Diffusion- and Chemometric-Based Separation of Complex Electrochemical Signals That Originated from Multiple Redox-Active Molecules

On-Chip Electrochemical Sensing with an Enhanced Detecting Signal Due to Concentration Polarization-Based Analyte Preconcentration

Here, we integrated two key technologies within a microfluidic system, an electrokinetic preconcentration of analytes by ion Concentration Polarization (CP) and local electrochemical sensors to detect the analytes, which can synergistically act to significantly enhance the detection signal. This synergistic combination, offering both decoupled and coupled operation modes for continuous monitoring, was validated by the intensified fluorescent intensities of CP-preconcentrated analytes and the associated enhanced electrochemical response using differential pulse voltammetry and chronoamperometry. The system performance was evaluated by varying the location of the active electrochemical sensor, target analyte concentrations, and electrolyte concentration using fluorescein molecules as the model analyte and Homovanillic acid (HVA) as the target bioanalyte within both phosphate-buffered saline (PBS) and artificial sweat solution. The combination of on-chip electrochemical sensing with CP-based preconcentration renders this generic approach adaptable to various analytes. This advanced system shows remarkable promise for enhancing biosensing detection in practical applications while bridging the gap between fundamental research and practical implementation.

Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection

Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.