Detection Of Pollutants In Water Research Articles

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.

Advanced Plasmonic Nanosensors for Monitoring of Environmental Pollutants

Background:Water is polluted day by day with biological and chemical toxins that can pose a serious threat to human health, animals and ecosystems. The regular identification and monitoring of biological and chemical toxins in water resources is the first step of the preventive method. The devices used in traditional detection methods such as adsorption and chromatography combined with mass spectrometry are not easy to transport for analysis and involve laborious preliminary sample preparation steps. However, the developments in nanosensors prepared with nanomaterials provide solutions to these challenges. Nanomaterials such as gold nanoparticles, graphene and quantum dots are often preferred for the surface preparation of plasmonic nanosensors for the selective, sensitive and label-free detection of very low concentrations of pollutants in water.Methods:There are different plasmonic nanosensors such as electrochemical, colorimetry and optical sensors prepared using different nanomaterials for the determination of environmental pollutants. These different detection nanosensors also have many advantages and disadvantages. In this review, the use of different nanomaterials in different types of plasmonic nanosensors for the determination of environmental pollutants, their modification and their effects on performance in terms of signal enhancement will also be discussed.Results::When the studies in the literature are examined, although many articles have been published on the detection of pollutants in water, the number of publications specific to nanomaterial-based plasmonic nanosensors for detection is quite limited. In this review, we focused on the use of different nanomaterials in the preparation of nanosensor surfaces for the detection of environmental pollutants, and the preparation, optimization, experimental analysis and application areas of different plasmonic nanosensors made in the literature for detection methodsConclusion::Recent developments in plasmonic nanosensors, nanomaterials such as gold nanoparticles, graphene and quantum dots provide ultra-sensitive detection at the molecular level, leading to extraordinary developments. Nanomaterials have important plasmonic properties and are preferred for the selective, sensitive and label-free detection of very low water pollutant concentrations. In studies conducted in the literature, it has been observed that environmental pollutants such as toxin, bacteria, heavy metal ions, pesticides, especially in water, are determined and analyzed. In these review, it was observed that the sensitive and selective properties of nanomaterial-based nanosensors yielded results with low detection limits. The current review includes developments and application-oriented progress of nanomaterial-based plasmonic nanosensors, especially for the detection and quantification of various pollutants and environmental pollutants in water.

Detection of Water Pollutants With a Nonuniform Array of Microwave Sensors

Detection of pollutants in water is conventionally performed in wet chemistry labs for samples that are collected manually. This task is time-consuming, costly, and difficult to expand to large water bodies and perform frequently. Thus, to facilitate water quality testing, a new methodology is presented based on the use of a non-uniform array of microwave sensors and applying machine learning to the collected data. The sensor elements resonate at different frequencies which cover a broad bandwidth, providing sufficient information for machine learning algorithms to determine the type of pollutant. Here, as a proof-of-concept, the proposed methodology is tested with water samples including Phosphate (PO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ), Lead (Pb), Mercury (Hg), and Chromium <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+6</sup> (Cr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+6</sup> ) with various concentrations. The proposed technology is fast, cost-effective, repeatable, and expandable for detecting a larger number of pollutants.

Application of Planarian Brain Regeneration: Detection of Water Pollution

Due to over industrialisation, the environmental pollution problem is becoming increasingly serious, especially in aquatic ecosystems. Compared with traditional physical and chemical detection methods, the use of biological indicators has become popular. The freshwater planarian Dugesia japonica is distributed extensively in aquatic ecosystems and has been applied to the area of environmental toxicology for its high chemical sensitivity. Moreover, D. japonica also has a powerful regenerative capability in which the injured planarian can regenerate a new brain in 5 days and complete an adult individual remodelling in 14 days. Therefore, it has been used as a new model organism in the field of neuro-regeneration toxicology. In our past study, D. japonica can be used as a biological indicator to detect water pollution. This can provide basic data for the detection of water pollution and provide a warning system in regard to aquatic ecosystems.

The high fluorescence sensitivity property and quenching mechanism of one-dimensional Cd-HCIA-1 sensor for nitrobenzene.

