The use of mechanical foaming for development of bioinks and bioprinting techniques
Bibliographic record
Abstract
Three-dimensional bioprinting is a unique subset of additive manufacturing in which the precise deposition of bioink gives yield to complex 3D structures made up of living cells embedded within hydrogels. For biological applications, 3D bioprinted structures are required to have a high degree of porosity to promote cellular growth and function. However, the low intrinsic porosity of bioinks often falls short in providing an ideal environment for cells. Moreover, many biocompatible hydrogels lack sufficient mechanical and rheological properties for 3D bioprinting. When these properties do not meet the requirements for printability, bioprinting fails due to poor shape fidelity and other complications. Therefore, further attention is required to develop methods to address the aforementioned limitations. \n \nThis thesis addresses the development of foam-based methods for the 3D bioprinting of supermacroporous structures, as well as the 3D bioprinting of low-viscosity/slow-crosslinking hydrogels. Albumin, a well-known foaming agent, is used to produce highly porous and 3Dprintable hybrid bioinks. Moreover, albumin foam is utilized as a sacrificial material to fabricate hollow fibers, and also is used as a foam-based support material in embedded bioprinting as an alternative to gel-based support baths. \n \nFor the investigation of a porous 3D-printable bioink, hybrid albumin-alginate solutions with various concentrations of albumin and sodium alginate are prepared and mechanically mixed. The resulting foam is 3D printed and crosslinked with a calcium chloride solution or mist. The fabricated scaffolds are characterized through the analysis of the printability, mechanical properties, porosity, water absorption, degradation, and drug release tests. These studies indicate that the mist-crosslinked scaffolds show superior mechanical properties and provide relatively longer drug release profiles. \n \nFor the hollow fiber bioprinting study, a foam-based sacrificial material is developed to be used as a core flow. This method involves the incorporation of the crosslinker (calcium chloride) into an albumin foam. The effects of foam and alginate flow rates on fiber diameter and wall thickness are investigated. Printing resolution is quantified by determining the printability number. Mechanical properties are assessed by analyzing breaking strain and filament collapse. Moreover, the viability of Neuro-2a cells co-incubated with the printed structures are examined, showing no detrimental impact on cell viability. \n \nFor the embedded bioprinting study, albumin foam enriched with cell culture media are used as a support bath to bioprint complex structures with low-viscosity/slow-crosslinking hydrogels. The optimal conditions of the foam-based bath are determined by testing different albumin concentrations and foaming times. A unique benefit of utilizing foam-based support baths in embedded bioprinting is the coalescence of bubbles over time, which leads to the formation of a sacrificial support bath. Additional benefits of utilizing foam as the supporting material include the enhanced access to surrounding gaseous oxygen, as well as immediate access to nutrients in the foam. A chitosan-based thermosensitive hydrogel of various complex structures are successfully bioprinted using the foam-based support material. Furthermore, the biocompatibility of the process is demonstrated by determining the cell viability over 7 days of L929 fibroblasts incorporated into a chitosan-collagen bioink. \n \nThe developed foam-based methods in this thesis leverage the advantages of the bioprinting techniques, while allowing direct 3D printing of highly porous structures, as well as lowviscosity/slow-crosslinking hydrogels. The use of innovative foam-based support materials is proved to be an excellent alternative to conventional support materials in coaxial bioprinting and embedded bioprinting systems for tissue engineering 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".