Reversible bonding in thermoplastic elastomer microfluidic platforms for harvestable 3D microvessel networks
Bibliographic record
Abstract
, engineered injectable 3D microvessel networks are created using thermoplastic elastomer (TPE) microfluidic devices. The TPE material used here is flexible, optically transparent, and can be robustly yet reversibly bonded to a variety of plastic substrates, making it a versatile choice for microfluidic device fabrication because it overcomes the weak self-adhesion properties and limited manufacturing options of poly(dimethylsiloxane) (PDMS). By leveraging the reversible bonding characteristics of TPE material templates, we present their utility as an organ-on-a-chip platform for forming and handling microvessel networks, and demonstrate their potential for animal-free tissue generation and transplantation in clinical applications. We first show that TPE-based devices have nearly 6-fold higher bonding strength during the cell culture step compared to PDMS-based devices while simultaneously maintaining a full reversible bond to (PS) culture plates, which are widely used for biological cell studies. We also demonstrate the successful generation of perfusable and interconnected 3D microvessel networks using TPE-PS microfluidic devices on both single and multi-vessel loading platforms. Importantly, after removing the TPE slab, microvessel networks remain intact on the PS substrate without any structural damage and can be effectively harvested following gel digestion. The TPE-based organ-on-a-chip platform offers substantial advantages by facilitating the harvesting procedure and maintaining the integrity of microfluidic-engineered microvessels for transplant. To the best of our knowledge, our TPE-based reversible bonding approach marks the first confirmation of successful retrieval of organ-specific vessel segments from the reversibly-bonded TPE microfluidic platform. We anticipate that the method will find applications in organ-on-a-chip and microphysiological system research, particularly in tissue analysis and vessel engraftment, where flexible and reversible bonding can be utilized.
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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".