Hydrophilic silicone-based ink derived from amphiphilic siloxane oligomers for the vat photopolymerization printing of embedded-channel fluidic devices
Bibliographic record
Abstract
The emergence of vat photopolymerization (VP) printing as an alternative fabrication method for fluidic devices has led to the rapid development of silicone-based resin material. However, most silicone-based resin materials are hydrophobic in nature, rendering them unsuitable for biomedical applications without post-processing. Herein, we introduce a new type of hydrophilic silicone-based resin material derived from vinyl-terminated amphiphilic siloxane oligomers, acrylamide, and glycidyl methacrylate, for the printing of fluidic devices. We demonstrate the strategy to overcome the challenges associated with amphiphilic-based formulation by adjusting the amphiphilic siloxane oligomer conformation with appropriate solvent blend, resulting in a silicone-based resin material with low pre-gel viscosity, high transparency, and hydrophilic characteristics. Besides, the developed material exhibits tunable elastic properties, excellent polar solvent resistance, and good biocompatibility. Upon photocuring depth tuning, the developed material displays high printing accuracy down to 200 µm in width and 50 µm in height. The material’s ability to replicate embedded fluidic channels with diverse shapes in one-step printing further shows its potential for fluidic device fabrication. The printed devices were revealed to be highly functional with the capability to process fluid at an elevated temperature of up to 100 ºC for 24 hours and a continuous flow rate of up to 20 mL/min. Further demonstration of the hydrogel beads synthesis for drug encapsulation reveals the feasibility of the printed device for real-world biomedical applications. The successful VP printing of hydrophilic silicone-based embedded-channel fluidic devices opened up new avenues for the fabrication of silicone-based fluidic devices for biomedical 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 | 0.001 |
| 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".