![]() ![]() This paper reviews the research progress of CuNCs detection in HMIs, which can be divided into four parts. This has promoted the development of CuNCs in fluorescence sensing. Furthermore, the application of heavy metal ions (HMIs) detection is also regarded as a major part of fluorescence sensing and the necessity of detecting the makeup of HMIs (Ag+, Te3+, Co2+, Se6+, Hg2+, Mn2+, etc.) in organisms and the environment. Until now, CuNCs have been developed and applied in multi-fields such as sensing, catalysis, light-emitting diode manufacturing, and cell imaging. Recently, copper nanoclusters (CuNCs) have attracted great research interest for their low synthesis cost, wide application, and easy functionalization. The results of the assay on 12 sets of solutions with different ionic species and concentrations showed that the proposed HMID-NET algorithm ultimately obtained a classification accuracy of 99.99% and a mean relative error of 8.85% in terms of the concentration. HMID-NET consists of a backbone and two branch networks that simultaneously detect the type and concentration of the ions in the solution. Finally, we designed a CNN-based detection network, called HMID-NET. Next, a dataset of 1200 samples was created after data preprocessing and data expansion. For this purpose, a portable electrochemical constant potential instrument was designed for data acquisition. First, we used square-wave voltammetry to collect data from heavy-metal ion solutions. This paper proposes a CNN-based heavy-metal ion detection system, which can automatically, accurately, and efficiently detect the type and concentration of heavy-metal ions. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implications. ![]() Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. Also, electrochemical biosensors employed in the detection of metal ions with various interfaces have been highlighted.ĭata processing is an essential component of heavy-metal ion detection. In this review, toxicity mechanisms of various metal ions and their relationship towards the induction of oxidative stress have been summarized. The coupling of electrochemical techniques with nanomaterials has enhanced the sensitivity, limit of detection, and robustness of the sensors. Among these, nanomaterials are considered to be most promising, owing to their strong adsorption, fast electron transfer kinetics, and biocompatibility, which are very apt for biosensing applications. To detect these metal ions, electrochemical biosensors with interfaces such as microorganisms, enzymes, microspheres, nano-materials like gold, silver nanoparticles, CNTs, and metal oxides have been developed. Among various metal ions arsenic, cadmium, lead, mercury and chromium are considered to be highly toxic. Hence, fast and accurate detection of metal ions has become a critical issue. ![]() Most of the metal ions are carcinogens and lead to serious health concerns by producing free radicals. ![]()
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