In the microchip electrophoresis with capacitively coupled contactless conductivity detection, the stray capacitance of the detector causes high background noise, which seriously affects the sensitivity and stability of the detection system. To reduce the effect, a novel design of planar grounded capacitively coupled contactless conductivity detector (PG-C4D) based on printed circuit board (PCB) is proposed. The entire circuit plane except the sensing electrodes is covered by the ground electrode, greatly reducing the stray capacitance. The efficacy of the design has been verified by the electrical field simulation and the electrophoresis detection experiments of inorganic ions. The baseline intensity of the PG-C4D was less than 1/6 of that of the traditional C4D. The PG-C4D with the new design also demonstrated a good repeatability of migration time, peak area, and peak height (n = 5, relative standard deviation, RSD ≤ 0.3%, 3%, and 4%, respectively), and good linear coefficients within the range of 0.05–0.75 mM (R2 ≥ 0.986). The detection sensitivity of K+, Na+, and Li+ reached 0.05, 0.1, and 0.1 mM respectively. Those results prove that the new design is an effective and economical approach which can improve sensitivity and repeatability of a PCB based PG-C4D, which indicate a great application potential in agricultural and environmental monitoring.

The problem of overlapping peaks has been a challenge in microchip electrophoresis (ME) signal analysis. However, traditional peak fitting algorithms have difficulty analyzing overlapping peaks due to the high dependence on the starting point. In this study, we propose a symmetrical peak fitting method named the tent-mapped whale optimization algorithm and Levenberg–Marquardt (TWOALM), which combines a whale optimization algorithm (WOA) improved by one of the most commonly used chaotic maps, the tent map and the Levenberg–Marquardt (LM) algorithm. Specifically, we first derive the fitted model for the overlapping peaks, showing that it is separable nonlinear least squares, significantly reducing the number of parameters to be optimized. Second, we integrate the tent map into the WOA, which improves the convergence speed of the peak fitting algorithm. Finally, we propose an efficient peak-fitting algorithm that combines the improved WOA and LM. The advantage of the proposed algorithm is that it is significantly faster than WOA and significantly more accurate than the LM algorithm. The results of fitting the synthetic peaks and ME signals showed that the combination of the chaotic map-based WOA algorithm and the LM algorithm can significantly improve the peak fitting performance and provide an effective solution for the analysis of overlapping peaks.

No abstract

Microchip electrophoresis (ME) is an ion detection system with low cost and portability, which is suitable for online analysis of environmental samples. However, the unresolved peaks in the detection signal of complex samples seriously affect the measurement accuracy of sample concentration. In this article, an efficient unresolved peaks analysis algorithm is proposed, which is based on the sigmoidal membership function, Lévy flight, and slime mould algorithm (SLSMA). First, the hyperbolic tangent function in the original slime mould algorithm (SMA) is replaced by the sigmoidal membership function to enhance the global optimization capability. Second, we use the Lévy flight sequences to further enhance the convergence speed of the SMA algorithm. Then, the performance of SLSMA is tested using synthetic peaks with different resolutions and noise levels. Finally, ME peaks are used to further validate the application of the proposed algorithm. The results show that the proposed algorithm has higher computational efficiency and can be used for the analysis of ME peaks.

No abstract