Physicochemical Limitations of Capillary Models Applied to High-Concentration Polymer Solutions
Bibliographic record
Abstract
Advances in binder jet printing (BJP) require the development of new binder-powder systems, for example, to increase compatibility with better performance metal alloys or to increase the strength of parts using stronger binders. The dynamics of binder absorption are principally understood through capillary models. However, validation of these models in BJP has focused on variation of powder properties. Using a design-of-experiments approach and an optical observation method to track absorption of droplets, this study tests the influence of fluid properties on absorption time against the predictions of capillary models. Properties specific to polymeric binders, such as molecular weight and entanglement state, are also considered. Capillary models are found to be generally accurate in predicting absorption time in dilute systems; however, these predictions are not accurate for highly concentrated binder solutions. The effect of polymer entanglement becomes prevalent as the solution concentration increases, which can also potentially occur as a result of increased evaporation due to powder bed heating. Specifically, concentrated solutions close to the onset of entanglement will absorb much more slowly than predicted. Future models of BJP systems must account for the possibility of polymer entanglement throughout the absorption process. Improved models will provide a more accurate understanding of the flow and solidification of the binder in the powder, allowing faster development of new binders for improved performance in printing.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".