Detection Of Contaminants In Water Research Articles

Review on the Use of Magnetic Nanoparticles in the Detection of Environmental Pollutants

Magnetic nanomaterials (MNPs) have been widely used in the detection of pollutants in the environment because of their excellent nano effect and magnetic properties. These intrinsic properties of MNPs have diversified their application in environmental contaminant detection. In this paper, the research status quo of the use of MNPs in detecting organic and inorganic contaminants from wastewater and soil is reviewed. The preparation method and modification technology of magnetic nanoparticles are also described in detail. The application prospect of magnetic nanoparticle composites in the detection of contaminants in water and soil is discussed. Compared with traditional detection methods, MNPs are more accurate and efficient in pollutant enrichment. Moreover, the biological synthesis of MNPs was proven to be eco-friendly and aided in sustainable development. The study shows that MNPs have good application prospects in soil pollution detection, but the mechanism still needs to be investigated to realize their popularization and application.

Ultra-trace level detection of nickel(II) ions in real water samples using surface anchored recognition probes at liquid crystal sensing platform

Rational fabrication approach for ultra-trace level detection of nickel (Ni(II)) ions in water samples has been developed using surface anchored recognition probes at liquid crystal sensing platform. In this system, the optimized volume per cent (5:1, %v/v) of synthesized surface anchored diacetate probes along with DMOAP decorated on glass substrate is able to perfectly align 5-cyanobiphenyl (5-CB) liquid crystal (LC) giving dark textural pattern. On the other hand, the fabricated probe is able to selectively reorient the transitions of the decorated LC molecules in the presence of Ni(II) ions too, leading to bright textural pattern under cross polarizers. The optimized and fabricated system demonstrated high selectivity and sensitivity, detecting Ni(II) ions as low as 0.08 μM (∼4.7 ppb) in distilled water and 0.8 μM (∼47 ppb) in real water with results observable to the naked eye. Moreover, the sensor can detect Ni(II) in drinking water, providing an approach to develop LC-based sensor for detection of natural water contaminants with simplified operating techniques.

Coordination Unsaturation and Basic Site-Immobilized Nanochannel in a Chemorobust MOF for 3-Fold-Increased High-Temperature Selectivity and Fixation of CO2 under Mild Conditions with Nanomolar Recognition of Roxarsone.

A multifaceted metal-organic framework (MOF) with task-specific site-engineered pores can promise high-temperature and moisture-tolerant capture and non-redox fixation of CO2 under mild conditions as well as ultrasensitive detection of carcinogenic contaminants in water. Herein, we report a pillar-bilayered MOF that holds a nanochannel with contrasting functionalities for both these sustainable applications with improved performance characteristics. The twofold entangled robust framework exhibits CO2 adsorption at elevated temperatures with considerable MOF-gas interaction. Interestingly, CO2 selectivity unveils nearly a 3-fold improvement upon the rise of temperature, affording a CO2/N2 value of 820 at 313 K, which outperforms many porous adsorbents. Additionally, breakthrough simulation establishes complete separation and attests the potential of this MOF in the separation of flue gas mixture. Importantly, minor CO2 loss during multiple capture-release cycles and under a relative humidity of 75% promise practical usability of the material. Density functional theory (DFT) not only portrays the atomistic level snapshots of temperature-triggered CO2 inclusion inside this microporous vessel alongside the role of diverse CO2-philic sites but also validates the basis of N2-phobicity of an azo-functionalized linker on such increased selectivity. The guest-free MOF further demonstrates non-redox and recyclable CO2 fixation with wide epoxide tolerance under solvent-free mild conditions and even works at atmospheric pressure and room temperature. The crucial roles of high-density acid-base sites in both adsorption and catalysis are supported by control experiments and by comparing the activity of an unfunctionalized MOF. The hydrolytic stability and strong luminescence signature benefit the framework in aqueous-phase selective and fast responsive detection of detrimental roxarsone (ROX) with high quenching (7.56 × 104 M-1) and very low sensitivity (68 nM). Apart from varying degrees of an energy-transfer mechanism, the fluorosensing of ROX is comprehensively supported by in-depth DFT studies that manifest alteration of MOF energy levels in the presence of organoarsenic compounds and depict MOF-analyte supramolecular interactions.

Versatile flexible SERS substrate for in situ detection of contaminants in water and fruits based on Ag NPs decorated wrinkled PDMS film.

