Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Multifunctional nanomaterials and nanocomposites for sensing and monitoring of environmentally hazardous heavy metal contaminants

The applications of conventional sensors are limited by the long response time, high cost, large detection limit, low sensitivity, complicated usage and low selectivity. These sensors are nowadays replaced by Nanocomposite-based modalities and nanomaterials which are known for their high selectivity and physical and chemical properties. These nanosensors effectively detect heavy metal contaminants in the environment as the discharge of heavy metals into natural water as a result of human activity has become a global epidemic. Exposure to these toxic metals might induce many health-related complications, including kidney failure, brain injury, immune disorders, muscle paleness, cardiac damage, nervous system impairment and limb paralysis. Therefore, designing and developing novel sensing systems for the detection and recognition of these harmful metals in various environmental matrices, particularly water, is of extremely important. Emerging nanotechnological approaches in the past two decades have played a key role in overcoming environmentally-related problems. Nanomaterial-based fabrication of chemical nanosensors has widely been applied as a powerful analytical tool for sensing heavy metals. Portability, high sensitivity, on-site detection capability, better device performance and selectivity are all advantages of these nanosensors. The detection and selectivity have been improved using molecular recognition probes for selective binding on different nanostructures. This study aims to evaluate the sensing properties of various nanomaterials such as metal-organic frameworks, fluorescent materials, metal-based nanoparticles, carbon-based nanomaterials and quantum dots and graphene-based nanomaterials and quantum dots for heavy metal ions recognition. All these nano-architectures are frequently served as effective fluorescence probes to directly (or by modification with some large or small biomolecules) sense heavy metal ions for improved selectivity. However, efforts are still needed for the simultaneous designing of multiple metal ion-based detection systems, exclusively in colorimetric or optical fluorescence nanosensors for heavy metal cations.

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu²⁺ contents on the SWASV signals of Pb²⁺ was investigated, and a nonlinear relationship between Pb²⁺ concentration and the peak currents of Pb²⁺ and Cu²⁺ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb²⁺ and Cu²⁺) and one output (i.e., Pb²⁺ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R²) and root-mean-square error (RMSE). Results showed that the SVR model with R² and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb²⁺ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.

Development of Heavy Metal Potentiostat for Batik Industry

The consumption of reactive dyes in the batik industry has led to a severe concern in monitoring the heavy metal level in wastewater. Due to the necessity of implementing a wastewater monitoring system in the batik factory, a Heavy Metal potentiostat (HMstat) was designed. The main goal of this study is to understand the optimal design concept of the potentiostat function in order to investigate the losses of accuracy in measurement using off-the-shelf devices. Through lab-scale design, the HMstat comprises of an analog potentiostat read-out circuit component (PRCC) and a digital control signal component (CSC). The PRCC is based on easy to use components integrated with a NI-myRIO controller in a CSC. Here, the myRIO was equipped with built-in analog to digital converter (ADC) and digital to analog converter (DAC) components. In this paper, the accuracy test and detection of cadmium(II) (Cd2+) and lead(II) (Pb2+) were conducted using the HMstat. The results were compared with the Rodeostat (an open source potentiostat available on the online market). The accuracy of the HMStat was higher than 95% and within the precision rate of the components used. The HMstat was able to detect Cd2+ and Pb2+ at −0.25 and −0.3 V, respectively. Similar potential peaks were obtained using Rodeostat (Cd2+ at −0.25 V and Pb2+ at −0.3 V).