Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms

Abstract

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.

Keywords

• heavy metals detection
• electrochemical modified electrode
• metal-based nanomaterials
• pretreatment methods
• prediction models and algorithms