Flexible surface-enhanced Raman spectroscopy (SERS) substrate has attracted great attention due to its convenient sampling and on-site monitoring capability. However, it is still challenging to fabricate a versatile flexible SERS substrate, which can be used for in situ detection of analytes either in water or on irregular solid surfaces. Here, we report a flexible and transparent SERS substrate based on a wrinkled polydimethylsiloxane (PDMS) film obtained by transferring corrugated structures on the aluminium/polystyrene bilayer film, onto which silver nanoparticles (Ag NPs) are deposited by thermal evaporation. The as-fabricated SERS substrate exhibits a high enhancement factor (∼1.19×105), good signal uniformity (RSD of 6.27%), and excellent batch-to-batch reproducibility (RSD of 7.3%) for rhodamine 6 G. In addition, the Ag NPs@W-PDMS film can maintain high detection sensitivity even after mechanical deformations of bending or torsion for 100 cycles. More importantly, being flexible, transparent, and light, the Ag NPs@W-PDMS film can both float on the water surface and conformally contact with the curved surface for in situ detection. The malachite green in aqueous environment and on apple peel can be easily detected down to 10-6 M with a portable Raman spectrometer. Therefore, it is expected that such a versatile flexible SERS substrate has great potential in on-site, in situ contaminant monitoring for realistic applications.

Significance of 3D printing for a sustainable environment

The additive manufacturing, also known as 3D printing, allows for a complete control over the entire manufacturing process that can be tuned to any application. In recent years, it has been tested for its role in various areas like environmental contaminant monitoring, providing solutions for the energy generation and storage and healthcare. To encourage sustainable detection platforms for the pollutants, the sensing electrodes have been reported to be 3D printed because of enhanced electrochemical properties coming from the high surface area of the printed materials. In general, the conventional methods of electrode preparation are time consuming, expensive, and mostly not adaptable. 3D printing however, negates all these challenges. Similarly, in the energy generation and storage field, the rapid and lightweight materials used during 3D printing make them viable and suitable alternative. Since 3D printing is a bottom-up method for the fabrication, the amount of raw material consumed can be tuned and the by-products or wastage can be minimized. Apart from these demanding areas, additive manufacturing, in last 2 years which witnessed epidemic outspread, has supported health sector immensely in fight against COVID. 3D printing allowed the rapid manufacturing of COVID-19 detection kits and helped to maintain the COVID-19 safeguards in place. Sample collection swabs, respirators, and other components of the personal protective equipment kits were among many products developed using 3D printing during the pandemic. Keeping these things in mind, this review encapsulates the use of 3 D printing for energy application, detection of water and biological contaminants, and as safeguard tool during COVID pandemic.

D-shaped photonic crystal fiber sensor based on the surface plasmon resonance effect for refractive index detection.

In this paper, a photonic crystal fiber (PCF) sensor based on the surface plasmon resonance (SPR) effect for refractive index (RI) detection is proposed. We design a D-shaped polished PCF structure consisting of air holes arranged in a hexagonal lattice. The silver film is coated on the middle channel of the polished surface of the PCF. The finite element method is used to analyze the propagation characteristics of the proposed D-shaped SPR-PCF sensor. Simulation results show that the proposed D-shaped SPR-PCF sensor has a maximum wavelength sensitivity of 30,000nm/RIU, an average wavelength sensitivity of 6785.71nm/RIU, and a maximum resolution of 3.33×10-6 R I U in the RI range of 1.22-1.36. Owing to the high wavelength sensitivity in the considered RI range, the proposed D-shaped SPR-PCF sensor is suitable for applications in water contamination detection, liquid concentration measurement, food safety monitoring, etc.

MXenes and MXene-Based Materials for Removal and Detection of Water Contaminants: A Review

MXenes and MXene-Based Materials for Removal and Detection of Water Contaminants: A Review

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.
 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.
 Drinking contaminated water can cause serious illness. Therefore, clean water is very important.

Role of Noncovalent Interactions in N,P-Functionalized Luminescent Carbon Dots for Ultrasensitive Detection of Moisture in D2O: Boosting Visible-NIR Light Sensitivity.

It is highly desirable to design cost-efficient and eco-friendly fluorometric sensors that can efficiently detect water contamination in D2O and other expensive organic solvents. Herein, we have synthesized N,P-codoped carbon dots (N,P-CDs) from o-phenylene diamine (o-PDA) and H3PO4 through the bottom-up carbonization method. Heteroatom co-doping increases the absorption cross section in the visible-NIR range, followed by the formation of stable emissive states in longer-wavelength regions. We have critically investigated the noncovalent interactions (especially H-bonding interactions) of various surface functional groups with surrounding solvent media through a detailed structure-property correlation. Based on the sensitivity of noncovalent H-bonding interactions to the stability of longer-wavelength emissive domains, we have utilized these N,P-CDs as cost-effective fluorometric sensors of water/moisture contamination in D2O especially under visible-NIR light; the optical sensitivity reaches up to 0.1 volume (%) level. The detailed sensing mechanism has been further supported by a computational study through a simple visualization approach by mapping and analyzing all possible noncovalent interactions between the CDs and the solvent medium.

