Microfluidic Printheads for Highly Switchable Multimaterial 3D Printing of Soft Materials
Bibliographic record
Abstract
Abstract Extrusion‐based 3D printing uses multiple printing nozzles to produce multimaterial combinations. It requires complex alignment and accuracy control as well as an effective start‐stop procedure to print intermittently, which has so far proven difficult to achieve. A recently developed simpler approach is to sequence the multimaterial inks through a single nozzle that will avoid both of these requirements. Although it has been successfully demonstrated for viscoelastic inks, adapting it to more widely available viscous inks is still a challenge. Here, a dynamically controllable multimaterial single‐nozzle 3D printing printhead is demonstrated that is capable of printing both viscous and viscoelastic materials. It uses pneumatically pulsed injection of multiple viscous inks into a central nozzle for printing. It is shown through simulations and experiments that a crucial but narrow range active reverse pressure in static reservoir(s) is essential to provide accurate switching in viscous inks, which is unlike viscoelastic inks that have a wider operating range and do not require a backflow control. Using this approach, high frequency and fast switching between four different viscous materials for pixelated printing of different materials which can be used to embed different functional properties in a variety of 2D and 3D shapes using commercially available polymers are demonstrated.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".