composition, making this technology a controllable and efficient strategy for the surface modification of nanoparticles. The findings indicate that plasma activation effectively reduces the negative surface zeta-potentials and induces aggregation and separation in the suspensions. The infrared spectra of the treated nanoparticles reveal that plasma activation leads to the formation of N H and C N bonds situated on the surface of PMMA. Our work demonstrates that organic-solvent involved MB-CPA emerges as a promising method for the surface modification of nanoparticles in the flow condition. • Surface functionalization of nanoparticles in fluidic suspension by cold plasma. • Microbubbles enhance active species transportation from air to liquid phase. • Controlled reactive species from plasma by tuning organic solvent in liquid phase.
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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".