Development of an Innovative Biosensor Based on Graphene/PEDOT/Tyrosinase for the Detection of Phenolic Compounds in River Waters.

Phenolic compounds, originating from industrial, agricultural, and urban sources, can leach into flowing waters, adversely affecting aquatic life, biodiversity, and compromising the quality of drinking water, posing potential health hazards to humans. Thus, monitoring and mitigating the presence of phenolic compounds in flowing waters are essential for preserving ecosystem integrity and safeguarding public health. This study explores the development and performance of an innovative sensor based on screen-printed electrode (SPE) modified with graphene (GPH), poly(3,4-ethylenedioxythiophene) (PEDOT), and tyrosinase (Ty), designed for water analysis, focusing on the manufacturing process and the obtained electroanalytical results. The proposed biosensor (SPE/GPH/PEDOT/Ty) was designed to achieve a high level of precision and sensitivity, as well as to allow efficient analytical recoveries. Special attention was given to the manufacturing process and optimization of the modifying elements' composition. This study highlights the potential of the biosensor as an efficient and reliable solution for water analysis. Modification with graphene, the synthesis and electropolymerization deposition of the PEDOT polymer, and tyrosinase immobilization contributed to obtaining a high-performance and robust biosensor, presenting promising perspectives in monitoring the quality of the aquatic environment. Regarding the electroanalytical experimental results, the detection limits (LODs) obtained with this biosensor are extremely low for all phenolic compounds (8.63 × 10-10 M for catechol, 7.72 × 10-10 M for 3-methoxycatechol, and 9.56 × 10-10 M for 4-methylcatechol), emphasizing its ability to accurately measure even subtle variations in the trace compound parameters. The enhanced sensitivity of the biosensor facilitates detection and quantification in river water samples. Analytical recovery is also an essential aspect, and the biosensor presents consistent and reproducible results. This feature significantly improves the reliability and usefulness of the biosensor in practical applications, making it suitable for monitoring industrial or river water.

Electrochemical Detection of Cd2+, Pb2+, Cu2+ and Hg2+ with Sensors Based on Carbonaceous Nanomaterials and Fe3O4 Nanoparticles.

Two electrochemical sensors were developed in this study, with their preparations using two nanomaterials with remarkable properties, namely, carbon nanofibers (CNF) modified with Fe3O4 nanoparticles and multilayer carbon nanotubes (MWCNT) modified with Fe3O4 nanoparticles. The modified screen-printed electrodes (SPE) were thus named SPE/Fe3O4-CNF and SPE/Fe3O4-MWCNT and were used for the simultaneous detection of heavy metals (Cd2+, Pb2+, Cu2+ and Hg2+). The sensors have been spectrometrically and electrochemically characterized. The limits of detection of the SPE/Fe3O4-CNF sensor were 0.0615 µM, 0.0154 µM, 0.0320 µM and 0.0148 µM for Cd2+, Pb2+, Cu2+ and Hg2+, respectively, and 0.2719 µM, 0.3187 µM, 1.0436 µM and 0.9076 µM in the case of the SPE/ Fe3O4-MWCNT sensor (following optimization of the working parameters). Due to the modifying material, the results showed superior performance for the SPE/Fe3O4-CNF sensor, with extended linearity ranges and detection limits in the nanomolar range, compared to those of the SPE/Fe3O4-MWCNT sensor. For the quantification of heavy metal ions Cd2+, Pb2+, Cu2+ and Hg2+ with the SPE/Fe3O4-CNF sensor from real samples, the standard addition method was used because the values obtained for the recovery tests were good. The analysis of surface water samples from the Danube River has shown that the obtained values are significantly lower than the maximum limits allowed according to the quality standards specified by the United States Environmental Protection Agency (USEPA) and those of the World Health Organization (WHO). This research provides a complementary method based on electrochemical sensors for in situ monitoring of surface water quality, representing a useful tool in environmental studies.

Study of physico-chemical characteristics of wastewater in an urban agglomeration in Romania.

This study investigates the level of wastewater pollution by analyzing its chemical characteristics at five wastewater collectors. Samples are collected before they discharge into the Danube during a monitoring campaign of two weeks. Organic and inorganic compounds, heavy metals, and biogenic compounds have been analyzed using potentiometric and spectrophotometric methods. Experimental results show that the quality of wastewater varies from site to site and it greatly depends on the origin of the wastewater. Correlation analysis was used in order to identify possible relationships between concentrations of various analyzed parameters, which could be used in selecting the appropriate method for wastewater treatment to be implemented at wastewater plants.

Estimating annual CO(2) flux for Lutjewad station using three different gap-filling techniques.

Long-term measurements of CO(2) flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO(2) flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R(2)), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO(2) fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.