Cerebral small vessel disease (CSVD) is a leading cause of stroke and dementia, making the study of small vessel hemodynamics vital for advancing diagnostic and therapeutic strategies. This research focuses on fabricating microfluidic devices that replicate the lateral lenticulostriate arteries (LSA) to validate computational flow models of small cerebral vessels. A key challenge in studying CSVD is the lack of experimental validation for computational fluid dynamics (CFD) models, which are widely used to simulate hemodynamics. To address this, additive masked Stereolithography (mSLA) was employed to fabricate a microfluidic model of the LSAs. The study explored the impact of orientation, exposure time, and layer height on the roundness and error of the intended area of printed micro-pores, to optimize the manufacturing of a microfluidic device. The smallest printed pore measured 270 μm in diameter. Pores printed at a larger angle as assessed from the build plate were more likely to remain open, but exhibited a larger decrease in area compared to smaller angles. A lower exposure time exhibited a larger pore area, whereas a larger layer height showed a decrease in area from intended. The layer height and angle did not influence the roundness, whereas an increase in exposure time decreased the roundness of the pores. Additionally, flow experiments were conducted using a 3D printed microfluidic device to compare empirical data with CFD and analytical simulations. The encountered resistance was larger for the experimental results (3.19 ⋅10^11 Pa⋅s/m^3) compared to the analytical result (1.96 ⋅10^11 Pa⋅s/m^3) and the computational result (1.90 ⋅10^11 Pa⋅s/m^3), likely due to deviation from the intended size. Finally, arterial microfluidic devices were printed and flow was induced to showcase their functionality. Achieving precise channel dimensions remains the primary challenge in mSLA printing due to cumulative dosage effects.
This research bridges the gap between computational modeling and experimental validation, providing a platform for studying cerebral microcirculation. The findings demonstrate the feasibility of using commercially available 3D-printed microfluidic devices to replicate small cerebral vessels. The outcomes of this study contribute to the advancement of vascular biomodeling, with implications for future clinical applications in stroke and neurovascular research.

Altered blood flow dynamics play a vital role in cerebrovascular diseases, such as cerebral small vessel disease (CSVD), an umbrella term encompassing various pathologies that affect small arteries, arterioles, capillaries, and venules. Understanding the complex relation between vascular geometry and local flow conditions requires high-resolution insight into microscale local hemodynamics. Nonetheless, in vivo imaging lacks the spatial resolution necessary to visualize flow in small vessels, and computational models require experimental validation to ensure modeling accuracy.

This thesis presents an integrated framework using 3D printed microfluidic models, particle image velocimetry (PIV), and computational fluid dynamics (CFD) to investigate hemodynamics on a microscale. Transparent 3D printed vascular models with simplified, straight, bifurcated, and pathological geometries were successfully fabricated using a direct 3D printing method employing a masked stereolithography (MSLA) 3D printer, achieving a minimum channel diameter of 0.5 mm. A developed post-processing method increased optical transparency, and a refractive index-matched working fluid was developed to minimize distortion. Microscopy and micro-CT imaging were used for morphological characterization, and flow experiments were conducted under steady-state laminar conditions. A microfluidic control system was employed to obtain global flow and pressure data, while micro-PIV was utilized to capture local velocity fields and wall shear stress (WSS). In the straight channels, the experimental results were compared with the analytical Hagen–Poiseuille approximation. The computational model was then validated using both analytical and experimental data for the straight geometries, followed by experimental validation of the computational model in the bifurcated and pathological geometries.

Velocity profiles between the computational and experimental results showed good agreement, exhibiting similar flow features with relative errors ranging from 5% to 17%. Experimental velocities were generally lower than those predicted by analytical and CFD methods, primarily due to limitations in near-wall resolution and visualization, as well as averaging over the depth-of-correlation (DOC) and the finite spatial resolution of PIV. These limitations were particularly pronounced in the sub-millimeter channel (0.5 mm) and at a lower magnification (4x objective). The wall shear stress comparison shows that the near-wall spatial resolution is the limiting factor rather than model physics. For the 4x objective or for the smaller-sized channels (0.5 mm and 1 mm channel), with a lower spatial resolution, the WSS was underestimated and showed significant deviations (15% to 40%) depending on the geometry. For the 2.0 mm channel and the middle section of the stenotic model at 10x magnification, the results matched well with an average error of 2.6%.

The thesis objective was achieved, and the integrated experimental-computational approach demonstrates that micro-PIV data from 3D printed vascular models can effectively be used to validate CFD simulations of microscale hemodynamics. The developed methodology provides a foundation for future work involving more complex patient-specific geometries, compliant walls, or non-Newtonian fluids, ultimately supporting the validation of numerical models for cerebrovascular flow under physiologically realistic conditions.

The brain is the most intricate organ in the human body, yet the underlying mechanisms of its cells and networks are not fully mapped. In addition to this lack of understanding, there are numerous neurological disorders and diseases for which a cure remains elusive. There has been persistent research to understand how neuronal cells function when interfaced to engineered biomaterials. The mechanical, topological, and chemical features of the extracellular matrix influence neuronal cell growth, and, among these, also electrical cues play a fundamental role in steering cell fate. The importance of electrical stimulation and 3D engineered microenvironments, better mimicking the spatial configuration followed by cells in the natural brain tissue, necessitates therefore the design of electrically conductive 3D microstructures. In light of the limited number of 3D electrically conductive scaffold studies, their reproducibility issues as well as fabrication constraints, the aim of this thesis is to at develop 3D electrically conductive free-standing microstructures made of polymeric materials. To achieve this goal, a protocol involving the chemical oxidative polymerization of EDOT (3,4-ethylene dioxythiophene) into PEDOT, an electrically conductive polymer, is developed. To ensure conductivity throughout polymeric 3D microstructures, EDOT is incorporated into an acrylate-based resin (IP-L) and 3D printed via twophoton polymerization (2PP), a 3D printing technology with sub-micrometre resolution. The electrical conductivity is experimentally measured, and it is reported how the tuning of printing parameters and organic solvents have a significant influence, with a maximum conductivity of 17.43 S/m after Dimethyl sulfoxide (DMSO) treatment. The mechanical properties of the 2PP-printed structures are evaluated as well, highlighting that the stiffness of microstructures decreases as EDOT doping increases. The versatility of the developed approach is demonstrated by fabricating 3D cage matrices featuring geometries suitable for neuronal cell culture. The reported results pave the way to further investigate the effect of 3D electrically conductive PEDOT-doped microstructures on neuronal cell growth and development.