I am Simuleon’s newest member and in this blog I’d like to give a short introduction to myself.
I am originally from Romania, where I finished my Bachelor’s in Electronics and Telecommunications. Then I moved to cold, but very nice Finland, where I finished my master’s in Science and Bioengineering and, just recently, my PhD in Applied Physics. In total, I’ve lived in Finland for about 9 years. Me and my partner moved to Veldhoven in July last year and are currently exploring the Dutch nature and culture.
One thing we noticed is that both the Finns and the Dutch love ice-skating, though the former has a bit of a climate advantage when it comes to natural ice.
As you probably guess from the varied topics from my studies and more below, I do like working in different fields. I love working in different challenging areas and finding new approaches, particularly using FEA. In the end, the general approach is usually the same, just the tools may vary.
During my master’s studies, I worked on a project to develop an immunoassay-based in-vitro diagnostic device (Ready-to-use Microfluidic Cartridges for Affordable Point-of-Care Diagnostics). Ultimately, the device could rapidly diagnose tuberculosis and cardiac troponin I (indirect measure of myocardial damage in patients with chest pain) at the patient’s home. To reduce costs, this device would be mass produced using injection molding of high-density polystyrene and laser welding. I fully developed a prototype for one of the key components of this device, a micromixer. The micromixer would ensure that the blood solution and reagent are fully mixed before reaching the immunoassay testing chamber. This meant combining CAD design (Solidworks), FEA-CFD (Comsol), rapid prototyping (PDMS casting) and experimental testing to design, identify and validate the optimal micromixer configuration. In addition, I designed the final mold inserts for the micromixer as well as the entire device and performed some basic quality testing of the final molded parts.
Figure 1. (Top) Overview of diagnostic device final design (dimensions are in mm) and micromixer insert. (Bottom) Mixing behavior predicted by the CFD model and measured experimentally.
For the PhD work, I focused on developing a methodology to generate subject-specific FE (Abaqus) able to predict subject-specific knee joint osteoarthritis (OA) in patients with ligament injury and surgery. This method should also be suitable for studies with large number of subjects and ultimately in a clinical setting.
We identified patients susceptible to OA by identifying locations in articular cartilage that experience excessive tissue tensile stresses and compressive/shear strains. The mechanical parameters are linked with the constituents of articular cartilage. For instance, collagen fibers (a component of cartilage) determines the tensile properties of cartilage, and thus excessive tensile stresses may lead to collagen fiber damage.
To validate the approach, we compared our results against quantitative MRI follow-up information. For instance, the transverse relaxation time (T2) of cartilage has been linked to the collagen network. Then we can assume that locations with excessive tensile stresses also have increased T2 relaxation times.
Figure 2. (Top) Overview of geometry and motion inputs required for generating subject specific FE models. (Bottom) Location susceptible to OA due excessive tissue tensile stress and location with excessive T2 relaxation.
I look forward working with all the customers that I hope to get to know pretty soon.