Vinyl Sulfonate Esters: Efficient Chain Transfer Agents for the 3D Printing of Tough Photopolymers without Retardation
Bibliographic record
Abstract
The formation of networks through light-initiated radical polymerization allows little freedom for tailored network design. The resulting inhomogeneous network architectures and brittle material behavior of such glassy-type networks limit the commercial application of photopolymers in 3D printing, biomedicine, and microelectronics. An ester-activated vinyl sulfonate ester (EVS) is presented for the rapid formation of tailored methacrylate-based networks. The chain transfer step induced by EVS reduces the kinetic chain length of the photopolymer, thus shifting the gel point to higher conversion, which results in reduced shrinkage stress and higher overall conversion. The resulting, more homogeneous network is responsible for the high toughness of the material. The unique property of EVS to promote nearly retardation-free polymerization can be attributed to the fact that after the transfer step no polymerizable double bond is formed, as is usually seen in classical chain transfer agents. Laser flash photolysis, theoretical calculations, and photoreactor studies were used to elucidate the fast chain transfer reaction and exceptional regulating ability of EVS. Final photopolymer networks exhibit improved mechanical performance making EVS an outstanding candidate for the 3D printing of tough photopolymers.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".