# A CNN-Based Method for Heavy-Metal Ion Detection
## 1 Introduction  
Heavy-metal ions (HMIs) are highly toxic and have a detrimental impact on ecosystems and human health [1]; hence, detecting the content of HMIs in the environment is an important research hotspot. However, heavy-metal ion detection is generally performed in the laboratory, which requires immovable instruments. In order to better detect heavy-metal ions in the environment, we are eager to explore a convenient and automatic heavy-metal ion detection method. For that goal, there is a need for not only a portable detection instrument but also an automated data-processing method. Heavy-metal ion detection is generally divided into two steps; the first is electrochemical sampling using sensors, and the second is the processing of the sampled data. Researchers have been fruitful in sampling HMI data. In the electrochemical sampling phase, the data are generally sampled by using a three-electrode potentiostat [2]. The concentration of a substance can be calculated from the relationship between the current signal received by the three-electrode potentiostat and the concentration of the substance being measured. [PERSON] et al. [3] used a potentiostat to detect heavy-metal ions Pb(II) in water. Currently, portable potentiostats are increasingly being used for the measurement of various substances [4]. [PERSON] et al. [5] used a portable potentiostat to detect ferricyanide. [PERSON] et al. [6] used a portable potentiostat to detect glucose in the blood, sodium in the urine, heavy-metal solutions, and malaria antigens. The portable potentiostat facilitates electrochemical sampling [7]. Most of the aforementioned studies have optimized the electrochemical sampling methods [8; 9] and have made the sampling instrument miniaturized. However, there has been little research into automated data processing. Generally, portable potentiostats require traditional time-consuming manual analysis after measuring the data. Therefore, we are eager to explore an automated electrochemical measurement method that can be ported to the portable potentiostat to simplify the detection. In the heavy-metal ion detection field, most studies use conventional data-processing methods. Conventional data-processing methods usually use square-wave voltammetry to perform polarimetric analysis of heavy-metal solutions [10]. The polarimetric analysis is a method that relies on the characteristics of the redox peaks of different ions to determine the species and concentration of the ions. The half-wave potentials of different ions usually vary, and the concentration of the same ion is linearly related to the peak height, which is the basis for the qualitative and quantitative analysis of the polar spectrum. By measuring the peak height of the standard solution at different concentrations, a standard curve can be plotted for the ion. Routinely, the type of ion can be predicted by comparing the half-wave potential of the measured solution to that of the standard solution. The concentration of the solution to be predicted can be obtained by comparing the standard curve fitted to the standard solution to the actual measured peak height [8]. Polar spectral analysis has achieved good results in heavy-metal detection; however, data-processing methods require manual comparisons to identify the data obtained from the measurements. Traditional manual identification needs to be improved because it is time-consuming and inefficient. Therefore, it is of great practical importance to study an efficient data-processing method for the detection of HMI. Deep learning is an automated approach to data processing, which has achieved high success in many areas [11]. Due to its excellent performance, the deep learning-based approach has also been applied to data processing in electrochemical analysis. [PERSON] et al. [12] used principal component analysis (PCA) to analyze the concentration of metal cations in quantitative aqueous mixtures.