In situ detection of heavy metal ions in sewage with screen-printed electrode-based portable electrochemical sensors

Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives

Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants' exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.

Water monitoring with an automated smart sensor supported with solar power for real-time and long range detection of ferrous iron

Low-power and smart sensing systems for iron detection are necessary for in situ monitoring of water quality. Here, a potentiometric Fe2+-selective electrode (ISE) was fabricated based on cyanomethyl N-methyl-N-phenyl dithiocarbamate for the first time as an ionophore. Under optimal conditions, the ISE showed a Nernstian slope of 29.76 ± 0.6 mV per decade for Fe2+ ions over a wide concentration range from 1.0 × 10-1 to 1.0 × 10-5 M with a lower detection limit (LOD) of 1.0 × 10-6 M. The ISE interference of various cations on the potentiometric response was also investigated. The ISE had a response time less than 3 s and the lifetime was two months. Also, an automated, long-range (LoRa), wireless enabled sampling microfluidic device powered with a solar panel as an autonomous power source was developed for a continuous sampling and sensing process. The sensing platform was employed in the determination of Fe2+ in acid mine drainage and spiked water samples with an average recovery of 100.7%. This simple, inexpensive (below $350), portable sensing platform will allow for rapid real-time monitoring of ground-, drinking-, and industrial waters contaminated with iron.

Ultra-sensitive electrochemical sensors through self-assembled MOF composites for the simultaneous detection of multiple heavy metal ions in food samples

Using an assemble-able MOF material, we successfully constructed an ultra-sensitive electrochemical sensor based on Bi2CuO4@Al-MOF@UiO-67 nanocomposite material, in order to investigate the adsorption properties of the Bi2CuO4@Al-MOF@UiO-67 functional material on the heavy metal ion. The Cd2+, Cu2+, Pb2+ and Hg2+ can be detected at the same time. Selective recognition and enrichment of various metal ions on different substrates can be achieved through the assembly of a large number of Al-MOF and UiO-67-MOF nanomaterial composites with small particle sizes on the Bi2CuO4 surface. Based on this, a new type of sensor is researched and prepared, which has been shown to have good stability and reproducibility. Due to its unique assembly structure, large active surface area, excellent adsorption capacity, and high electrical conductivity, Bi2CuO4@Al-MOF@UiO-67 presents outstanding performance. In addition, the sensor also exhibits excellent electrocatalytic redox capacity and high selectivity. The adsorption capacity of Cd2+, Cu2+, Pb2+ and Hg2+ is also significantly improved under the action of the sensor electrode, however, this is not the case. The limits of detection for Cd2+, Cu2+, Pb2+ and Hg2+ were found to be 0.02 pM, 0.032 pM, 0.018 pM and 0.041 pM, respectively. In order to investigate the detection mechanism of Cd2+, Cu2+, Pb2+ and Hg2+ was adsorption properties as well as electrochemical accumulation of Bi2CuO4@Al-MOF@UiO-67 on the metal atoms were investigated. This method has been successfully applied to samples of rice, sorghum, maize, milk, honey, and tea, and has enabled the simultaneous detection of Cd2+, Cu2+, Pb2+ and Hg2+, which is of significant practical value.

Electrochemical and Colorimetric Nanosensors for Detection of Heavy Metal Ions: A Review

