Detection Of Pollutants In Water Research Articles

Fabrication of nanofibrous membranes decorated with metal-organic frameworks for detection of pollutants in water

The environmental pollution caused by antibiotics, Fe3+ and MnO4− pollutants is becoming increasingly serious. Polyacrylonitrile (PAN) and polymethyl methacrylate (PMMA) were used and decorated with metal-organic frameworks (MOFs) to fabricated three kinds of nanofibrous membranes (NFMs) with different shapes and sizes were prepared by electrospinning technology using in situ growth method and mixed spinning method. The structures and properties of the above three kinds of NFMs were characterized. Among them, PAN@Co/Mn-MOF-74 NFM prepared by in-situ growth method based on PAN was a kind of nano-fluorescent NFM sensor with uniform structure and good fluorescence performance. It showed unique specificity and excellent sensitivity in the detection of ORN, Fe3+ and MnO4−. Compared with previously reported functionalized MOFs, PAN@Co/Mn-MOF-74 NFM has a lower limit of detection (LOD). This study provides a feasible technical route for the preparation of nano-fluorescent NFMs and the targeted detection of trace metal ions and antibiotics.

Simulation of a Sensitive Mid-infrared (MIR) D-Shaped Optical Fiber Water Pollutant Sensor

AbstractIn this work, an efficient optical sensor is proposed for the sensitive detection of various pollutants in water. The suggested optical sensor is based on an indium fluoride (InF3) glass fabricated as a D-shaped optical fiber. The polished surface of the D-shaped fiber is coated with a gold grating to induce the surface plasmon resonance (SPR). The SPR depends on the optical properties of the polluted water analyte in physical contact with the grating. The proposed optical SPR fiber sensor operates within the mid-infrared (MIR) range (3000–4500 nm) to detect any slight change in the water refractive index (RI) due to any pollutants. The full vectorial finite element method (FVFEM) is utilized to calculate the modal properties of the reported sensor. High sensor sensitivity of 17,834 nm/RIU (refractive index units) is achieved for the detection of dissolution of nitric acid (HNO3) in water at a concentration of 14% v/v (volume/volume). Additionally, the reported sensor detects the dissolution of hydrogen peroxide (H2O2) in water investigated at concentrations of 15% v/v and 30% v/v, with sensitivities of 12,308 nm/RIU and 17,143 nm/RIU, respectively. Further, suspending polystyrene beads of diameter 0.1 μm in the water at a concentration of 10% v/v gives a maximum sensitivity of 5333 nm/RIU. Therefore, the proposed sensor provides a promising approach for the detection of water pollutants in the MIR wavelength regime, rather than the weaker response in the near infrared.

Preparation of a recyclable composite aerogel with controllable peroxidase-like activity for on-site and visual analysis of the dihydroxybenzene isomers

Colorimetric detection methods have been attracted and preferred for analytes assessment. Although simple and highly efficient colorimetric strategies based on nanozymes have been developed, there remain inherent problems of intrinsic color interference for accurate analysis and erratic activity to lose controllable, recyclable, and sensitive efficacies. Herein, we have prepared a novel composite aerogel named PACO that is rationally assembled by the poly (vinyl alcohol) aerogel (PVA aerogel) and copper oxide nanoparticles (CuO NPs) to address these challenges. PACO has been proved to exhibit the controllable peroxidase (POD)-like activity through separating the solid PACO from the aqueous solutions. Meanwhile, PACO can avoid intrinsic color interferences and provide more accurate detection performance for naked-eye discrimination. Furthermore, PACO shows the recyclable and long lifetime performances for sensitive detection of water pollutants including hydroquinone (HQ) and catechol (CC) with the LOD of 0.084 μM and 0.101 μM, respectively. Finally, we have constructed a smartphone-based colorimetric detection platform to realize the on-site and visualanalysis of water pollutants in real water samples. Overall, PACO achieves the advanced detection performances to exhibit great potential in nanozymes based sensing method development and colorimetric-related analytical research.

