An Electrochemical Immunosensor Based on SPA and rGO-PEI-Ag-Nf for the Detection of Arsanilic Acid

A sensitive electrochemical immunosensor was prepared for rapid detection of ASA based on arsanilic acid (ASA) monoclonal antibody with high affinity. In the preparation of nanomaterials, polyethyleneimine (PEI) improved the stability of the solution and acted as a reducing agent to generate reduced graphene oxide (rGO) with relatively strong conductivity, thereby promoting the transfer of electrons. The dual conductivity of rGO and silver nanoparticles (AgNPs) improved the sensitivity of the sensor. The synthesis of nanomaterials were confirmed by UV-Vis spectroscopy, X-ray diffraction, transmission electron microscopy and scanning electron microscopy. In the optimal experiment conditions, the sensor could achieve the detection range of 0.50–500 ng mL−1 and the limit of detection (LOD) of 0.38 ng mL−1 (S/N = 3). Moreover, the sensor exhibited excellent specificity and acceptable stability, suggesting that the proposed sensor possessed a good potential in ASA detection. Thus, the as-prepared biosensor may be a potential way for detecting other antibiotics in meat and animal-derived foods.

APPLICATION OF THE “JUMPING FROGS” ALGORITHM FOR RESEARCH AND OPTIMIZATION OF THE TECHNOLOGICAL PROCESS

Context. An application of the method of a “jumping frogs” search algorithm to construct optimal experiment plans for cost (time) in the study of technological processes and systems that allow the implementation of an active experiment on them is proposed. The object of study are optimization methods for cost (time) costs of experimental designs, based on the application of a “jumping frogs” search algorithm. Objective. To obtain optimization results by optimizing the search of a “jumping frogs” search algorithm for the cost (time) costs of plans for a full factorial experiment. Method. A method is proposed for constructing a cost-effective (time) implementation of an experiment planning matrix using algorithms for searching for “jumping frogs”. At the beginning, the number of factors and the cost of transitions for each factor level are entered. Then, taking into account the entered data, the initial experiment planning matrix is formed. Then, taking into account the entered data, the initial matrix of experiment planning is formed. The “jumping frogs” method determines the “successful frog” by the lowest cost of transitions between levels for each of the factors. After that, the permutations of the “frogs” are performed. The “frog” strives for the most “successful” and, provided it stays close, remains in the location. Then the gain is calculated in comparison with the initial cost (time) of the experiment. Results. Software has been developed that implements the proposed method, which was used to conduct computational experiments to study the properties of these methods in the study of technological processes and systems that allow the implementation of an active experiment on them. The experimental designs that are optimal in terms of cost (time) are obtained, and the winnings in the optimization results are compared with the initial cost of the experiment. A comparative analysis of optimization methods for the cost (time) costs of plans for a full factorial experiment is carried out. Conclusions. The conducted experiments confirmed the operability of the proposed method and the software that implements it, and also allows us to recommend it for practical use in constructing optimal experiment planning matrices.