Viscosity of Mono‐ and Polydisperse Mixtures of Photopolymer and Rigid Spheres for Manufacturing of Engineered Composite Materials Using Vat Photopolymerization
Why this work is in the frame
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Bibliographic record
Abstract
Vat photopolymerization (VP) additive manufacturing involves selectively curing low‐viscosity photopolymers via exposure to ultraviolet light in a layer‐wise fashion. Dispersing filler materials in the photopolymer enables tailored end‐use properties, but also increases the viscosity and the timescale associated with interparticle network structural recovery postshear. These rheological properties influence self‐leveling and recoating of the liquid photopolymer mixture during VP. Herein, viscosity of photopolymer and rigid spherical glass microparticles (filler) is experimentally determined as a function of filler fraction, filler size distribution (mono‐ and polydisperse), shear rate, and temperature, which are important VP process parameters. Employing existing viscosity models for mono‐ and polydisperse polymer mixtures demonstrates that particle–particle interactions and the formation of nonspherical clusters of particles strongly affect the viscosity of both monodisperse and polydisperse mixtures with particle volume fractions > 0.05 due to agglomeration/deagglomeration of clusters at elevated shear rates. Consequently, unmodified viscosity models, which assume uniformly dispersed, rigid, spherical particles, are applicable only for mixtures with particle volume fractions < 0.05. It is shown that modifying model parameters such as the fluidity limit and intrinsic viscosity, which explicitly account for nonspherical clusters of particles, improves agreement between viscosity models and experiments, in particular when using a fractal approach.
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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