Quick Clean Water: IoT and Machine Learning-Based Water Contamination Detection System

Quick Clean Water can detect water contamination in private wells, piped water, surface water, and water used for agriculture, recreation, and other purposes in developed and developing countries. Current testing systems are slow, costly, low in availability, and give back minimal results of 10-16 contaminants using expensive strips. Quick Clean Water is reusable, easy-to-use, portable, affordable, and gives advanced results of presently 21 contaminants. First, the Quick Clean Water device calculates the pH, turbidity, temperature, total dissolved solids, conductivity, and salinity using sensors. Inputting the pH, turbidity, conductivity, and TDS, a machine learning model using the Random Ensemble algorithm predicts whether the water is safe at a 55% accuracy rate, which can be improved through data augmentation. Several algorithms were tested and evaluated by the precision, recall, f-score, specificity, negative predictive value, and accuracy rates. The hypothesis was that the K-Means Clustering would result in the best model, but Random Ensemble was the most efficient. If the water is classified as non-potable, users can enter the odor, color, and taste of their water into a 99% accurate ML model using the Random Ensemble algorithm to identify the exact contaminant in their water, which can be advanced by researching more contaminants.

A novel cyber-physical resilience-based strategy for water quality sensor placement in water distribution networks

ABSTRACT Water distribution networks (WDNs) employ contamination warning systems (CWS) with water quality sensors to detect water contamination. If some of these sensors are compromised during a contamination event, the consequences regarding public health can be devastating. With the emerging threat of cyber-attacks on critical infrastructure, WDNs are at risk of cyber-physical attacks that can deliberately contaminate water and concurrently mask the event by cyber-attacking sensors in the CWS. We develop a novel optimization strategy for the placement of water quality sensors that maximizes resilience under cyber-physical attack scenarios, suitable for designing a new sensor layout or upgrading an existing one. The resilient strategy is tested on multiple WDNs, focused on the upgrade problem. Results show improvement in the expected CWS detection performance under cyber-physical attacks, while the performance trade-off under nominal operation is minimal compared to classic strategies, hinting at a good design solution for cyber-wise water utilities.

A Graphene-Metasurface-Inspired Optical Sensor for the Heavy Metals Detection for Efficient and Rapid Water Treatment

Heavy metal ion contamination of water supplies has significantly increased during the last century due to advances in industry and technology. Therefore, a lot of effort was put into developing chemical and physical methods for detecting and tracking the presence of these potentially harmful solutions. Despite their comparatively high sensitivity and low detection limits, these methods are hindered by complex instrumentation and tedious, expensive, and difficult chemical processes. Therefore, in this study, we present a straightforward and effective sensing method based on the graphene metasurface for detecting several classes of heavy metal ions. A graphene-metasurface-inspired optical sensor with a glass substrate is developed that can detect Cu2+ and Mg2+ with a sensitivity of 113.92 GHz/RIU and 113.9 GHz/RIU, respectively. In addition to that, the linear fitting curve for both the metal ions is established, and R2 score of 0.9997 and 0.9982 is achieved, respectively. Furthermore, the lowest value of the figure of merit (FOM) of 2.98 RIU−1 and the maximum Quality factor (Q factor) of 11.22 is obtained. The proposed structure also exhibited a low detection limit as well as a resolution of 0.52 RIU and 78.14 THz, respectively. As a result of these findings, a simple and accurate tool for detecting water contamination with heavy metals and aqueous solutions with relatively high performance is developed.

Nanoscale carbon dot-embedded metal-organic framework for turn-on fluorescence detection of water in organic solvents.

An easy-to-use, highly selective, and real-time organic solvent quality assessment is desirable to detect water contamination in organic solvents. Herein, a one-step procedure using ultrasound irradiation was used for encapsulating nanoscale carbon dots (CDs) into metal-organic framework-199 (HKUST-1) to form CDs@HKUST-1 composite. The CDs@HKUST-1 exhibited very weak fluorescence due to photo-induced electron transfer (PET) from the CDs to the Cu2+ centers, acting as a fluorescent sensor in its off-state. The designed material can detect and discriminate water from other organic solvents, driven by turn-on fluorescence. This highly sensitive sensing platform could be applied for the detection of water in ethanol, acetonitrile, and acetone with wide linear detection ranges of 0-70% v/v, 2-12% v/v, and 10-50% v/v and limits of detection of 0.70% v/v, 0.59% v/v, and 1.08% v/v, respectively. The detection mechanism is attributed to the interruption of the PET process due to the release of fluorescent CDs after treatment with water. A smartphone-based quantitative test was successfully developed to monitor the water content in organic solvents utilizing CDs@HKUST-1 and a phone color processing application, thus making it possible to develop an on-site, real time and easy-to-use sensor for water detection.

