New Method Of Electroanalysis Of Agrochemicals Using Carbon Based Nanosensor

The use of agrochemicals can promote the safety of edible agricultural products,including pesticides,veterinary drugs,growth regulators,etc.Among them,plant hormone pesticides and quinolone antibiotics belong to agrochemicals.Phytohormonal pesticides and quinolone antibiotics are closely related to human life and health.The improper use of agrochemicals can affect human physiological metabolism and cause diseases.Therefore,it is particularly necessary to develop a rapid,sensitive and efficient method for detecting agrochemicals.In the construction of electrochemical sensors,carbon nanomaterials have the advantages of large specific surface area and good catalytic performance,so they are widely used in the field of sensors.Studies have shown that most carbon nanomaterials have nanoenzyme characteristics,which establishes the foundation for the design of biomimetic sensors.Based on the design of carbon nanosensors,this thesis introduces new methods for rapid inspection of agrochemicals.The specific content is as follows:（1）Under optimal conditions,the nanozyme sensor is composed of carboxylated multi-walled carbon nanotubes（COOH-MWCNT）and molybdenum disulfide（MoS₂）nanosheets,which can sensitively detect 5-sodium nitroguaiacol（5-NG）in tomato and chicken feed.In the presence of Nafion（Nf）,the MoS₂-COOH-MWCNT nanocomposite is simply obtained by ultrasonic process.The MoS₂-COOH-MWCNT film was characterized,and the parameters of the film electrode were optimized such as p H,scan rates,percentage of Nf,and the ratio of COOH-MWCNT to MoS₂.The electrocatalytic oxidation mechanism of 5-NG and the kinetics of the MoS₂-COOH-MWCNT nanocomposite nanozyme were studied.The fabricated nanozyme sensor showed good electrochemical response,wide linear range（0.1～70μM）and low detection limit（LOD）.At the same time,the sensor showed good repeatability,reproducibility and good anti-interference ability;（2）Machine learning（ML）plays an important role in predicting the performance of electrodes.In this work,a new strategy for applying the ML model artificial neural network（ANN）algorithm to predict the electrochemical performance of the carbonized metal-organic framework C-ZIF-67 as an electrode material is proposed.Morphological and elemental analysis were carried out through SEM,TEM,etc.and the existence of elements C,N,O,and Co was proved.In this research,models of electrochemical sensors and supercapacitors are established through ANN to realize intelligent data analysis.The intelligent model of the sensor and supercapacitor show excellent performance prediction.Compared with the bare glassy carbon electrode（GCE）,the prepared electrode exhibited excellent electrochemical response to niclosamide（NA）,wide linear range（0.001～9μM）and low detection limit;（3）The flexible electrode LIPG was prepared by laser direct writing,and the Cu-MOF-NH₂-MWCNT composite was obtained by ultrasonic process.The composite was drip-coated on the flexible electrode to obtain the Cu-MOF-NH₂-MWCNT/LIPG electrode,which can be successfully used for the electrochemical detection of ciprofloxacin.The composite material Cu-MOF-NH₂-MWCNT was characterized and the experimental parameters were optimized.The electrode showed good repeatability,reproducibility,and nanozyme characteristics.In addition,it also introduces the first-order derivative voltammetry analysis method,and the relationship obtained by this method has better analytical capabilities.