Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications

Flexible electronics for heavy metal ion detection in water: a comprehensive review

Flexible electronics offer a versatile, rapid, cost-effective and portable solution to monitor water contamination, which poses serious threat to the environment and human health. This review paper presents a comprehensive exploration of the versatile platforms of flexible electronics in the context of heavy metal ion detection in water systems. The review overviews of the fundamental principles of heavy metal ion detection, surveys the state-of-the-art materials and fabrication techniques for flexible sensors, analyses key performance metrics and limitations, and discusses future opportunities and challenges. By highlighting recent advances in nanomaterials, polymers, wireless integration, and sustainability, this review aims to serve as an essential resource for researchers, engineers, and policy makers seeking to address the critical challenge of heavy metal contamination in water resources. The versatile promise of flexible electronics is thoroughly elucidated to inspire continued innovation in this emerging technology arena.

Comprehensive review of emerging contaminants: Detection technologies, environmental impact, and management strategies

Emerging contaminants (ECs) are a diverse group of unregulated pollutants increasingly present in the environment. These contaminants, including pharmaceuticals, personal care products, endocrine disruptors, and industrial chemicals, can enter the environment through various pathways and persist, accumulating in the food chain and posing risks to ecosystems and human health. This comprehensive review examines the chemical characteristics, sources, and varieties of ECs. It critically evaluates the current understanding of their environmental and health impacts, highlighting recent advancements and challenges in detection and analysis. The review also assesses existing regulations and policies, identifying shortcomings and proposing potential enhancements. ECs pose significant risks to wildlife and ecosystems by disrupting animal hormones, causing genetic alterations that diminish diversity and resilience, and altering soil nutrient dynamics and the physical environment. Furthermore, ECs present increasing risks to human health, including hormonal disruptions, antibiotic resistance, endocrine disruption, neurological effects, carcinogenic effects, and other long-term impacts. To address these critical issues, the review offers recommendations for future research, emphasizing areas requiring further investigation to comprehend the full implications of these contaminants. It also suggests increased funding and support for research, development of advanced detection technologies, establishment of standardized methods, adoption of precautionary regulations, enhanced public awareness and education, cross-sectoral collaboration, and integration of scientific research into policy-making. By implementing these solutions, we can improve our ability to detect, monitor, and manage ECs, reducing environmental and public health risks.

Revolutionizing agriculture with nanotechnology: Innovative approaches in fungal disease management and plant health monitoring

Nanotechnology has emerged as a transformative force in modern agriculture, offering innovative solutions to address challenges related to fungal plant diseases and overall agricultural productivity. Specifically, the antifungal activities of metal, metal oxide, bio-nanoparticles, and polymer nanoparticles were examined, highlighting their unique mechanisms of action against fungal pathogens. Nanoparticles can be used as carriers for fungicides, offering advantages in controlled release, targeted delivery, and reduced environmental toxicity. Nano-pesticides and nano-fertilizers can enhance nutrient uptake, plant health, and disease resistance were explored. The development of nanosensors, especially those utilizing quantum dots and plasmonic nanoparticles, promises early and accurate detection of fungal pathogens, a crucial step in timely disease management. However, concerns about their potential toxic effects on non-target organisms, environmental impacts, and regulatory hurdles underscore the importance of rigorous research and impact assessments. The review concludes by emphasizing the significant prospects of nanotechnology in reshaping the future of agriculture but advocates for a balanced approach that prioritizes safety, sustainability, and environmental stewardship.

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

Smart optical sensing of multiple antibiotic residues from wastewater effluents with ensured specificity using SERS assisted with multivariate analysis

