Heavy metal pollution has become an increasingly serious environmental issue, making the detection of heavy metals essential for safeguarding public health and the environment. This review aims to highlight the commonly used methods for detecting heavy metals (such as atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), inductively coupled plasma–mass spectrometry (ICP-MS), square-wave anodic stripping voltammetry (SWASV), etc.), with a particular focus on electrochemical detection and electrode modification materials. Metal nanomaterials (such as titanium dioxide (TiO₂), copper oxide (CuO), ZIF-8, MXene, etc.) are emphasized as promising candidates for enhancing the performance of sensors due to their high surface area and excellent catalytic properties. However, challenges such as interference from non-target heavy metal ions and the formation of organometallic complexes with organic compounds can complicate the detection process. To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. This paper aims to review commonly used methods for detecting heavy metals, with a particular emphasis on electrochemical techniques. It will also highlight the challenges faced in these methods, such as interference and sensitivity limitations, and propose innovative solutions, including the use of metal nanomaterials for improved sensor performance and the integration of advanced algorithms and pretreatment techniques to address interference and enhance detection accuracy.

The aim of this review is to gather current researches into sensors based on noble metal and semiconductor nanomaterials in biomedical detection and elucidate the basic principle and applications of different sorts of semiconductor nanomaterials, i.e., metal oxide NPs, transition metal dichalcogenides (TMDs), metal-organic frameworks (MOFs) and magnetic metal oxide (MMO) NPs. Based on the classifications of nano-biosensors, they can be summarized as electrochemical nano-biosensors, optical nano-biosensors, calorimetric nano-biosensors, and piezoelectric nano-biosensors, wherein, electrochemical and optical nano-biosensors acting as most popular study objects are focused on to excavate the great improvements in excellent sensitivity, selectivity and stability based on fabrication techniques.

In this paper, a portable electrochemical system for the on-site detection of heavy metals (HMs) in farmland soil is developed, characterized, and evaluated. The system has a three-electrode configuration and includes a signal acquisition and processing device and a computer, which integrates a microcontroller-based potentiostat to implement square-wave anodic stripping voltammetry (SWASV) at a bismuth-film-modified glassy carbon electrode. In addition, three analysis methods, namely, the standard addition method (SAM), the bivariate regression model and double stripping voltammetry, were integrated into this system for the on-site detection of HMs to meet different analysis requirements. One unique characteristic of the system is its custom software, which enables calculations based on all three analysis methods. Moreover, stripping peak currents and background currents can be identified and acquired automatically by two of the developed algorithms. The system was assessed by applying it to detect Pb(II) and Cd(II) in an acetate buffer solution and acetic acid soil extracts. The interference of Cu(II) can be inhibited by adding 0.1 mM ferrocyanide to the real sample exacts by complexation. The results indicate that the system was sensitive, effective, and reliable and has broad application prospects in the field of on-site analysis. Additionally, the system can be used by non-specialist personnel.