Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors’ overall performance, especially concerning real-sample performance and the capability for actual field application.

Excessive nitrite amounts harm the environment and put public health at high risk. Therefore, accurate and sensitive detection of nitrite in surface and groundwater is mandatory for mitigating its adverse effects. Herein, a highly sensitive electrochemical sensor based on carbon screen-printed electrodes (CSPE) surface-modified with photochemically-made gold nanoparticles (AuNPs, ~12 nm) is proposed for nitrite detection. Scanning electron microscopy, cyclic voltammetry, and electrochemical impedance spectroscopy show that AuNPs uniformly coat the CSPE, increase its surface area, and contribute to oxidizing nitrite to much lower potential (+0.5 V vs. Ag/AgCl) and faster rate. Under optimized differential pulse voltammetry conditions, the CSPE/AuNPs-PEI electrode responds linearly (R² > 0.99) to nitrite within a wide concentration range (0.01–4.0 µM), showing a sensitivity of 0.85 µA·µM⁻¹·cm⁻² and limit of detection as low as 2.5 nM. The CSPE/AuNPs-PEI electrode successfully detects nitrite in tap water and canned water of olives, showing no influence of those matrices. In addition, the electrode’s response is highly reproducible since a relative standard deviation lower than 10% is observed when the same electrode is operated in five consecutive measurements or when electrodes of different fabrication batches are evaluated.

Sweat analysis has significant potential in personalized health monitoring and exercise physiology assessment due to its non-invasive and real-time nature. However, the complexity of sweat composition limits the applicability of existing potentiometric or voltammetric single sensors and e-tongue systems in complex analysis, as they rely on the selective detection of a single target analyte using specific electrodes, followed by pattern recognition based on that information. To address this limitation, this study proposes a flexible impedimetric e-tongue system based on a sensor array and machine learning-based pattern recognition for the holistic assessment of artificial sweat, enabling the high-accuracy classification of six exercise-related physiological states. The system is constructed using a laser-induced graphene (LIG) interdigitated electrode array, integrated with multi-walled carbon nanotubes (MWCNTs) and metal oxide nanoparticles (MONPs) composite sensing films, ensuring high sensitivity and stability. Experimental results demonstrate that different sensing elements exhibit complementary responses to major sweat analytes (Na⁺, K⁺, lactic acid, and glucose), forming distinctive sweat fingerprint signals. Key parameters were extracted from impedance spectroscopy (IS) and further analyzed using principal component analysis (PCA) for features transformation and support vector machine (SVM) for classification, achieving an overall 97.69% classification accuracy in identifying exercise-related physiological states. Furthermore, the sensor array exhibited high sensitivity, a broad linear range, excellent repeatability (RSD ≤ 1.67%), reproducibility (RSD ≤ 2.9%), and stability over ~ 20 days, confirming its feasibility for sweat analysis. Compared to potentiometric and voltammetric single sensors and e-tongue systems, the proposed impedimetric e-tongue system does not rely on the selectivity of individual sensors but instead analyzes the overall impedance pattern, significantly enhancing its capability for complex biofluid analysis. In conclusion, this study presents a novel impedimetric e-tongue system as a potential solution for exercise monitoring, health assessment, and personalized medicine, providing new insights into the development of non-invasive sweat analysis technologies.

A new reagent-less method for the detection of phosphate. Carbon screen printed electrodes were modified with copper phthalocyanines offered excellent electrocatalytic properties, with carbon nanotubes to enhance the electrocatalytic performance.