A water-stable Y(III)-MOF as multi-responsive luminescent sensor for high-efficiency detection of Fe3+, Cu2+, and MnO4- ions in aqueous solutions

The effective detection of pollutants in water, such as Fe3+, Cu2+, and MnO4- ions, is critical to human health and environmental protection. Two yttrium-based coordination polymers {[Y(BITA)(H2O)]NO3}n (CP-1) and {[Y(HBTA)(ox)(H2O)]·H2O}n (CP-2) based on a π-conjugated 2,5-di(1H-imidazol-1-yl)terephthalic acid (H2BITA) were successfully prepared and sufficiently characterized. CP-1 exhibits a unique cationic 3D framework with a lvt net, accompanied by the decoration of NO3- anions. Adjacent Y3+ sites are bridged by double syn-syn carboxylate to form [Y2(μ-COO)2]n chains. CP-2 shows a 3D two-fold interpenetrating network with zigzag chains of [Y2(μ-C2O2)]n formed by ancillary oxalate bridging Y3+ ions. The HBTA- linkers propagate the 1D oxalate chains into 3D frameworks. The luminescent properties of CPs were investigated. Both CPs-1 and 2 exhibit purple and green fluorescence in the solid state, respectively. Meanwhile, CP-1 demonstrates excellent luminescent emission together with good chemical stability. Intriguingly, CP-1 exhibits outstanding selective and sensitive detection of cationic Fe3+ and Cu2+ and anionic MnO4- in aqueous solutions through luminescence quenching. In particular, the luminescence intensity could be significantly decreased by existence of MnO4-, which is seldom reported among the Y(III)-based coordination polymers. The quenching mechanism for various ions has been discussed in detail.

STM32 BASED WATER QUALITY MONITORING SYSTEM

Water is a precious and essential resource for all life on Earth. Ensuring the quality of water is vital for human health, environmental sustainability, and various industrial applications. This project introduces a novel STM32-based Water Quality Monitoring System designed to address the need for continuous and real-time monitoring of water parameters in various settings like pH and electrical conductivity sensors. The STM32-based Water Quality Monitoring System offers several advantages, including early detection of water pollution, enabling prompt action to mitigate environmental damage and health risks. It provides historical data for trend analysis, aiding in the identification of long-term water quality patterns and trends. Additionally, it reduces the need for manual sampling and laboratory testing, thus lowering operational costs for water treatment facilities. Furthermore, the system is highly adaptable and can be integrated into a variety of water management scenarios, including municipal water supplies, industrial processes, aquaculture, and environmental monitoring. It promotes sustainable water resource management by ensuring that water quality remains within safe and regulated standards In summary, this project presents an innovative STM32-Based Water Quality Monitoring System that leverages advanced microcontroller technology to address the critical issue of water quality monitoring. Key Words: water quality, stm32, pH sensor, conductivity sensor, aquaculture

PLANT MICROBIAL FUEL CELLS: THE CURRENT STATE AND POTENTIAL APPLICATIONS

Phyto-treatment engineering structures have recently found wide application in the biotechnology of wastewater treatment. Currently, it is important to study and use the possibility of bioelectric power generation, as well as develop energy-efficient hybrid technologies based on plant-microbial fuel cells. The purpose of the study is to summarize information on the ability of plant organisms to generate bioelectric potentials and to create plant-fuel cells as a component of an integrated phytotechnology for wastewater treatment in Eastern Siberia. The article shows applications of phytotechnologies for wastewater treatment using a system of algae, higher aquatic vegetation, microorganisms in different climatic zones, including the Arctic. These technologies have been implemented in China, where dozens of such facilities are annually built. The article describes the designs of plant-microbial fuel cells (PMFC), the electrogenic activity of s plants and microorganisms, promising areas for the use of microbial fuel cells, PMFC as power sources for small devices, sensors for early detection of water pollution. Design solutions for creating RMFC batteries for the neutralization of soils contaminated with antibiotics are presented.

Synthesis and Recognition Properties of Cd(II) Framework for Highly Selective Sensing of Fe3+, Cr2O72− and CrO42− ions

AbstractIn this study, we successfully constructed a high‐performance fluorescent material for the detection of toxic pollutants in water, [Cd(2‐bpmh)(BDC)H2O]n (1) (2‐bpmh=1,4‐bis(2‐pyridyl)‐2,3‐diaza‐1,3‐butadiene, H2BDC=terephthalic acid), which achieved high yield under the condition of solvothermal reaction. Single crystal X‐ray analysis revealed that complex 1 forms a one‐dimensional chain structure, which is further assembled into a three‐dimensional structure through hydrogen bonding interactions. The luminescence sensing research showed that 1 not only displayed excellent luminescence quenching efficiency, but also could specifically recognize Cr2O72−, CrO42− and Fe3+ ions in water.

A deep learning approach for detecting underwater plastic waste

The water pollution, flooding, and recycling issues in Indonesia are exacerbated by the critical level of underwater plastic waste. Furthermore, the lack of visibility makes it difficult to collect plastic waste in the water. In this research, a plastic detection instrument was developed to detect water pollution using a deep learning method with the You Only Look Once version 3 (YOLOv3) algorithm. The study was conducted in four steps: 1) create a custom dataset of plastic waste images, 2) annotated the plastic waste dataset, 3) configurated and train the model, 4) validated the result. The proposed model has achieved an overall mean average precision (mAP) of 83.12% This result indicated that the model is effective in detecting plastic waste in underwater environment.

