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Cultured neuronal networks, nerve cells cultured in vitro inside microelectrodes arrays have been used in 'rat brained robots', the robots driven not with digital microcontrollers but with nerve cells. Microfluidics (lab on a chip) platforms have emerging applications: 'lung on a chip', 'heart on a chip', 'organ on a chip', etc. to replace clinical trials.
In brain and neuroscience research and in vivo body electronics implant industries, flexible, easy to microfabricate microelectrodes are also researched to find replacements for metal electrodes. This research will focus on the development of an integrated platform of those promising technologies into a modular platform, interfacing of nerve cells cultured in immobilised microstructures inside microfluidics with embedded systems (electronics and software) and investigation of dynamics of bi-directional communication between neuron and silicon. In vitro and microfluidic nerve networks can be stimulated, firing of neurons can be recorded, patterns can be analysed with a data acquisition and control embedded system. Such an integrated platform can be useful to research robotics applications, to replace clinical trials, to become a part of personal, preventive self diagnostic instruments (so that medical care costs can be reduced), to make better and cheaper in vivo bio-electronics implants and to make biosensors and bioMEMS.
Figure. The block diagram describing PhD research: integration, interfacing and investigating
Engineering contributions have played an important role in the rise and evolution of cellular biology. Engineering technologies have helped biologists to explore the living organisms at cellular and molecular levels, and have created new opportunities to tackle the unsolved biological problems. There is now a growing demand to further expand the role of engineering in cellular biology research. For an engineer to play an effective role in cellular biology, the first essential step is to understand the cells and their components. However, the stumbling block of this step is to comprehend the information given in the cellular biology literature because it best suits the readers with a biological background.
This research aims to overcome this bottleneck by describing the human cell components as micro-plants that form cells as micro-bio-factories. This concept can accelerate the engineers'comprehension of the subject.
In this work, first the structure and function of different cell components are described. In addition, the engineering attempts to mimic various cell components through numerical modelling or physical implementation are highlighted. Next, the interaction of different cell components that facilitate complicated chemical processes, such as energy generation and protein synthesis, are described. These complex interactions are translated into simple flow diagrams, generally used by engineers to represent multi-component processes.
The cell organelles are shown as the micro-plants of a micro-bio-factory
This research investigates the development and experimental analysis of a dielectrophoresis (DEP) system, which is used for the manipulation and separation of microparticles in liquid flow. The system is composed of arrays of microelectrodes integrated to a microchannel. Novel curved microelectrodes are symmetrically placed with respect to the centre of the microchannel with a minimum gap of 40 μm. Computational fluid dynamics method is utilised to characterise the DEP field and predict the dynamics of particles.
The performance of the system is assessed with microspheres of 1, 5 and 12 μm diameters. When a high-frequency potential is applied to microelectrodes a spatially varying electric field is induced in the microchannel, which creates the DEP force. Negative-DEP behaviour is observed with particles being repelled from the microelectrodes. The particles of different dimensions experience different DEP forces and thus settle to separate equilibrium zones across the microchannel. Experiments demonstrate the capability of the system as a field flow fraction tool for sorting microparticles according to their dimensions and dielectric properties.
Separation of 1 and 5 µm particles at 20 MHz
This research investigates the separation of polystyrene microparticles suspended in deionized (DI) water according to their dimensions using a dielectrophoretic (DEP) system. The DEP system utilises curved microelectrodes integrated into a microfluidic system. Microparticles of 1, 6, and 15 μm are applied to the system and their response to the DEP field is studied at different frequencies of 100, 200, and 20 MHz. The microelectrodes act as a DEP barrier for 15 Ám particles and retain them at all frequencies whereas the response of 1 and 6 μm particles depend strongly on the applied frequency. At 100 kHz, both particles are trapped by the microelectrodes. However, at 200 kHz, the 1 μm particles are trapped by the microelectrodes while the 6 μm particles are pushed toward the sidewalls. Finally, at 20 MHz, both particles are pushed toward the sidewalls. The experiments show the tunable performance of the system to sort the microparticles of various dimensions in microfluidic systems.
Separation of 1, 6, and 15 µm particles at 200 kHz
Current advancements in nanotechnology are dependent on the capabilities that can enable nano-scientists to extend their eyes and hands into the nano-world. For this purpose, a haptics (devices capable of recreating tactile or force sensations) based system for AFM (Atomic Force Microscope) is proposed. The system enables the nano-scientists to touch and feel the sample surfaces, viewed through AFM, in order to provide them with better understanding of the physical properties of the surface, such as roughness, stiffness and shape of molecular architecture.
At this stage, the proposed work uses offline images produced using AFM and perform image analysis to create virtual surfaces suitable for haptics force analysis. The research work is in the process of extension from offline to online process where interaction will be done directly on the material surface for realistic analysis.
Figure -Top-left: Raw AFM image of the scanned sample, top-right: Filtered version of raw Image (left), Bottom-left: Rendered surface of the filtered image (top-right), Bottom-right: Generated virtual Haptics surface