Micro-extrusion of organic inks for direct-write assembly
Bibliographic record
Abstract
Direct-write assembly is a highly versatile microfabrication technique used to create microfluidic networks by the robotic deposition of a fugitive ink onto a moving stage. To optimize the resulting shape of the microchannel, the translational speed of the moving stage has to closely match the linear velocity of the fugitive ink at the micro-nozzle exit. In this work, we have performed a comprehensive characterization of the micro-extrusion process of organic fugitive inks through a nozzle and characterized the rheological properties of petroleum jelly-based organic inks with various microcrystalline wax contents (10 to 40 wt%). The local microcrystal concentration has been probed using polarized optical microscopy and Raman spectroscopy. Small amplitude oscillatory shear tests in a vane geometry have revealed a solid-like structure of the organic inks, and a strong shear-thinning behavior of the complex viscosity. Particle tracking velocimetry (PTV) experiments performed in a glass microchannel have suggested the occurrence of apparent slip, showing a microcrystal depletion layer near the nozzle wall and a plug flow in the remainder of the micro-nozzle. From Raman spectroscopy and polarized microscopy performed on extruded samples, a crystal free layer was observed and estimated to be approximately 10–20 µm thick (or 2–4% of the microcapillary diameter), explaining the strong apparent wall slip behavior.
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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 | 0.001 |
| 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 | 0.001 |
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".