Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell

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.

AI powered electrochemical multi-component detection of insulin and glucose in serum

Multi-component detection of insulin and glucose in serum is of great importance and urgently needed in clinical diagnosis and treatment due to its economy and practicability. However, insulin and glucose can hardly be determined by traditional electrochemical detection methods. Their mixed oxidation currents and rare involvement in the reaction process make it difficult to decouple them. In this study, AI algorithms are introduced to power the electrochemical method to conquer this problem. First, the current curves of insulin, glucose, and their mixed solution are obtained using cyclic voltammetry. Then, seven features of the cyclic voltammetry curve are extracted as characteristic values for detecting the concentrations of insulin and glucose. Finally, after training using machine learning algorithms, insulin and glucose concentrations are decoupled and regressed accurately. The entire detection process only takes three minutes. It can detect insulin at the pmol level and glucose at the mmol level, which meets the basic clinical requirements. The average relative error in predicting insulin concentrations is around 6.515%, and that in predicting glucose concentrations is around 4.36%. To verify the performance and effectiveness of the proposed method, it is used to determine the concentrations of insulin and glucose in fetal bovine serum and real clinical serum samples. The results are satisfactory, demonstrating that the method can meet basic clinical needs. This multi-component testing system delivers acceptable detect limit and accuracy and has the merits of low cost and high efficiency, holding great potential for use in clinical diagnosis.

Calibration curve-free electrochemical quantitation by micro-nano multi-scale gap devices

Quantitation without relying on the calibration curve has long been an issue of overcoming analytical problems accompanied with the inherent limitations of the calibration curve fitting errors. Here, we report on a calibration curve-free method for electrochemical quantitation based on a multi-scale gap device (MGD). The MGD is an integrated device having a series of interdigitated electrodes (IDE) with micro-to-nano gap distances. The device shows a gap-dependent redox current of the analyte when subjected to the electrochemical cycling between the two facing electrodes of its componential IDEs. Based on the fact that the current increases as the gap distance decreases, the analyte concentration could be directly estimated: the rate of increase in the current was directly proportional to the analyte concentration. The calibration curve was not necessary for the quantitation. The accuracy of this MGD approach was better than that of an IDE collection of the same gap distance, which was deteriorated at the larger gap distances particularly. The MGD-based quantitation of dopamine, potassium ferricyanide, and aminophenol was demonstrated in a relatively broad range of concentrations (100 nM-5 mM).

A Laser-Activated Membrane Introduction Mass Spectrometry Study of Proton Spillover Promoted Alkane Dehydrogenation

Operando high-throughput evaluation of heterogeneous catalysts by laser-activated membrane introduction mass spectrometry (LAMIMS) elucidates the Pt loading dependence of methylcyclohexane dehydrogenation on platinized γ-alumina beads. A CO₂ marking laser rapidly and sequentially heats catalyst beads positioned on a heat-dissipating carbon paper support that overlays a silicone membrane, separating the bead library reaction zone from a quadrupole mass analyzer. The toluene m/z peak varies logarithmically with Pt loading, suggesting that reactivity includes factors that are negatively correlated to Pt loading. These factors may include the Pt/γ-Al₂O₃ surface interfacial region as one component of a heterogeneous catalytically active surface area/mass. This work demonstrates LAMIMS as a broadly applicable high-throughput operando screening method for heterogeneous catalysts.