High efficient electrochemical biosensor based on exonuclease-Ⅲ-assisted dual-recycling amplification for ultrasensitive detection of kanamycin

A target-triggered and exonuclease-Ⅲ-assisted strand displacement, dual-recycling amplification reaction-based biosensor was developed for the rapid, ultrasensitive and accurate detection of kanamycin. The robust profiling platform was constructed using high conductive MXene/VS2 for the electrode surface modification and high active CeCu2O4 bimetallic nanoparticles as nanozyme to improve the sensitivity as well as the catalytic signal amplification of the biosensor. Using the dual supplementary recycling of primer DNA and hairpin DNA, the electrochemical platform could accurately detect kanamycin to as low as 0.6 pM from the range of 5 pM to 5 μM. By profiling five other antibiotics, this platform exhibited high specificity, enhanced repeatability and reproducibility. Based on these intrinsic characteristics and by utilizing milk and water samples, the as-designed biosensor offers a remarkable strategy for antibiotic detection due to its favorable analytical accuracy and reliability, thereby demonstrating potential application prospect for various antibiotic biosensing in food quality control, water contamination detection and biological safety analysis.

Polyoxotungstate-based supramolecular complexes as multifunctional electrocatalysts for sensing water contaminants

Electrochemical detection of water contaminants driven by polyoxometalate-based materials provides a promising approach for environmental safety and diseases prevention. Herein, three polyoxotungstate-based supramolecular complexes were synthesized with formulas of [Cd3(H2O)2(trz)6][HSiW12O40]2·17H2O (1), [Cd(imi)6]2[SiW12O40]·24H2O (2), [Himi]4[SiW12O40]·H2O (3), (Htrz ​= ​1-H-1,2,4-triazole; imi ​= ​Imidazole), which were employed as multifunctional sensors for the detection of various contaminants in water. Structural analysis showed that in compound 1, the trinuclear cadmium cluster [Cd3(Htrz)6]6+ links [SiW12O40]4- anion to form a dumbbell-shaped fragment, which interacts with each other via abundant hydrogen bonds to extend the structure into three-dimensional supramolecular network. In compound 2, the windmill-shaped [Cd(imi)6]2+ cations interact with [SiW12O40]4- anion via electrostatic interactions and hydrogen bonding to form 3-D supramolecular structure. Compound 3 displays an ‘inorganic core-organic hell’ hybrid supramolecular structure constructed by [SiW12O40]4- anion cluster and four protonated organic [Himi]+ cations. Three compounds as electrochemical sensors exhibited favorable electrocatalytic activities for the detection of hexavalent chromium Cr(VI), NO2− and H2O2 with remarkable anti-interference capacity. As for the determination of Cr(VI), compound 1 shows the lowest limit of detection (LOD) of 0.86 ​μM and high sensitivity of 39.3 ​μA ​mM−1 among three compounds, which meets the WHO standard for the minimum concentration of Cr(VI) in drinking water (less than 0.962 ​μM). Moreover, the LODs of compound 1 towards NO2− and H2O2 detection are 3.74 ​μM and 1.39 ​μM along with sensitivities of 4.81 ​μA ​mM−1 for NO2− ion and 37.2 ​μA ​mM−1 for H2O2, respectively. This work provides some important insights into the design of efficient POMs-based electrochemical sensors for environmental applications.

Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water.

In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we demonstrate a deep learning-based multicapturer surface-enhanced Raman scattering (SERS) platform on plasmonic nanocube metasurfaces for multiplex determination of organophosphorus pesticides (OPPs) residues. Poly(vinylpyrrolidone), 4-mercaptobenzoic acid, and l-cysteine are assembled on Ag nanocubes (AgNCs) and act as capturers to chemically define OPPs. Meanwhile, the OPPs-captured AgNCs efficiently close the interparticle distance and generate plasmonic metasurfaces, guaranteeing ultrasensitive and reproducible SERS analysis. Furthermore, by strategically combining all capturer-OPP SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of each OPP. Based on the combined-SERS spectra, a deep learning model is trained to predict OPPs, which significantly improve the qualitative and quantitative analysis accuracy. We successfully identified multiple OPPs in farmland, river, and fishpond water using this strategy. The whole detection procedure requires only 30 min, including sampling, SERS measurements, and deep learning analyses. This combination of a multicapturer SERS platform with the deep learning algorithm creates a rapid and reliable analytical strategy for multiplex detection of target molecules, providing a potential paradigm shift for environment-related research.

