Electrochemical Detection of Heavy Metals Using Graphene-Based Sensors: Advances, Meta-Analysis, Toxicity, and Sustainable Development Challenges

Abstract

Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass-fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection.

Keywords: electrochemical sensor; food product; graphene derivatives; heavy metals; meta-analysis.

Figure 1

Number of publications between 2015–2024: (A) Development of electrochemical sensors based on graphene materials. (B) Development of sensors based on graphene derivatives for heavy metal detection. (C) ROSES flow diagram for the publications identified for meta-analysis.

Figure 2

Schematic protocol of the electrochemical Hg²⁺ aptasensor (Reproduced with permission from [29], Copyright 2023 MDPI).

Figure 3

Construction of sensing platform. Pb²⁺ acts on specific sites (rA) of DNAzyme (A). Specific assembly process of the bioelectrode (B). Structure of EBFC and capacitor integrated device (C) (Reproduced with permission from Ref. [54], Copyright 2024 Elsevier B.V.).

Figure 4

Schematic illustration of L-Au-MOFs-GO composite synthesized via the in situ growth and the detection of Cd²⁺ and Pb²⁺ by L-Au-MOFs-GO-modified electrode (Reproduced with permission from Ref. [87], Copyright 2023 Elsevier B.V.).

Figure 5

Schematic representation of the T-GO-C nanomaterial-fabricated electrochemical sensor electrode and its SWV sensing of Hg²⁺ and Cr⁶⁺ (Reproduced with permission from Ref. [90], Copyright 2025 Elsevier B.V.).

Figure 6

Schematic route for the preparation of Ag NPs/rGO nanocomposite (Reproduced with permission from Ref. [99], Copyright 2024 Elsevier B.V.).

Figure 7

The preparation process of the signal label (Probe II) (A). Schematic diagram of a sensor for simultaneous detection of Hg²⁺ and Pb²⁺ (B) (Reproduced with permission from Ref. [124], Copyright 2024 Elsevier B.V.).

Figure 8

LOD and LDR forest plot of all reviewed graphene-based sensors and biosensors for the electrochemical detection of heavy metals [21,22,23,24,25,27,28,30,31,32,34,36,37,38,39,40,42,43,44,45,46,47,49,50,51,52,54,55,56,57,58,59,60,61,62,63,64].

Figure 9

LOD and LDR forest plot of all reviewed graphene oxide-based sensors for the electrochemical detection of heavy metals [33,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,85,86,87,88,89,90,91,92,93,94,95,96,97].

Figure 10

LOD and LDR forest plot of all reviewed reduced graphene oxide-based sensors and biosensors for the electrochemical detection of heavy metals [99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,116,118,119,120,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137].

Figure 11

Comparative LOD and LDR performance of all reviewed sensors and biosensors grouped by heavy metal ions.

Figure 12

Sankey flow diagram for contributing percentages of categories for all reviewed sensors and biosensors for the electrochemical detection of heavy metals.