Bibliographic record
Abstract
Nature routinely uses a bottom – up approach for the organization of materials arounds us. This unique structural and hierarchical organization spans seven orders of magnitude in length scale: from molecular (~ nm) to organ dimensions (~ cm) is a hallmark of many organs. The variation in the structural organization imparts different functionalities and macroscopic properties such as elasticity, permeability, and optical transparency to tissues. Tissue – engineering routinely uses several promising strategies to engineer tissues that mimic the complex architecture and functionality of native tissues. However, some of their limitations include the inability to recapitulate the hierarchical assembly of materials, tunability of nanoscale properties, lack of scalability, and time – consuming fabrication methods. In this thesis, we have developed two microfluidics – enabled approaches for the scalable formation of fibrillar and polysaccharide – based hierarchical soft materials with precise control over the material composition, geometric and mechanical properties, cellular response, and structural assembly. In the first approach, we adopted a microfluidic strategy for the continuous formation of centimeter – wide, ultrathin collagen sheets. Application of shear via hydrodynamic flow – focusing through a geometric constriction and strain – induced pulling by a rotating mandrel, and the osmotic and evaporation – induced removal of water resulted in anisotropically aligned and compacted sheets with ultimate tensile strengths of 0.5 – 2.5 MPa and elastic moduli of 3 – 36 MPa. We used multi – lamellar collagen sheets to produce tissue – engineered vascular grafts with biomechanical properties similar to native vessels. The collagen sheets were also used as transwell – membranes for drug screening purposes. Human bronchial epithelial cells showed a high trans – epithelial resistance of 1900Ω x cm2. F508del – CFTR cells showed a three – fold increase in chloride conductance when treated with Cystic Fibrosis Trans –Regulator corrector, lumacaftor (VX – 809). The second microfluidic strategy allowed the continuous preparation, and processing of meter – long, roll – to – roll processed biomaterial sheets with control over thickness, morphology, and elastic properties. 3D structures with deterministic and stochastic voids were produced using layer – by – layer folding and stacking techniques. The approaches presented here find application in areas such as tissue engineering, regenerative medicine, drug – screening, and biohybrid devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".