Size-controlled synthesis of bioinspired polyserotonin nanoparticles with free radical scavenging activity
Bibliographic record
Abstract
Polyserotonin-based nanoparticles are a new class of bioinspired nanomaterial with recently demonstrated therapeutic potential for future clinical applications. It is therefore important to establish a robust and rapid method of synthesizing polyserotonin nanoparticles (PSeNP) in the size range ideal for in vivo utilization. Since the formation of PSeNP is base-catalyzed, here we report the influence of solution pH, in the presence of different base systems, on the kinetics of PSeNP formation and physico-chemical properties of the resulting nanoparticles. We show that the rate of formation and the size of PSeNP depend on both the nature of the base and the initial pH of the reaction. We have also improved the kinetics of particle formation by performing the synthesis at an elevated temperature (60 °C), leading to a dramatic reduction in synthesis time from days to hours. This presents a significant advance in the efficiency of PSeNP synthesis and provides a facile approach in tuning the size of nanoparticles to suit various applications. Furthermore, we show that similar to serotonin, PSeNP also exhibits free radical scavenging property. Our results demonstrate that PSeNP has the potential to become a key player in the advancement of nanotechnology-mediated antioxidative therapy.
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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) | 0.001 | 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 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".