Fully Oxygen-Tolerant Visible-Light-Induced ATRP of Acrylates in Water: Toward Synthesis of Protein-Polymer Hybrids
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Bibliographic record
Abstract
Over the last decade, photoinduced ATRP techniques have been developed to harness the energy of light to generate radicals. Most of these methods require the use of UV light to initiate polymerization. However, UV light has several disadvantages: it can degrade proteins, damage DNA, cause undesirable side reactions, and has low penetration depth in reaction media. Recently, we demonstrated green-light-induced ATRP with dual catalysis, where eosin Y (EYH2) was used as an organic photoredox catalyst in conjunction with a copper complex. This dual catalysis proved to be highly efficient, allowing rapid and well-controlled aqueous polymerization of oligo(ethylene oxide) methyl ether methacrylate without the need for deoxygenation. Herein, we expanded this system to synthesize polyacrylates under biologically relevant conditions using CuII/Me6TREN (Me6TREN = tris[2-(dimethylamino)ethyl]amine) and EYH2 at ppm levels. Water-soluble oligo(ethylene oxide) methyl ether acrylate (average Mn = 480, OEOA480) was polymerized in open reaction vessels under green light irradiation (520 nm). Despite continuous oxygen diffusion, high monomer conversions were achieved within 40 min, yielding polymers with narrow molecular weight distributions (1.17 ≤ D̵ ≤ 1.23) for a wide targeted DP range (50–800). In situ chain extension and block copolymerization confirmed the preserved chain end functionality. In addition, polymerization was triggered/halted by turning on/off a green light, showing temporal control. The optimized conditions also enabled controlled polymerization of various hydrophilic acrylate monomers, such as 2-hydroxyethyl acrylate, 2-(methylsulfinyl)ethyl acrylate), and zwitterionic carboxy betaine acrylate. Notably, the method allowed the synthesis of well-defined acrylate-based protein-polymer hybrids using a straightforward reaction setup without rigorous deoxygenation.
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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) | 0.001 | 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 it