Photophysical and Optical Properties of Semiconducting Polymer Nanoparticles Prepared from Hyaluronic Acid and Polysorbate 80
Bibliographic record
Abstract
A nanoprecipitation procedure was utilized to prepare novel diketopyrrolopyrrole-based semiconducting polymer nanoparticles (SPNs) with hyaluronic acid (HA) and polysorbate 80. The nanoprecipitation led to the formation of spherical nanoparticles with average diameters ranging from 100 to 200 nm, and a careful control over the structure of the parent conjugated polymers was performed to probe the influence of π-conjugation on the final photophysical and thermal stability of the resulting SPNs. Upon generation of a series of novel SPNs, the optical and photophysical properties of the new nanomaterials were probed in solution using various techniques including transmission electron microscopy, dynamic light scattering, small-angle neutron scattering, transient absorption, and UV-vis spectroscopy. A careful comparison was performed between the different SPNs to evaluate their excited-state dynamics and photophysical properties, both before and after nanoprecipitation. Interestingly, although soluble in organic solution, the nanoparticles were found to exhibit aggregative behavior, resulting in SPNs that exhibit excited-state behaviors that are very similar to aggregated polymer solutions. Based on these findings, the formation of HA- and polysorbate 80-based nanoparticles does not influence the photophysical properties of the conjugated polymers, thus opening new opportunities for the design of bioimaging agents and nanomaterials for health-related 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 | 0.000 |
| 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".