Towards Determining Kinetics of Annihilation Electrogenerated Chemiluminescence by Concentration-Dependent Luminescent Intensity

Determination of organophosphate flame retardant tris(2-chloroethyl)phosphine based on the luminol-H2 O2 chemiluminescence system

Organophosphorus flame retardants (OPFRs) are new types of environmental pollutants, therefore the rapid and sensitive detection of OPFRs is a very important objective. A new experimental phenomenon was found in which tris(2-chloroethyl)phosphine (TCEP), a type of OPFR, could effectively enhance the signal of the luminol-H₂ O₂ chemiluminescence (CL) system. Combined with the controllability of flow injection analysis, a rapid, stable, and sensitive CL method was established. The CL intensity responded linearly to the concentration of TCEP in the range 0.5-100 μg/L (R²  = 0.999) with a low detection limit of 33 ng/L. Relative standard deviation (RSD) was 2.2% (n = 7, c = 100 μg/L). Water samples were labelled and recycled with RSDs of 1.1-5.7% and recoveries of 88.7-116.1%. Based on these results, this study established a new CL detection method for the environmental pollutant TCEP.

Electrochemiluminescence reaction pathways in nanofluidic devices

Nanofluidic electrochemical devices confine the volume of chemical reactions to femtoliters. When employed for light generation by electrochemiluminescence (ECL), nanofluidic confinement yields enhanced intensity and robust luminescence. Here, we investigate different ECL pathways, namely coreactant and annihilation ECL in a single nanochannel and compare light emission profiles. By high-resolution imaging of electrode areas, we show that different reaction schemes produce very different emission profiles in the unique confined geometry of a nanochannel. The confrontation of experimental results with finite element simulation gives further insight into the exact reaction ECL pathways. We find that emission strongly depends on depletion, geometric exclusion, and recycling of reactants in the nanofluidic device.

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care 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 smartphone-based ECL sensor with Ru(bpy)32+/TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V 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 µM and 2.5 µM. 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 data-driven 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.