Integration of Metal-Organic Polyhedra onto a Nanophotonic Sensor for Real-Time Detection of Nitrogenous Organic Pollutants in Water.

The grave health and environmental consequences of water pollution demand new tools, including new sensing technologies, for the immediate detection of contaminants in situ. Herein, we report the integration of metal-organic cages or polyhedra (MOCs/MOPs) within a nanophotonic sensor for the rapid, direct, and real-time detection of small (<500 Da) pollutant molecules in water. The sensor, a bimodal waveguide silicon interferometer incorporating Rh(II)-based MOPs as specific chemical receptors, does not require sample pretreatment and enables minimal expenditure of time and reagents. We validated our sensor for the detection of two common pollutants: the industrial corrosion inhibitor 1,2,3-benzotriazole (BTA) and the systemic insecticide imidacloprid (IMD). The sensor offers a fast time-to-result response (15 min), high sensitivity, and high accuracy. The limit of detection (LOD) in tap water for BTA is 0.068 μg/mL and for IMD, 0.107 μg/mL, both of which are below the corresponding toxicity thresholds defined by the European Chemicals Agency (ECHA). By combining innovative chemical molecular receptors such as MOPs with state-of-the-art photonic sensing technologies, our research opens the path to implement competitive sensor devices for in situ environmental monitoring.

Plasmonic nanoparticle's anti-aggregation application in sensor development for water and wastewater analysis.

Colorimetric sensors have emerged as a powerful tool in the detection of water pollutants. Plasmonic nanoparticles use localized surface plasmon resonance (LSPR)-based colorimetric sensing. LSPR-based sensing can be accomplished through different strategies such as etching, growth, aggregation, and anti-aggregation. Based on these strategies, various sensors have been developed. This review focuses on the newly developed anti-aggregation-based strategy of plasmonic nanoparticles. Sensors based on this strategy have attracted increasing interest because of their exciting properties of high sensitivity, selectivity, and applicability. This review highlights LSPR-based anti-aggregation sensors, their classification, and role of plasmonic nanoparticles in these sensors for the detection of water pollutants. The anti-aggregation based sensing of major water pollutants such as heavy metal ions, anions, and small organic molecules has been summarized herein. This review also provides some personal insights into current challenges associated with anti-aggregation strategy of LSPR-based colorimetric sensors and proposes future research directions.

Electrochemical determination of nitrate on Ag nanoparticles‐decorated poly (direct blue 15) electrodes

AbstractIn this study, for the first time, the electro‐polymerization of Direct blue15 (DB15), an azo dye, was carried out on the surface of ITO. Furthermore, the poly(DB15) surface was electrochemically decorated with Ag nanoparticles (AgNPs), and the fabricated AgNPs/PDB15 electrodes were examined as nitrate sensors. Compared to unmodified ITO electrode, the AgNPs/PDB15 electrode had greatly improved electrochemical response to nitrate reduction. The nitrate determination in a linear range from 1.0×10−5 mol L−1 to 2.27×10−3 mol L−1 was performed with a detection limit of 9.66 μM. The synthesized electrode is a promising sensor for the electrochemical detection of nitrate pollutants in water.

Prediction of biochemical oxygen demand with genetic algorithm-based support vector regression

Abstract Five-day biochemical oxygen demand (BOD5) is a vital wastewater contamination strength indicator. The process of measuring BOD5 is to measure the mass of molecular oxygen consumed in 1 L of water at 20 °C over a 5-day incubation period. It is a time-consuming process and often too late for water management agencies to make a timely reaction if the result of measurement shows a water body is seriously polluted. Biosensors can simplify the process of BOD5 measurement; however, the measurement results often deviate significantly from the measured BOD5 values. The main aim of this research is to identify a machine learning model, which could predict BOD5 value from historical data and make it easier to detect water pollution in advance and timely adopt treatment measures. Three machine learning techniques, linear regression, support vector regression (SVR) and multi-layer perceptron (MLP) and two optimization processes have been studied in this research. Four main steps, preprocessing (one-time only), model training, model evaluation (testing) and analysis have been implemented in the experiments. With three feature selection strategies, the results of the experiment showed that SVR with genetic algorithm (GA) optimizer achieved the best performance with R2 of 0.694 and the lowest MAE of 0.109.