Surface-enhanced Raman spectroscopy offers great potential for rapid and highly sensitive detection of pharmaceuticals from environmental sources. Herein, we investigated the feasibility of label-free sensing of antibiotic residues from wastewater effluents with high specificity by combining with multivariate analysis. Highly ordered silver nanoarrays with ∼34 nm roughness have been fabricated using a cost-effective electroless deposition technique. As-fabricated Ag arrays showed superior LSPR effects with an enhancement factor of 8 × 107. Excellent reproducibility has also been noticed with RSD values within 11%, whilst the sensor showed good stability and reusability characteristics for being used as a low-cost and reusable sensor. SERS studies demonstrated that antibiotics-spiked wastewater effluents can be detected with high efficiency in a label-free method. The molecular fingerprint bands of antibiotics such as sulfamethoxazole, sulfadiazine, and ciprofloxacin were well analyzed in effluent, tap, and deionized water. It has been found that antibiotics can be detected near picomolar levels; meanwhile, liquid chromatography-mass spectrometry (LC-MS) exhibited a detection limit within nanomolar concentrations only. Furthermore, the specificity of SERS sensing has been further analyzed using a multivariate analysis method, principal component analysis followed by linear discriminant analysis (PCA-LDA); which showed prominent discrimination to distinguish each antibiotic residue from wastewater effluents. The current study presented the potential of Ag nanoarray sensors for rapid, highly specific, and cost-effective analysis of pharmaceutical products for environmental remediation applications.

Illuminating Bacterial Contamination in Water Sources: The Power of Fluorescence-Based Methods

Bacterial contamination of water sources is a significant public health concern, and therefore, it is important to have accurate and efficient methods for monitoring bacterial concentration in water samples. Fluorescence-based methods, such as SYTO 9 and PI staining, have emerged as a promising approach for real-time bacterial quantification. In this review, we discuss the advantages of fluorescence-based methods over other bacterial quantification methods, including the plate count method and the most probable number (MPN) method. We also examine the utility of fluorescence arrays and linear regression models in improving the accuracy and reliability of fluorescence-based methods. Overall, fluorescence-based methods offer a faster, more sensitive, and more specific option for real-time bacterial quantification in water samples.

A systematic review of industrial wastewater management: Evaluating challenges and enablers

The study provides a systematic literature review (SLR) encompassing industrial wastewater management research from the past decade, examining enablers, challenges, and prevailing practices. Originating from manufacturing, energy production, and diverse industrial processes, industrial wastewater's handling is critical due to its potential to impact the environment and public health. The research aims to comprehend the current state of industrial wastewater management, pinpoint gaps, and outline future research prospects. The SLR methodology involves scouring the Scopus database, yielding an initial pool of 253 articles. Refinement via search code leaves 101 articles, followed by abstract screening that reduces articles to 79, and finally 66 well-focused articles left for thorough full-text examination. Results underscore the significance of regulatory frameworks, technological innovation, and sustainability considerations as cornerstones for effective wastewater management. However, substantial impediments like; inadequate infrastructure, resource constraints and the necessity for stakeholder collaboration still exist. The study highlights emerging research domains, exemplified by advanced technologies like nanotechnology and bioremediation, alongside the pivotal role of circular economy principles in wastewater management. The SLR offers an exhaustive view of contemporary industrial wastewater management, accentuating the imperative of an all-encompassing approach that integrates regulatory, technological, and sustainability facets. Notably, the research identifies gaps and opportunities for forthcoming exploration, advocating for interdisciplinary research and intensified stakeholder collaboration. The study's insights cater to policymakers, practitioners, and researchers, equipping them to address the challenges and capitalize on prospects in industrial wastewater management effectively.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe

Optical methods such as ultraviolet/visible (UV/Vis) and fluorescence spectroscopy are well-established analytical techniques for in situ water quality monitoring. A broad range of bio-logical and chemical contaminants in different concentration ranges can be detected using these methods. The availability of results in real time allows a quick response to water quality changes. The measuring devices are configured as portable multi-parameter probes. However, their specification and data processing typically cannot be changed by users, or only with difficulties. Therefore, we developed a submersible sensor probe, which combines UV/Vis and fluorescence spectroscopy together with a flexible data processing platform. Due to its modular design in the hardware and software, the sensing system can be modified to the specific application. The dimension of the waterproof enclosure with a diameter of 100 mm permits also its application in groundwater monitoring wells. As a light source for fluorescence spectroscopy, we constructed an LED array that can be equipped with four different LEDs. A miniaturized deuterium-tungsten light source (200-1100 nm) was used for UV/Vis spectroscopy. A miniaturized spectrometer with a spectral range between 225 and 1000 nm permits the detection of complete spectra for both methods.

HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning

There is a need to develop improved methods for water quality analysis. Traditionally, water quality analysis is performed in a laboratory on discrete samples or in the field with simple sensors, but these methods have inherent limitations. Ultraviolet-visible absorption spectroscopy (UVAS) is a commonly used laboratory technique for water quality analysis and is being applied more broadly in combination with machine learning (ML) to allow for the detection of multiple analytes without sample pretreatments. This methodology (referred to here as Hydrochemical analysis using Ultraviolet-visible absorption spectroscopy and Machine learning; 'HUM') can be applied in the laboratory or in situ while requiring less time, labor, and materials compared to traditional laboratory analysis. HUM has been used for the quantification of a variety of chemicals in a variety of settings, but information is lacking related to instrumental setup, sample requirements, and data analysis procedures. For instance, there is a need to investigate the influence of spectral parameters (e.g., sensitivity, signal-to-noise ratio, and spectral resolution) on measurement error. There is also a lack of research aimed at developing ML algorithms specifically for HUM. Finally, there are emerging concepts such as sensor fusion and model-sensor fusion which have been applied to similar fields but are not common in studies involving HUM. This review suggests the need for further studies to better understand the factors that influence HUM measurement accuracy along with the need for hardware and software developments so that the methodology can ultimately become more robust and standardized. This, in turn, could increase its adoption in both academic and non-academic settings. Once the HUM methodology has matured, it could help to reduce the environmental impacts of society by improving our understanding and management of environmental systems through high-frequency data collection and automated control of water quality in environmentally relevant systems.

Advances in Surface-Enhanced Raman Scattering Sensors of Pollutants in Water Treatment

Water scarcity is a world issue, and a solution to address it is the use of treated wastewater. Indeed, in these wastewaters, pollutants such as pharmaceuticals, pesticides, herbicides, and heavy ions can be present at high concentrations. Thus, several analytical techniques were initiated throughout recent years for the detection and quantification of pollutants in different types of water. Among them, the surface-enhanced Raman scattering (SERS) technique was examined due to its high sensitivity and its ability to provide details on the molecular structure. Herein, we summarize the most recent advances (2021-2023) on SERS sensors of pollutants in water treatment. In this context, we present the results obtained with the SERS sensors in terms of detection limits serving as assessment of SERS performances of these sensors for the detection of various pollutants.

Environmental ammonia analysis based on exclusive nitrification by nitrifying biofilm screened from natural bioresource

Biofilm-based biological nitrification is widely used for ammonia removal, while hasn't been explored for ammonia analysis. The stumbling block is the coexist of nitrifying and heterotrophic microbes in real environment resulting in non-specific sensing. Herein, an exclusive ammonia sensing nitrifying biofilm was screened from natural bioresource, and a bioreaction-detection system for the on-line analysis of environmental ammonia based on biological nitrification was reported. The nitrifying microbes were aggregated into a nitrifying biofilm through a result-oriented bioresource enrichment strategy. The predominant nitrifying population and progressive surface reaction in the plug flow bioreactor led to the exclusive and exhaustive ammonia biodegradation for the establishment of a novel analytical method. The on-line ammonia monitoring prototype achieved complete biodegradation for determining ammonium nitrogen within 5 min and showed exceptional reliability in long-term real sample measurements without frequent calibration. This work offers a low-threshold natural screening paradigm for developing sustainable bioresource-based analytical technologies.

Efficient removal of veterinary drugs from aqueous solutions using magnetically separable carbonaceous materials derived from cobalt and iron metal-organic frameworks

