Microfluidic Production of Ultrathin, Handleable Collagen Sheets Exhibiting Toe‐heel Tensile Behavior
Bibliographic record
Abstract
The extracellular matrix (ECM) of cardiovascular tissues displays a non-linear, strain-dependent elastic modulus, attributed to the hierarchical organization of collagen. At low loads, these tissues exhibit compliance, permit contraction or dilation, while at high loads, they stiffen and increase their mechanical strength at least tenfold. Although collagen gels are widely used in 3D cell culture, tissue engineering, and biofabrication, current engineering techniques fail to replicate this hierarchical organization at the microscale. As a result, they lack both the non-linear tensile behavior and the physiologically relevant strength of native tissues. To address this limitation, we prepare ultrathin, templated collagen sheets (1.8 microns thin and 10 mm wide) from an acidic collagen solution using a microfluidic wet spinning process, incorporating and later removing microscale oil droplets at 2.25% volume concentration. Templated collagen sheets exhibit a two-fold increase in fibril alignment dispersion compared with non-templated ones. When assessed along their length, the Young's modulus of the templated sheets increases 62-fold at 90% failure strain, recapitulating the tensile behavior of native load-bearing tissues. We anticipate that these ultrathin templated collagen sheets will find broad applications as substrate materials for the bottom-up fabrication of load-bearing biomaterials and tissue structures for in vitro applications and implantation.
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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) | 0.000 | 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".