Interfacing a Tetraphenylethene Derivative and a Smart Hydrogel for Temperature-Dependent Photoluminescence with Sensitive Thermoresponse
Bibliographic record
Abstract
We report, for the first time, the design and synthesis of thermoresponsive (TR) photoluminescent (PL) hydrogel nanoparticles, with a core consisting of poly[styrene-co-(N-isopropylacrylamide)] (PS-co-PNIPAM) and a PNIPAM-co-PAA shell. PAA represents polyacrylic acid which interacts with our emitting molecule 1,2-bis[4-(2-triethylammonioethoxy)phenyl]-1,2-diphenylethene dibromide (d-TPE). The electrostatic interaction between each water-soluble d-TPE molecule and two AA repeat units activates these d-TPE molecules to exhibit strong PL. Our d-TPE doped PS-co-PNIPAM/PNIPAM-co-PAA particles in water display remarkable TR PL: the emission intensity decreased in the course of heating from 2 to 80 °C and recovered during cooling from 80 to 2 °C. Such linear, reversible, and sensitive TR PL is achieved by the use of both PAA and PNIPAM as the shell polymeric chain and by careful optimization of the d-TPE to AA feed molar ratio. Thus, the emission of the d-TPE molecule is affected sensitively by temperature. In addition to such an exceptionally temperature-dependent PL, the presence of CrO4(2-) resulted in the decrease of the emission intensity, which was also temperature-dependent. The present study provides a unified conceptual methodology to engineer functional water-dispersible hydrogel nanoparticles that are stimuli-responsive with the potential to advance various PL-based 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".