Gold Nanoparticles as Exquisite Colorimetric Transducers for Water Pollutant Detection.

Gold nanoparticles (AuNPs) are useful nanomaterials as transducers for colorimetric sensors because of their high extinction coefficient and ability to change color depending on aggregation status. Therefore, over the past few decades, AuNP-based colorimetric sensors have been widely applied in several environmental and biological applications, including the detection of water pollutants. According to various studies, water pollutants are classified into heavy metals or cationic metal ions, toxins, and pesticides. Notably, many researchers have been interested in AuNP that detect water pollutants with high sensitivity and selectivity, while offering no adverse environmental issues in terms of AuNP use. This review provides a representative overview of AuNP-based colorimetric sensors for detecting several water pollutants. In particular, we emphasize the advantages of AuNP as colorimetric transducers for water pollutant detection in terms of their low toxicity, high stability, facile processability, and unique optical properties. Next, we discuss the status quo and future prospects of AuNP-based colorimetric sensors for the detection of water pollutants. We believe that this review will promote research and development of AuNP as next-generation colorimetric transducers for water pollutant detection.

CONTAMINANT IDENTIFICATION IN WATER BY MICROBIAL BIOSENSORS

The various micro-organisms are used to identify contaminated water. By the help of biosensors, drinking water pollution can be estimated. Rising environmental pollution is badly affecting water. Drinking contaminated water causes serious diseases in human beings as well as in animals.&#x0D; Microorganism in contaminated water can spread serious diseases. Biosensors are genetically modified to identify the occurrence of heavy metals, chemicals and toxins in contaminated water. Different microbes are used as biosensors such as Escherichia coli, Algae, Fungi and Clostridium etc. All these biosensors help to detect water pollution by different ways. For example, algae can detect water contamination by fluorescence during photosynthesis. Most rural areas are being affected by contaminated water. Most of the people living in rural areas are being affected by the polluted water as clean water is not available in these areas. People living in rural areas use water that looks clear but contains micro-pathogens that can cause dangerous diseases. Biosensors are important for identifying contamination, Due biosensor, can easily deduct pollution. Water is very important for the survival.&#x0D; Drinking contaminated water can cause serious illness. Therefore, clean water is very important.

Optical Biosensors and Their Applications for the Detection of Water Pollutants

The correct detection and quantification of pollutants in water is key to regulating their presence in the environment. Biosensors offer several advantages, such as minimal sample preparation, short measurement times, high specificity and sensibility and low detection limits. The purpose of this review is to explore the different types of optical biosensors, focusing on their biological elements and their principle of operation, as well as recent applications in the detection of pollutants in water. According to our literature review, 33% of the publications used fluorescence-based biosensors, followed by surface plasmon resonance (SPR) with 28%. So far, SPR biosensors have achieved the best results in terms of detection limits. Although less common (22%), interferometers and resonators (4%) are also highly promising due to the low detection limits that can be reached using these techniques. In terms of biological recognition elements, 43% of the published works focused on antibodies due to their high affinity and stability, although they could be replaced with molecularly imprinted polymers. This review offers a unique compilation of the most recent work in the specific area of optical biosensing for water monitoring, focusing on both the biological element and the transducer used, as well as the type of target contaminant. Recent technological advances are discussed.

Application of Three-Dimensional Fluorescence Spectroscopy in Smart Agriculture — Detection of Oil Pollutants in Water

Three-dimensional fluorescence spectroscopy is a fast, nondestructive analysis method with good selectivity and high precision, which provides a foundation for the development of the current smart agriculture system. In modern agriculture, where agricultural information is fully perceived, it is still very difficult to quickly and destructively detect the internal chemical composition of soil, crops and agricultural products. Accurate determination of oil pollutants in water by using three-dimensional fluorescence spectroscopy technology can provide a basis for crop irrigation and is of great significance for improving agricultural benefits. The fluorescence spectrum analysis method is adopted to distinguish three kinds of mineral oil-gasoline, kerosene and diesel. In order to make the distinguishment more intuitive and convenient, a new identification method for mineral oil is proposed. The three-dimensional fluorescence spectra of the experimental dimension are reduced into two-dimensional fluorescence spectra. The concrete operations are as follows: adopting the method of end-to-end data matrix to constitute a large Ex image, and then figuring out the envelope curve, processing and analyzing the envelope image. Four factors, such as the ranges of excitation wavelength when the relative fluorescence intensity is greater than 0.5, the optimal excitation wavelengths, their kurtosis coefficients and skewness coefficients, are to be selected as the distinguishing feature parameters of mineral oil, and thus different kinds of mineral oil can be distinguished directly according to the feature parameters. The experimental results show that the proposed method has a high resolution for different kinds of mineral oil. Accurate and fast spectral data analysis methods can make up for the deficiencies of other agricultural information perception methods, provide a basis for the application of smart agriculture in many aspects and have a positive significance for promoting the comprehensive intelligent development of agriculture.

