Degree

Doctor of Philosophy (PhD)

Department

Mechanical and Industrial Engineering

Document Type

Dissertation

Abstract

The development of reliable hydrogel-based scaffolds for extrusion bioprinting remains limited by the poor structural fidelity of low-viscosity bioinks and the lack of robust, high-throughput quality evaluation methods. This dissertation can resolve such challenges by developing the combination of optimized hydrogel formulations, cryogenic-assisted bioprinting, and artificial intelligence (AI)-based scaffold evaluation to the use of tissue engineering and preclinical cancer modelling applications. To assess the rheological behavior, printability, mechanical properties, and biocompatibility of the alginate – gelatin (Alg–Gel) system, a novel system of hydrogel was developed and characterized using Alg–Gel hydrogel system. The 7% alginate, 8% gelatin mixture was found to be the best combination of shear-thinning properties, storage modulus, compressive strength, and compatibility with the cells. Printability experiments revealed that a 27-gauge tapered needle offered the greatest normalized printability index, the smallest strand width and the highest accuracy of printing and the lowest extrusion pressure by printing from 3D bioprinter of Allevi 3.0. High proliferation of adipose-derived stem cells (ASCs) and MDA-MB-231 breast cancer cells during a two-week culture period was observed on the optimized hydrogel, indicating that it can be used in bioprinted disease models and personalized drug-screening applications. Second, to address the challenge of printing low-viscosity hydrogels, a cryogenic bioprinting platform was created and thermally evaluated.

Liquid nitrogen cooled high conductivity copper was used to provide a stable surface temperature range of -45 °C to -15 °C, allowing deposited filaments to solidify quickly and preventing post extrusion spreading. This cryo-bioprinting method was used to analyze alginate agarose hydrogels of different compositions in a variety of scaffold geometries. The 3% alginate-4% agarose solution exhibited the best structural fidelity, especially of curved geometries, and is an effective solution to make low-viscosity hydrogel scaffolds by cryogenic surface cooling. Finally, a geometry conscious, AI-based deep learning model was created to evaluate scaffold quality automatically. The model combined a ResNet-18 backbone with evidential uncertainty estimation on 864 printed scaffolds across a range of geometries and processing conditions to classify the quality of the scaffold and measure prediction confidence. The framework demonstrated good classification performance and greatly outperformed traditional image similarity techniques as well as not susceptible to geometric variation, lighting and position changes. Together, these efforts form the overall approach to enhancing hydrogel bioprinting by refining biomaterials, controlling the cryogenic process, and smart quality control, which will establish a scale base in the future of tissue engineering, regenerative medicine, and personalized therapeutic use.

Date

8-27-2026

Committee Chair

Ram Devireddy

LSU Acknowledgement

1

LSU Accessibility Acknowledgment

1

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