The sensing of nitroaromatic compounds in aqueous solution is closely related to environmental sustainability and human health. In this study, a novel Cd(II) coordination polymer (Cd-HCIA-1) was designed and prepared, and its crystal structure, luminescence performance, detection of nitro pollutants in water, and fluorescence quenching mechanisms were studied. Cd-HCIA-1 exhibited a one-dimensional ladder-like chain based on a T-shape ligand of 5-((4-carboxybenzyl) oxy) isophthalic acid (5-H3CIA). The H-bonds and π-π stacking interactions were then used to construct the supramolecular skeleton in common. Luminescence studies revealed that Cd-HCIA-1 can detect nitrobenzene (NB) in aqueous solution with high sensitivity and selectivity, and the limit of detection was 3.03 × 10-9 mol L-1. The fluorescence quenching mechanism of the photo-induced electron transfer for NB by Cd-HCIA-1 was obtained through an investigation of the pore structure, density of states, excitation energy, orbital interactions, hole-electron analysis, charge transfer, and electron transfer spectra by using density functional theory (DFT) and time-dependent DFT methods. NB was absorbed in the pore, π-π stacking increased the orbital overlap, and the LUMO was mainly composed of NB fragments. The charge transfer between ligands was blocked, resulting in fluorescence quenching. This study on fluorescence quenching mechanisms can be used to develop efficient explosive sensors.

Porous silica nanospheres: a nanoplatform towards protein immobilization and cogent design of biosensors for aromatic water pollutant detection

A rational approach for exploiting the physicochemical properties of porous silica nanospheres (pSN) affecting biomolecule immobilization and aromatic water pollutant monitoring has been demonstrated.

Improvement of water pollution detection method based on convolution neural network

Improvement of water pollution detection method based on convolution neural network

Flexible PDMS-Based SERS Substrates Replicated from Beetle Wings for Water Pollutant Detection

The flexible surface-enhanced Raman scattering (SERS) sensor, which has the bionic 3D nanoarray structure of a beetle-wing substrate (BWS), was successfully prepared by replicated technology and thermal evaporation. The bionic structure was replicated with polydimethylsiloxane (PDMS) and then silver (Ag) nanoisland thin films were deposited by thermal evaporation. The deposition times and thicknesses (25-40 nm) of the Ag thin films were manipulated to find the optimal SERS detection capability. The Ag nanoisland arrays on the surface of the bionic replicated PDMS were observed by scanning electron microscope (SEM), X-ray diffraction (XRD), and contact angle, which can generate strong and reproducible three-dimensional hotspots (3D hotspots) to enhance Raman signals. The water pollutant, rhodamine 6G (R6G), was used as a model molecule for SERS detection. The results show that 35 nm Ag deposited on a PDMS-BWS SERS substrate displays the strongest SERS intensity, which is 10 times higher than that of the pristine BWS with 35 nm Ag coating, due to the excellent 3D bionic structure. Our results demonstrate that bionic 3D SERS sensors have the potential to be applied in wearable devices and sensors to detect biomolecules and environmental pollutants, such as industrial wastewater, in the future.

WaterSegformer: A lightweight model for water body information extraction from remote sensing images

Accurate and efficient extraction of water body information from remote sensing images is of great help to monitor water resources at the macro level, natural disaster prediction, and water pollution detection and prevention. Although many large models have achieved extremely high accuracy in remote sensing image water segmentation tasks, lightweight models are still a non-negligible choice for many application scenarios because of the limitation of computing and storage resources. Here, WaterSegformer is described, an efficient and powerful lightweight water body segmentation model based on Segformer-b0. The Deepmask module is designed to make the model pay more attention to the details in the image and use Lovász loss to improve IoU. In addition, DeepLabv3+ is used as the teacher model to guide the training of the model in the way of relational knowledge distillation. WaterSegformer realizes 95.06% mIoU on the test set with only 6.38 G and 3.72 M of FLOPs and parameters, respectively. Experimental results show that WaterSegformer achieves an excellent balance between accuracy, computational complexity and model size, which is hardware-friendly, easy to deploy and enables real-time segmentation. This method provides a new idea for water body information extraction from remote sensing images in practical applications.