Freeform Etching of Microchannels in Hydrogels by Ultrasonic Cavitation
Bibliographic record
Abstract
With structural similarities to biological tissues, hydrogels offer many potential applications in biomedicine. To improve hydrogel perfusion, simple microchannels can be fabricated using a variety of templating and printing approaches, but the formation of interconnected, winding, and branching channels remains a significant challenge. The cavitation‐mediated etching of microchannels in agarose hydrogels is demonstrated. An ultrasonic cavitation transducer coupled with a motorized control system is used to enable the formation of consistent microchannels within the agarose hydrogels with ellipsoid cross‐sectional areas and uniform widths on the order of 244 ± 19.5 μm. With increasing transducer voltage, the average microchannel width increases, while higher positional translation speed results in shorter dwell times and, therefore, smaller microchannels. Infusion of fluorescent dyes indicates little turbulence within the microchannels formed by the cavitation etching process. This technique can fabricate branched and complex microchannel paths. Furthermore, the mechanical and swelling properties of hydrogels with internal microchannels formed by cavitation at varying pH support future development in diverse applications including tissue engineering, drug delivery, and biomimetic lab‐on‐a‐chip systems.
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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".