生物传感器与生物电子学

This work deals with the development of an impedimetric immunosensor sensitive to horse immunoglobulin G. The biosensing platform involve the electrodeposition of polyaniline onto screen-printed carbon electrode as electrochemical transducer matrix. Cyclic Voltammetry and Scanning Electron Microscopy were used to monitor the formation of the recognition layer through the immobilization of anti-horse IgG antibodies on the conductive polymer. Electrochemical Impedance Spectroscopy (EIS) was performed to test the specific detection of horse serum in PBS buffer, indicating that the immunosensor was sensitive to the target analyte and without any response to heterologous sera (swine and bovine). The constructed sensors were first tested with PBS homogenates of commercial samples of raw ground horsemeat, pork and beef diluted at 10-5, 10-4 and 10-3 % w/v in PBS. The total time for performing the test was approximately 72 minutes and a selective response of the device is obtained with a 0.004% limit of detection.

Multilayered films of cellulose nanoparticles (NFC’s) and modified multi-walled carbon nanotubes (MWCNT’s) were assembled by means of alternate electrostatic adsorption with positively charged poly(ethyleneimine) (PEI) onto cellulose support. The free carboxylic groups of NFC’s and MWCNT’s were coupled with ethylenediamine. Glucose oxidase and laccase were immobilized by means of Schiff base reaction between aldehyde groups of glutaraldehyde and the free amino sites of the proteins.

The immobilized enzymes on the surface of nanoparticles have higher value for the specific activity compared to the enzymes immobilized directly on the cellulose surface indicating the stabilization of the proteins by the nanoparticles. The kinetics of the enzymes, catalyzed reactions and reusability of the enzymes were investigated and were showing better properties for enzymes cross-linked with glutaraldehyde. After 5 days the initial enzyme activity of glucose oxidase was around 85%, but the initial enzyme activity of laccase was 60%. The kinetic investigations of the immobilized enzymes showed no significant difference in Michaelis constant but the maximum reaction rate is decreased.

Artificial Intelligence (AI) research has a lot to learn from nature. My work links biology with computation and AI every day, but recently the rest of the world was reminded of the connection: The 2018 Nobel Prize in Chemistry went to Frances Arnold together with George Smith and Gregory Winter for developing major breakthroughs that are collectively called “directed evolution.” One of its uses is to improve protein functions, making them better catalysts in biofuel production. Another use is entirely outside chemistry – outside even the traditional life sciences. It’s about my main innovation that Evolution continues with Artificial Intelligence and at the field of Quantum Biology, through the Economics, until the field of Military Self-Defense.

The design of fluxgate magnetometers is typically a nonlinear multi-objective optimization problem. Different objectives often conflict with each other, and sometimes an optimal Fluxgate Magnetometer Sensor (FMS) performance is difficult to achieve. The sensitivity of the sensor decreases with an increase of noise level while trying to reduce the sensor dimension. Hence, there is need for a systematic optimization approach for FMS design to find its optimum performance. The combined modified multi-objective Firefly Optimization Algorithm (FOA) and systematic optimization approach is suggested to improve FMS’s design in this research by simultaneously optimizing the sensitivity and noise of a FMS while the sensor core, pick-up coil, and detection circuit are minimized. The developed model allowed improved sensitivity of 86.65%, reduction of noise level by 59.97% while still keeping the sensor size small by 14.29%.

The biosensor amperometric transducers can work in the case of three basic types of reversible inhibitor enzyme systems – with competitive inhibition, with non-competitive inhibition and mixed inhibition. Typically they work in static mode. Now they are investigated in dynamic mode. The kinetic in those type biosensors is generally discussed in terms of a simple extension to the Michaelis-Menten reaction scheme. The investigated biosensors are amperometric product sensitive. The parameters for simulations are chosen from some real experiments with biosensors. The models are described in non-stationary diffusion conditions. Solving system of non-linear partial differential equations is received in three dimensional size and the concentration profiles in active membrane of substrate S(x,t), inhibitor I(x,t) and product P(x,t) are received. The systems of non-linear differential partial equations have been solved numerically in MATLAB medium. The influence of starting concentration of substrate, inhibitor and kinetic parameters - reaction rate and reaction constants of biosensors over output current has been investigated.