Quasi‐Chromophores Segregated by Single‐Chain Nanoparticles of Fluorinated Zwitterionic Random Copolymers Showing Remarkably Enhanced Fluorescence Emission Capable of Fluorescent Cell Imaging
Bibliographic record
Abstract
Organic and polymer fluorescent nanomaterials are a frontier research focus. Here in this work, a series of fluorinated zwitterionic random copolymers end-attached with a quasi-chromophoric group of pyrene or tetraphenylethylene (TPE) are well synthesized via atom transfer radical polymerization with activators regenerated by electron transfer (ARGET ATRP). Those random copolymers with total degree of polymerization 100 or 200 are able to produce fluorescent single-chain nanoparticles (SCNPs) through intra-chain self-folding assembly with quite uniform diameters in the range of 10-20 nm as characterized by dynamic light scattering and transmission electron microscopy. By virtue of the segregation or confinement effect, both SCNPs functionalized with pyrene or TPE group are capable of emitting fluorescence, with pyrene tethered SCNPs exhibiting stronger fluorescence emission reaching the highest quantum yield ≈20%. Moreover, such kind of fluorescent SCNPs manifest low cytotoxicity and good cell imaging performance for Hela cells. The creation of fluorescent SCNPs through covalently attached one quasi-chromophore to the end of one fluorinated zwitterionic random copolymer provides an alternative strategy for preparing polymeric luminescence nanomaterials, promisingly serving as a new type of fluorescent nanoprobes for biological imaging applications.
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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".