Highly sensitive label-free in vitro detection of aflatoxin B1 in an aptamer assay using optical planar waveguide operating as a polarization interferometer.

This work reports on further development of an optical biosensor for the in vitro detection of mycotoxins (in particular, aflatoxin B1) using a highly sensitive planar waveguide transducer in combination with a highly specific aptamer bioreceptor. This sensor is built on a SiO2-Si3N4-SiO2 optical planar waveguide (OPW) operating as a polarization interferometer (PI), which detects a phase shift between p- and s-components of polarized light propagating through the waveguide caused by the molecular adsorption. The refractive index sensitivity (RIS) of the recently upgraded PI experimental setup has been improved and reached values of around 9600 rad per refractive index unity (RIU), the highest RIS values reported, which enables the detection of low molecular weight analytes such as mycotoxins in very low concentrations. The biosensing tests yielded remarkable results for the detection of aflatoxin B1 in a wide range of concentrations from 1 pg/mL to 1 µg/mL in direct assay with specific DNA-based aptamers. Graphical abstract Optical planar waveguide polarization interferometry biosensor for detection of aflatoxin B1 using specific aptamer.

Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach.

This work reports on further development of an inhibition electrochemical sensor array based on immobilized bacteria for the preliminary detection of a wide range of organic and inorganic pollutants, such as heavy metal salts (HgCl2, PbCl2, CdCl2), pesticides (atrazine, simazine, DDVP), and petrochemicals (hexane, octane, pentane, toluene, pyrene, and ethanol) in water. A series of DC and AC electrochemical measurements, e.g., cyclic voltammograms and impedance spectroscopy, were carried out on screen-printed gold electrodes with three types of bacteria, namely Escherichia coli, Shewanella oneidensis, and Methylococcus capsulatus, immobilized via poly L-lysine. The results obtained showed a possibility of pattern recognition of the above pollutants by their inhibition effect on the three bacteria used. The analysis of a large amount of experimental data was carried out using an artificial neural network (ANN) programme for more accurate identification of pollutants as well as the estimation of their concentration. The results are encouraging for the development of a simple and cost-effective biosensing technology for preliminary in-field analysis (screening) of water samples for the presence of environmental pollutants. Graphical abstract.