Rapid synthesis of carbon-based magnetic materials derived from cobalt and iron metal-organic frameworks (MOFs), ZIF-67, and MIL-100(Fe), by microwave-assisted method, followed by carbonization under a N₂ atmosphere is described in this study. The carbon-derived MOFs (CDMs) were evaluated for the removal of the emerging pollutants sulfadiazine (SDZ) and flumequine (FLU) used as veterinary drugs. The study aimed to link the adsorption behavior with their surface properties and elemental composition. C-ZIF-67 and C-MIL-100(Fe) showed hierarchical porous structures with specific surface areas of 295.6 and 163.4 m² g^-1, respectively. The Raman spectra of the CDMs show the characteristic D and G bands associated with defect-rich carbon and sp² graphitic carbon, respectively. The CDMs exhibit cobalt species (Co₃O₄, CoO, and Co) in C-ZIF-67 and iron species (Fe₂O₃, Fe₃O₄, and Fe) in C-MIL-100 (Fe) which are related to the magnetic behavior of CDMs. C-ZIF-67 and C-MIL-100 (Fe) had saturation magnetization values of 22.9 and 53.7 emu g^-1, respectively, allowing easy solid-liquid separation using a magnet. SDZ and FLU removal rates on CDMs follow pseudo-second-order kinetics, and adsorption isotherms fit the Langmuir model based on regression coefficient values. Adsorption thermodynamics calculations showed that the adsorption of SDZ and FLU by CDMs was a thermodynamically favorable process. Therefore, these properties of C-ZIF-67 and C-MIL-100 (Fe) and their regeneration ability facilitate their use as adsorbents for emerging pollutants.

The Antifungal Properties of Tamarix aphylla Extract against Some Plant Pathogenic Fungi

Tamarix aphylla is a Saudi herb, which possesses antimicrobial properties and potentially introduces a solution to the subsequent dilemma caused by agrochemicals and antifungal misuse. The current study aimed to assess the fungicidal properties of water and ethanolic extracts of T. aphylla leaves against Macrophomina phaseolina, Curvularia spicifera, and Fusarium spp. The chemical composition of T. aphylla was evaluated by gas chromatography/mass spectrometry technique (GC−MS) and Fourier-transform infrared spectroscopy (FTIR). The antifungal assay assessed the fungal growth inhibition using the poisoned food technique. Scanning and transmission electron microscopy (SEM and TEM) were used to evaluate the structural changes induced in the fungal species post-treatment by T. aphylla. FTIR and GC−MS analysis revealed that T. aphylla extracts were rich in aromatic and volatile compounds, such as Benzeneselenol, Gibberellic acid, and Triaziquone, which proved multiple antifungal properties. The results showed significant inhibition in the growth of all species (p < 0.05) except for F. moniliforme, where the water extract induced the highest mycelial growth inhibition at the dose of 30%. The highest inhibition was for M. phaseolina treated with the water extract (36.25 ± 1.06 mm, p < 0.001) and C. spicifera, treated with the ethanolic extract (27.25 ± 1.77 mm, p < 0.05), as compared to the untreated control and the positive control of Ridomol. SEM and TEM revealed some ultrastructural changes within the fungal growth of treated M. phaseolina, which included the thickening and mild rupture of mycelia. Those findings suggested the robust antifungal properties of T. aphylla against some filamentous fungi. The phenolic composition illustrated the potential fungicidal properties of T. aphylla. Additional studies are required to focus on more antimicrobial properties of T. aphylla against other species, particularly those that might benefit the medical field.

Detection of Emerging Pollutants Using Aptamer-Based Biosensors: Recent Advances, Challenges, and Outlook

The synergistic potentialities of innovative materials that include aptamers have opened new paradigms in biosensing platforms for high-throughput monitoring systems. The available nucleobase functional moieties in aptamers offer exclusive features for bioanalytical sensing applications. In this context, compared to various in-practice biological recognition elements, the utilization of aptamers in detection platforms results in an extensive range of advantages in terms of design flexibility, stability, and sensitivity, among other attributes. Thus, the utilization of aptamers-based biosensing platforms is extensively anticipated to meet unaddressed challenges of various in-practice and standard analytical and sensing techniques. Furthermore, the superior characteristics of aptasensors have led to their applicability in the detection of harmful pollutants present in ever-increasing concentrations in different environmental matrices and water bodies, seeking to achieve simple and real-time monitoring. Considering the above-mentioned critiques and notable functional attributes of aptamers, herein, we reviewed aptamers as a fascinating interface to design, develop, and deploy a new generation of monitoring systems to aid modern bioanalytical sensing applications. Moreover, this review aims to summarize the most recent advances in the development and application of aptasensors for the detection of various emerging pollutants (EPs), e.g., pharmaceutical, and personal care products (PPCPs), endocrine-disrupting chemicals (EDCs), pesticides and other agricultural-related compounds, and toxic heavy elements. In addition, the limitations and current challenges are also reviewed, considering the technical constraints and complexity of the environmental samples.