Human exposure to acute and chronic levels of heavy metal ions are linked with various health issues, including reduced children's intelligence quotients, developmental challenges, cancers, hypertension, immune system compromises, cytotoxicity, oxidative cellular damage, and neurological disorders, among other health challenges. The potential environmental HMI contaminations, the biomagnification of heavy metal ions along food chains, and the associated risk factors of heavy metal ions on public health safety are a global concern of top priority. Hence, developing low-cost analytical protocols capable of rapid, selective, sensitive, and accurate detection of heavy metal ions in environmental samples and consumable products is of global public health interest. Conventional flame atomic absorption spectroscopy, graphite furnace atomic absorption spectroscopy, atomic emission spectroscopy, inductively coupled plasma-optical emission spectroscopy, inductively coupled plasma-mass spectroscopy, X-ray diffractometry, and X-ray fluorescence have been well-developed for HMIs and trace element analysis with excellent but varying degrees of sensitivity, selectivity, and accuracy. In addition to high instrumental running and maintenance costs and specialized personnel training, these instruments are not portable, limiting their practicality for on-demand, in situ, field study, or point-of-need HMI detection. Increases in the use of electrochemical and colorimetric techniques for heavy metal ion detections arise because of portable instrumentation, high sensitivity and selectivity, cost-effectiveness, small size requirements, rapidity, and visual detection of colorimetric nanosensors that facilitate on-demand, in situ, and field heavy metal ion detections. This review highlights the new approach to low-cost, rapid, selective, sensitive, and accurate detection of heavy metal ions in ecosystems (soil, water, air) and consumable products. Specifically, the review highlights low-cost, portable, and recent advances in smartphone-operated screen-printed electrodes (SPEs), plastic chip SPES, and carbon fiber paper-based nanosensors for environmental heavy metal ion detection. In addition, the review highlights recent advances in colorimetric nanosensors for heavy metal ion detection requirements. The review provides the advantages of electrochemical and optical nanosensors over the conventional methods of HMI analyses. The review further provides in-depth coverage of the detection of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) ions in the ecosystem, with emphasis on environmental and biological samples. In addition, the review discusses the advantages and challenges of the current electrochemical and colorimetric nanosensors protocol for heavy metal ion detection. It provides insight into the future directions in the use of the electrochemical and colorimetric nanosensors protocol for heavy metal ion detection.

A Time-Division Multiplexing Multi-Channel Micro-Electrochemical Workstation with Carbon-Based Material Electrodes for Online L-Trosine Detection

In the background of the rapid development of artificial intelligence, big data, IoT, 5G/6G, and other technologies, electrochemical sensors pose higher requirements for high-throughput detection. In this study, we developed a workstation with up to 10 channels, which supports both parallel signal stimulation and online electrochemical analysis functions. The platform was wired to a highly integrated Bluetooth chip used for wireless data transmission and can be visualized on a smartphone. We used this electrochemical test platform with carbon-graphene oxide/screen-printed carbon electrodes (CB-GO/SPCE) for the online analysis of L-tyrosine (Tyr), and the electrochemical performance and stability of the electrodes were examined by differential pulse voltammetry (DPV). The CB-GO-based screen-printed array electrodes with a multichannel electrochemical platform for Tyr detection showed a low detection limit (20 μM), good interference immunity, and 10-day stability in the range of 20-200 μM. This convenient electrochemical analytical device enables high-throughput detection and has good economic benefits that can contribute to the improvement of the accuracy of electrochemical analysis and the popularization of electrochemical detection methods in a wide range of fields.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Electrochemical biosensor based on antibody-modified Au nanoparticles for rapid and sensitive analysis of influenza A virus

To cope with the easy transmissibility of the avian influenza A virus subtype H1N1, a biosensor was developed for rapid and highly sensitive electrochemical immunoassay. Based on the principle of specific binding between antibody and virus molecules, the active molecule-antibody-adapter structure was formed on the surface of an Au NP substrate electrode; it included a highly specific surface area and good electrochemical activity for selective amplification detection of the H1N1 virus. The electrochemical test results showed that the BSA/H1N1 Ab/Glu/Cys/Au NPs/CP electrode was used for the electrochemical detection of the H1N1 virus with a sensitivity of 92.1 µA (pg/mL)^-1 cm², LOD of 0.25 pg/ml, linear ranges of 0.25-5 pg/mL, and linearity of (R ² = 0.9846). A convenient H1N1 antibody-based electrochemical electrode for the molecular detection of the H1N1 virus will be of great use in the field of epidemic prevention and raw poultry protection.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11581-023-04944-w.

A novel electrochemical sensing platform based on the esterase extracted from kidney bean for high-sensitivity determination of organophosphorus pesticides

An electrochemical sensing platform using kidney bean esterase as a new detection enzyme was proposed for organophosphorus pesticide determination. The determination of trichlorfon can be easily achieved with a LOD as low as 3 ng L−1 (S/N = 3).