Embedded 3D Printing of Newtonian Fluids in Elasto-viscoplastic Matrix
Bibliographic record
Abstract
Embedded 3D printing (EM3D) enables freeform patterning of soft materials by extruding ink into a yield-stress supporting matrix. While prior studies have focused on viscoplastic or shear-thinning inks, printing Newtonian fluids─such as silicone oil and liquid metal─remains challenging due to (i) matrix yielding induced by needle motion and (ii) Rayleigh–Plateau (RP) instability driven by interfacial tension. In this study, we investigate the EM3D of Newtonian inks with extremely low surface tension (silicone oil) and extremely high surface tension (Galinstan liquid metal), embedded in an elasto-viscoplastic Laponite matrix. Flow visualization with particle image velocimetry reveals that matrix yielding around the needle scales with (γ̇ c /γ̇ Y ) 1/3, where γ̇ c = U / d is the characteristic shear rate and γ̇ Y = 2π f γ Y is the yield threshold derived from amplitude sweep rheology. We demonstrate that printing orthogonal to a straight-needle aggravates matrix yielding and compromises print fidelity. To resolve this issue, we propose a bent-needle geometry, which reduces the yielded region and improves filament stability by minimizing stress propagation along the needle path. To address RP instability, we derive a theoretical stability criterion that balances interfacial tension Γ and yields stress τ Y, given by τ Y ∝ Γ/ d . This prediction is experimentally validated using Newtonian inks with distinct interfacial tensions (35 mN/m for silicone oil and 345 mN/m for Galinstan). Our findings provide a unified design framework for Newtonian-ink EM3D, incorporating both rheological and geometric strategies to overcome flow-induced instability. This work expands the accessible material space for EM3D by providing fundamental insights into fluid–matrix interactions, offering practical guidelines for reliable printing of Newtonian inks in soft electronics and bioprinting applications.
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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) | 0.001 | 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".