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

Occurrence and distribution of heavy metals in water and soil sediments of Vellore District, Tamil Nadu, India

The occurrence of heavy metals (HMs) in water and soil sediments represents a serious environmental concern. This study revealed the presence and distribution of HMs in water and soil sediments of various places in Vellore District, Tamil Nadu, India. Twenty-one sites were selected along the study area, and inductively coupled plasma-optical emission spectrometry (ICP-OES) was used to analyze the concentration of the heavy metals. The dominance of various HMs in the soil sediment sample follows the order strontium (Sr) > Manganese (Mn) > Barium (Ba) > Zinc (Zn) > Nickel (Ni) and Sr > Mn > Zn > Boron (B) > , respectively. It was found that the concentration of HMs in water and soil sediments in Ambur market and Mottukollai area was significantly higher than the recommended limits. Thus, the results showed that the presence of HMs in water and soil sediments could be threatened pollution factors unsafe for irrigation, drinking, and other human activities.

Potential Development of N-Doped Carbon Dots and Metal-Oxide Carbon Dot Composites for Chemical and Biosensing

Among carbon-based nanomaterials, carbon dots (CDs) have received a surge of interest in recent years due to their attractive features such as tunable photoluminescence, cost effectiveness, nontoxic renewable resources, quick and direct reactions, chemical and superior water solubility, good cell-membrane permeability, and simple operation. CDs and their composites have a large potential for sensing contaminants present in physical systems such as water resources as well as biological systems. Tuning the properties of CDs is a very important subject. This review discusses in detail heteroatom doping (N-doped CDs, N-CDs) and the formation of metal-based CD nanocomposites using a combination of matrices, such as metals and metal oxides. The properties of N-CDs and metal-based CDs nanocomposites, their syntheses, and applications in both chemical sensing and biosensing are reviewed.

A field-deployable water quality monitoring with machine learning-based smartphone colorimetry

Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.

Advances in fabrication of molecularly imprinted electrochemical sensors for detection of contaminants and toxicants

Worldwide growing concerns about water contamination and pollution have increased significant interest in trace level sensing of variety of contaminants. Thus, there is demand for fabrication of low cost, miniaturized sensing device for in-situ detection of contaminants from the complex environmental matrices capable of providing selective and sensitive detection. Molecularly imprinted polymers (MIPs) has portrayed a substantial potential for selective recognition of various toxicants from a variety of environmental matrices, thus widely used as artificial recognition element in the electrochemical sensors (ECS) owing to their chemical stability, easy and low cost synthesis. The combination of nanomaterials modifiers with MIPs has endowed MIP-ECS with significantly improved sensing performance in the recent years, as the nanomaterial provide properties such as increased surface area, increased conductivity and electrocatalytic activity with enhanced electron transport phenomena, whereas MIPs provide selective recognition effect. In the present review, we have summarized the advances of MIP-ECS electrochemical sensors reported in last six years (2017-2022) for sensing of variety of contaminates including drugs, metal ions, hormones and emerging contaminates. Scope of computational modelling in design of sensitive and selective MIP-ECS is reviewed. We have focused particularly on the synthetic protocols for MIPs preparation including bulk, precipitation, electropolymerization, sol-gel and magnetic MIPs. Moreover, use of various nanomaterial as modifiers and sensitizers and their effects on the sensing performance of resulting MIP-ECS is described. Finally, the potential challenges and future prospects in the research area of MIP-ECS have been discussed.

Nanomaterials in bioelectrochemical devices: on applications enhancing their positive effect

Electrochemical biosensors and biofuel cells are finding an ever-increasing practical application due to several advantages. Biosensors are miniature measuring devices, which can be used for on-the-spot analyses, with small assay times and sample volumes. Biofuel cells have dual benefits of environmental cleanup and electric energy generation. Application of nanomaterials in biosensor and biofuel-cell devices increases their functioning efficiency and expands spheres of use. This review discusses the potential of nanomaterials in improving the basic parameters of bioelectrochemical systems, including the sensitivity increase, detection lower-limit decrease, detection-range change, lifetime increase, substrate-specificity control. In most cases, the consideration of the role of nanomaterials links a certain type of nanomaterial with its effect on the bioelectrochemical device upon the whole. The review aims at assessing the effects of nanomaterials on particular analytical parameters of a biosensor/biofuel-cell bioelectrochemical device.

