Highly Selective Electrochemiluminescence Sensor Based on Molecularly Imprintedquantum Dots for the Sensitive Detection of Cyfluthrin A highly selective and sensitive molecularly imprinted electrochemiluminescence (MIECL) sensor was developed based on the multiwall carbon nanotube (MWCNT)enhanced molecularly imprinted quantum dots (MIPQDs) for the rapid determination of cyfluthrin (CYF). The MIPQDs fabricated by surface grafting technique exhibited excellent selective recognition to CYF, resulting in a specific decrease of ECL signal at the MWCNTMIPQD modified electrode. Under optimal conditions, the MIECL signal was proportional to the logarithm of the CYF concentration in the range of 0.2 xc2xb5gL to 1.0 xc3x97 103 xc2xb5gL with a determination coefficient of 0.9983. The detection limit of CYF was 0.05 xc2xb5gL, and good recoveries ranging from 86.0% to 98.6% were obtained in practical samples. The proposed MIECL sensor provides a novel, rapid, high sensitivity detection strategy for successfully analyzing CYF in fish and seawater samples.

DataDriven Modeling of SmartphoneBased Electrochemiluminescence Sensor Data Using Artificial Intelligence Understanding relationships among multimodal data extracted from a smartphonebased electrochemiluminescence (ECL) sensor is crucial for the development of lowcost pointofcare diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru(bpy)32 luminophore and its experimentally measured ECL and electrochemical data. A smartphonebased ECL sensor with Ru(bpy)32TPrA was developed using disposable screenprinted carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru(bpy)32 concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 xc2xb5M and 2.5 xc2xb5M. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru(bpy)32 using easily observable key features. The results demonstrate that datadriven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.