Chemical structure–property relationships of photocurable monomers/macromers: Potential binder candidates for ceramic/metal vat photopolymerization
Bibliographic record
Abstract
Successful ceramic/metal vat photopolymerization (VPP) depends on the precise optimization of photocurable polymers to achieve ideal component properties. Most photocurable polymers consist of binary systems with monomers/macromers of varying functionalities. Crosslinking polymerization in VPP, particularly concerning monomer/macromer chemical structure , has not been extensively researched. This study systematically investigates the effects of three difunctional monomers , three difunctional macromers, and three multifunctional monomers . 18 combinations were prepared using difunctional monomers/macromers and multifunctional monomers at a 50:50 ratio. The study examined how linker chain length, ethoxylation, functional groups, and functionality impact properties critical for VPP processes, including photocuring behavior, mechanical properties, and shrinkage. Curing depth measurements showed that resins with rigid monomers and longer chain lengths had a higher critical energy ( E c ), while flexible monomers had a lower E c . Methacrylate-containing formulations exhibited a higher E c than acrylates . Although resins with higher functionality ( f a v e ) generally showed higher E c , this trend was not always consistent. Photo-DSC results showed that ethoxylated TMPTA slows down crosslinking polymerization in methacrylate-containing resins but accelerates it in acrylate formulations. Moreover, combining tetrafunctional monomer with methacrylate enhanced the polymerization rate of methacrylate. The compression tests showed that an excess of ethylene oxide groups in the chemical structure of used monomers introduces a higher degree of flexibility and softness to the cured structure, leading to a reduction in mechanical properties. Furthermore, it was observed that methacrylate-containing resins shrank more compared to their acrylate-containing counterparts. Finally, five potential candidates were proposed for ceramic/metal VPP.
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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) | 0.002 | 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".