Microfluidic devices for the detection of contamination in water samples: A review

Being a burgeoning environmental problem, water pollution becomes a worldwide apprehension due to the inadequate accessibility of fresh water. Heavy metals, pharmaceuticals waste, organic, and inorganic contaminants present in wastewater have shown significant threats to the environment and public health globally. Currently available monitoring techniques involve complex equipment, longer processing time, and highly skilled professionals, making these techniques inadequate. Towards achieving a portable, highly sensitive, and selective water quality control system, microfluidic devices have emerged as the technique for the need of the hour. Several researchers have turned their attention to the microfluidic devices and have been doing extensive research on the development and application of microfluidic-based devices for the detection of water contaminants. Considering the fact that microfluidic devices have already marking an impact in this field, this review article discusses latest development on the microfluidic-based water quality control devices. This review summarizes popular fabrication approaches for microfluidic devices, explored detection schemes, relevant fundamentals along with the state-of-the-art research work performed on microfluidic devices for water contamination including heavy metals, organic, and inorganic contaminants.

Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy.

Fast monitoring of water quality is a fundamental part of environmental management and protection, in particular, the possibility of qualitatively and quantitatively determining its contamination at levels that are dangerous for human health, fauna and flora. Among the techniques currently available, Raman spectroscopy and its variant, Surface-Enhanced Raman Spectroscopy (SERS), have several advantages, including no need for sample preparation, quick and easy operation and the ability to operate on the field. This article describes the application of the Raman and SERS technique to liquid samples contaminated with different classes of substances, including nitrates, phosphates, pesticides and their metabolites. The technique was also used for the detection of the air pollutant polycyclic aromatic hydrocarbons and, in particular, benzo(a)pyrene, considered as a reference for the carcinogenicity of the whole class of these compounds. To pre-concentrate the analytes, we applied a methodology based on the well-known coffee-ring effect, which ensures preconcentration of the analytes without any pretreatment of the sample, providing a versatile approach for fast and in-situ detection of water pollutants. The obtained results allowed us to reveal these analytes at low concentrations, close to or lower than their regulatory limits.

Chemical Strategies for Dendritic Magneto-plasmonic Nanostructures Applied to Surface-Enhanced Raman Spectroscopy.

Chemical analyses in the field using surface-enhanced Raman scattering (SERS) protocols are expected to be part of several analytical procedures applied to water quality monitoring. To date, these endeavors have been supported by developments in SERS substrate nanofabrication, instrumentation portability, and the internet of things. Here, we report distinct chemical strategies for preparing magneto-plasmonic (Fe3 O4 : Au) colloids, which are relevant in the context of trace-level detection of water contaminants due to their inherent multifunctionality. The main objective of this research is to investigate the role of poly(amidoamine) dendrimers (PAMAMs) in the preparation of SERS substrates integrating both functionalities into single nanostructures. Three chemical routes were investigated to design magneto-plasmonic nanostructures that translate into different ways for assessing SERS detection by using distinct interfaces. Hence, a series of magneto-plasmonic colloids have been characterized and then assessed for their SERS activity by using a model pesticide (thiram) dissolved in aqueous samples.

Use of whole-cell bioreporters to assess bioavailability of contaminants in aquatic systems.

Water contamination has become increasingly a critical global environmental issue that threatens human and ecosystems’ health. Monitoring and risk assessment of toxic pollutants in water bodies is essential to identifying water pollution treatment needs. Compared with the traditional monitoring approaches, environmental biosensing via whole-cell bioreporters (WCBs) has exhibited excellent capabilities for detecting bioavailability of multiple pollutants by providing a fast, simple, versatile and economical way for environmental risk assessment. The performance of WCBs is determined by its elements of construction, such as host strain, regulatory and reporter genes, as well as experimental conditions. Previously, numerous studies have focused on the design and construction of WCB rather than improving the detection process and commercialization of this technology. For investigators working in the environmental field, WCB can be used to detect pollutants is more important than how they are constructed. This work provides a review of the development of WCBs and a brief introduction to genetic construction strategies and aims to summarize key studies on the application of WCB technology in detection of water contaminants, including organic pollutants and heavy metals. In addition, the current status of commercialization of WCBs is highlighted.