AIE-Active, Stimuli-Responsive Fluorescent 2D Block Copolymer Nanoplatelets Based on Corona Chain Compression
Bibliographic record
Abstract
Aggregation-induced emission (AIE) represents a powerful tool in nanoscience as a result of enhanced luminescence in the condensed state. Although AIEgenic materials have been utilized in a wide range of applications, well-defined self-assembled nanoparticles with tailorable and uniform dimensions and morphology remain challenging to access. Herein, we use the seeded growth, living crystallization-driven self-assembly (CDSA) method to prepare size-tunable and uniform AIE-active 2D nanoplatelets from amphiphilic block copolymer (BCP) precursors with a crystallizable core-forming block and a corona-forming block to which tetraphenylethene (TPE) groups were covalently grafted as AIE-active pendants. The nanoplatelets were formed as a result of a solvophobicity-induced 1D to 2D morphology preference change, which accompanied the seeded growth of a BCP with a quaternized corona-forming block bearing the TPE luminogen. The 2D nanoplatelets exhibited a solvent-responsive fluorescent emission, and examples with coronas containing homogeneously distributed AIE-active TPE groups and Hg(II)-capturing thymine units exhibited excellent performance as proof-of-concept “turn-on” sensors for Hg(II) detection with a rapid response, high selectivity, and a low detection limit (5–125 × 10 –9 M, i.e., 1–25 ppb). The fluorescence intensity was found to be nonlinear with respect to analyte concentration and to increase with the area of the nanoplatelet. This behavior is consistent with a cooperative mechanism based on changes in the steric compression of the corona chains, which gives rise to a restriction of the intramolecular motion (RIM) effect.
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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) | 0.000 | 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 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".