Facile Obtainment of Fluorescent PEG Hydrogels Bearing Pyrene Groups by Frontal Polymerization
Bibliographic record
Abstract
Frontal polymerization (FP) was used to prepare poly(ethylene glycol) methyl ether acrylate (PEGMA) fluorescent polymer hydrogels containing pyrenebutyl pendant groups as fluorescent probes. The polymerization procedure was carried out under solvent-free conditions, with different molar quantities of pyrenebutyl methyl ether methacrylate (PybuMA) and PEGMA, in the presence of tricaprylmethylammonium (Aliquat 336®) persulfate as a radical initiator. The obtained PEGPy hydrogels were characterized by FT-IR spectroscopy, confirming the effective incorporation of the PybuMA monomer into the polymer backbone. The thermal properties of the hydrogels were determined using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). After immersing the hydrogels in deionized water at 25 °C and pH = 7, their swelling behavior was investigated by mass gain at different pH and temperature values. The introduction of PybuMA comonomer into the hydrogel resulted in a decreased swelling ability due to the hydrophobicity of PybuMA. The optical properties of PEGPy were determined by UV-visible absorption and fluorescence spectroscopies. Both monomer and excimer emission bands were observed at 379–397 and 486 nm, respectively, and the fluorescence spectra of the PEGPy hydrogel series were recorded in different solvents to explore the coexistence of monomer and excimer emissions.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".