Ms. Camila Uzcategui Profile

Camila Uzcategui

Doctoral Candidate

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Publications (2)

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Proceedings Article | 4 March 2019 Presentation

3D printing hydrogels with orthogonally tunable mechanical properties (Conference Presentation)

Asais Camila Uzcategui, Archish Muralidharan, Yangyi Yu, Karin Payne, Virginia Ferguson, Stephanie Bryant, Robert McLeod

Proceedings Volume 10875, 108750T (2019) https://doi.org/10.1117/12.2510051

KEYWORDS: 3D modeling, 3D printing, Tissue engineering, Tissues, FT-IR spectroscopy, Stereolithography, Injuries, Cartilage, Environmental sensing, Photopolymerization

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A predominant challenge in tissue engineering is the need of a robust technique for producing structures with precise three-dimensional control of mechanical properties. In an effort to understand how to control the mechanical and chemical properties of materials patterned using stereolithography, this study focuses on the development of a model that allows for orthogonally programmable elastic modulus and geometry in a 3D hydrogel structure. In this study, we use pediatric physeal tissue engineering as a model application. Pediatric physeal injuries can be detrimental to children because damaged cartilage within the physis can lead to assymetric growth arrest. A challenge related to physeal tissue regeneration is the presence of three distinct zones within the physis or growth plate, where cells evolve differently and are known to be sensitive to the mechanical environment. We use a poly(ethylene glycol) diacrylate based photopolymerization chain-growth reaction with a modulus that can range from 600KPa to 39 MPa to spatially control the mechanical properties in our 3D parts and match the growth plate tissue environment. We apply Fourier transform infrared spectroscopy (FTIR) to quantify the degree of monomer conversion over time and intensity, while comparing it with a mechanical model that relates conversion to modulus. To evaluate the spatially controlled mechanical properties in printed structures, nanoindentation is performed to extract the modulus in each biomimetic zone. In this work we present the implementation of both a gradual change in mechanical properties, as well as a step function in 3D scaffolds on the order of 100 microns. This work enables predictive models of structural properties that can be translated into tissue engineering microstructures that match the native biomechanical environment of any tissue.

Proceedings Article | 14 March 2018 Presentation

Characterization and optimization of a stereolithography-based hydrogel for improved properties (Conference Presentation)

A. Camila Uzcategui, Archish Muralidharan, Virginia Ferguson, Stephanie Bryant, Robert McLeod

Proceedings Volume 10491, 104910B (2018) https://doi.org/10.1117/12.2290869

KEYWORDS: Stereolithography, Tissue engineering, FT-IR spectroscopy, Statistical modeling, Polymers, Additive manufacturing, 3D printing, Printing, Optics manufacturing, Tissue optics

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3D printing hydrogels via stereolithography is a promising system for the creation of regenerative medical implants. However, the mechanical and chemical properties of materials patterned using stereolithography-based printing techniques are largely unknown. In an effort to understand how optical propagation, polymerization, and transport dynamics result in material mechanical property distribution, this study focuses on the axial (depth) characterization of single-layer printed materials as a function of light intensity, exposure time and absorption depth. We use a poly(ethylene glycol) diacrylate based photopolymerization chain-growth reaction with an approximate bulk modulus of 39 MPa to explore this phenomenon. Fourier transform infrared spectroscopy (FTIR) is employed to quantify the degree of monomer conversion over time and intensity, while comparing it with a Beer-Lambert model that describes the effect of Tinuvin CarboProtect, the neutral absorber in our material on conversion as a function of depth. To evaluate the mechanical properties of the printed materials, compression testing is performed on each sample to extract the bulk modulus and to map conversion as a function of exposure time by fitting the force-displacement curves of each condition to the Hertz model for a quantitative comparison of material stiffness. This study aims to understand material and mechanical property distribution in a single layer, and use this understanding to create a predictive model of these properties, as well as decrease the scale of the distribution with the implementation of a novel post-cure technique. This work enables predictive models of microscopic polymer properties that can be translated into improved macroscopic structural properties to form a robust polymer network for tissue engineering microstructures.

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