Zahra Motamedi

· Developed a microfluidic titanium dioxide (TiO2)- based methodology of exosome isolation reducing sample volume, contamination with an on-chip methodology.

· Established controlled and measurable experimentation by employing liposomes as a model system proven to be a cheap and reliable alternative approach for early stages of exosome study.

· Conceptualized the pattern of micromixers, designed in SolidWorks, conducted photolithography, performed statistical analysis based on experimental data, achieving optimized design.

· Evaluated the performance of the novel microfluidic methodology utilizing dynamic light scattering (DLS), Zeta Potential Measurements and fluorescence imaging showing a capturing efficiency of 94.49% and a recovery rate of 84.53%.

· Conducted a study on simulating micro river formations in hydrogels to enhance the understanding of organic flow structures in non-Newtonian fluids for applications in cell culture and microfluidic devices.

· Executed experiments to measure hydrogel viscosity under varying temperatures and shear rates, overcoming the limitations of existing fluid dynamics models and providing essential data for accurate non-Newtonian fluid simulations.

· Developed and fine-tuned a Multi-Layer Perceptron (MLP) neural network using experimental data, achieving a 95% accuracy rate in hydrogel viscosity prediction, significantly advancing the modeling of non-Newtonian fluid behaviors.

· Successfully integrated the MLP model into ANSYS Fluent through a custom User-Defined Function, markedly improving the simulation accuracy of microriver formations.

· Designed HVAC and Plumbing system of a 4-storey residential building using AutoCAD and Revit.

· Performed cooling and heating load calculations using Carrier HAP and Excel.

· Involved in size calculation of HVAC, plumbing, and energy systems according to ASHRAE 62.1/ 90.1, and the related plumbing codes.

· Provided cost estimation and selecting HVAC equipment, such as boilers, chillers, air handling units, and fan coils.

· Led the project of developing a device for measuring the viscosity and density of fluids using vibrations, achieving an 86% confidence level in fluid property predictions through the integration of 3D printing, Arduino programming, and machine learning.

· Utilized CAD software for the detailed design of apparatus prototypes, followed by 3D printing and extensive experimental testing and iterative engineering, leading to a refined and optimized apparatus.

· Developed and programmed an Arduino-based control system, achieving fine-tuned manipulation of vibrations for accurate fluid analysis.

· Executed advanced signal processing in MATLAB on sensor-derived vibration data, involving noise filtering, signal amplification, normalization, and feature extraction, crucial for accurately deducing fluid properties

· Implemented and fine-tuned a neural network using data from water-ethanol mixtures, leading to the system's ability to identify unknown fluid properties with an 86% confidence level.