Chitosan coated papers as sustainable platforms for the development of surface-enhanced Raman scattering hydrophobic substrates

Paper-based sensors for surface-enhanced Raman scattering (SERS) detection of water pollutants have gained much attention in the last decades. However, most of the fabricated paper-based SERS substrates still face some limitations regarding reproducibility and uniformity. In this work, we report the fabrication of hydrophobic paper-based SERS substrates with high reproducibility and uniformity by inkjet printing Ag colloidal nanoparticles (NPs) on office paper pre-treated with chitosan (CH) or N,N,N-trimethyl chitosan (TMC). We demonstrate that the previous coating of paper with such biopolymers substantially improves the SERS performance of the paper substrates. In screening experiments, crystal violet (CV) 100 μM was used as molecular probe, leading to an increase of about 9- and 7-fold in the observed SERS intensity when the papers were coated with CH and TMC, respectively. Also, the reproducibility of the substrates coated with the biopolymers increased more than 2 times compared to non-coated office paper. We suggest that the improved performance of the coated substrates is related to a more uniform deposition of the AgNPs on the paper surface due to the hydrophobic nature, low porosity and roughness of the coated paper.

Self-assembly and phase transition of gold nanoclusters in natural deep eutectic solvent for visual detection of toxicants in water environment

Ecological damage derived from water pollution has increased greatly accompanying by the development of industries, which promotes the exploration of water environment monitoring platform. Structure-oriented synthesis of gold nanoclusters (NADES-Au NCs) based on novel natural deep eutectic solvent (NADES), has been successfully obtained without the use of any stabilizer or capping agents. With the pH stability and photobleaching resistance in the complex environment, spongy NADES-Au NCs showed superior optical properties than other forms. Moreover, this green sensing platform based on NADES-Au NCs displayed two well-separated emission peaks at 440 and 669 nm, respectively. There was a linear range of 0.5–200 µM with a detection limit of 0.25 µM for Hg2+ and a linear range of 1–150 µM with a detection limit of 0.12 µM for nitroanilines. Such NADES-Au NCs demonstrated the satisfactory recoveries and reliabilities in water samples, which presented great potential of water pollution detection. Simultaneously, the application of NADES as a green solvent for the synthesis of metal nanoclusters may provide new avenues for sensing platform.

Conducting polymer functionalized graphene-based electrochemical sensors for sensing pollutants in water

Recent trends in electrochemical sensors based on conducting polymer functionalized graphene for the detection of pollutants in water are highlighted in this review. Graphene has been the subject of a lot of scientific research to be composited with conducting polymers. Researchers are interested in graphene and its variants because they have a lot of good qualities, like good electrical and mechanical properties and very high surface area. With this review, we intend to arouse interest in the important topic of graphene and conducting polymer nanocomposite that is making significant advances in electrochemical sensing, especially for sensing pollutants in water.

Heavy metals biosensor based on defective one-dimensional phononic crystals

In recent years, the detection of water pollution with low levels of heavy metals has attracted the great attention of many researchers as a result of the imminent danger of this type of pollution to all mankind. Meanwhile, we introduce a theoretical approach based on the one-dimensional phononic crystals (1D-PnCs) with a central defect layer as a novel platform for the highly sensitive detection of heavy metal pollution in freshwater. Therefore, the creation of a resonant peak in the transmittance spectrum related to this defect layer is highly conceivable. In this regard, the detection of cadmium chloride (CdCl2) as a dangerous, toxic, and extremely hazardous heavy metal could be investigated based on the small displacement in the position of this resonant peak with the changes in the CdCl2 concentration. Notably, any change in CdCl2 concentration has a direct impact on its acoustic properties. The theoretical framework of our research study is essentially based on the 2 × 2 transfer matrix method and the acoustic properties of the constituent materials as well. The optimization of all sensor parameters represents the mainstay of this study to get the best sensor performance. In this regard, the proposed sensor has a remarkably high sensitivity (S = 1904.25 Hz/ppm) over a concentration range of 0 – 10000 ppm. In addition, the sensor has a high quality factor (QF), and figure of merit of 1771.318, and 73529410−5 (ppm−1), respectively. Finally, we believe this sensor could be a key component of a feasible platform for detecting low concentrations of different heavy metal ions in freshwater.