Evaluating risk, exposure, and detection capabilities for chemical threats in water

The unexpected release of chemicals into the environment requires estimation of human health risks, followed by risk management decisions. When environmental concentrations of toxicants are associated with adverse health risks, the limit for analytical measurement needs to be at or below the risk threshold. The aim of this study was to assess chemical contaminants that have the potential to produce acute adverse human health impacts following oral consumption of contaminated drinking water. The U.S. Environmental Protection Agency's (EPA) Candidate Contaminant List, version 4 (CCL4) and EPA's Selected Analytical Methods (SAM) document were screened to identify 24 chemicals that exist as a solid or liquid at room temperature, with acute oral LD₅₀ (lethal dose in 50% of the test population) values < 500 mg/kg-d and water solubility > 500 mg/L at ambient temperature. While these screening criteria were used to identify prioritized needs for targeted research, it does not imply that other chemicals on the CCL4 and SAM lists are not issues in acute and chronic exposures. Of these 24 most toxic and most soluble chemicals, this evaluation identified 6 chemicals (2-chlorovinylarsonous acid, lewisite, N-nitrosopyrrolidine, N-nitrosodiethylamine, 3-hydroxycarbofuran, and triethylamine) lacking either sufficient toxicity value information or analytical sensitivity required to detect at levels protective against adverse effects in adults for acute exposures. This assessment provides an approach for gap identification and highlights research needs related to water contamination incident involving these six priority chemicals.

Efficient k-means clustering and greedy selection-based reduction of nodal search space for optimization of sensor placement in the water distribution networks

Monitoring of water distribution network (WDN) requires placement of sensors at strategic locations to detect maximum contamination events at the earliest. The multi-objective optimization (MOO) of sensor placement is a complicated problem owing to its combinatorial nature, interconnected and large WDN sizes, and temporal flows producing complex outcomes for a given set of contamination events. In this study, a new method is proposed to reduce the complexity of the problem by condensing the nodal search space. This method first segregates the nodes based on intrusion events detected, using k-means clustering, followed by selecting nodes from each group based on the improvement observed in the objectives, namely, contamination event detection, expected detection time, and affected population. The selected nodes formed the decision variable space for the MOO study. The developed strategy was tested on two benchmark networks: BWSN Network1 and C-town network, and its performance is compared with the traditional method in terms of hypervolume contribution rate (CR) indicator and the number of Pareto points. The optimal subset of nodes generated twice the number of Pareto points than the complete set of nodes set for placing 20 sensors and had 10% more than CR indicator than the traditional method. For the placement of 5 sensors, the proposed solutions were better at the higher detection likelihood values, which is required to achieve maximum detection. The proposed sensor placement algorithm can be easily scaled to large WDNs. It is expected to provide a better optimal sensor placement solution irrespective of network size as compared to the traditional approach.

Design and Development of a Bimodal Optical Instrument for Simultaneous Vibrational Spectroscopy Measurements

Vibrational spectroscopy techniques are widely used in analytical chemistry, physics and biology. The most prominent techniques are Raman and Fourier-transform infrared spectroscopy (FTIR). Combining both techniques delivers complementary information of the test sample. We present the design, construction, and calibration of a novel bimodal spectroscopy system featuring both Raman and infrared measurements simultaneously on the same sample without mutual interference. The optomechanical design provides a modular flexible system for solid and liquid samples and different configurations for Raman. As a novel feature, the Raman module can be operated off-axis for optical sectioning. The calibrated system demonstrates high sensitivity, precision, and resolution for simultaneous operation of both techniques and shows excellent calibration curves with coefficients of determination greater than 0.96. We demonstrate the ability to simultaneously measure Raman and infrared spectra of complex biological material using bovine serum albumin. The performance competes with commercial systems; moreover, it presents the additional advantage of simultaneously operating Raman and infrared techniques. To the best of our knowledge, it is the first demonstration of a combined Raman-infrared system that can analyze the same sample volume and obtain optically sectioned Raman signals. Additionally, quantitative comparison of confocality of backscattering micro-Raman and off-axis Raman was performed for the first time.

Synthesis and Characterization of Activated Carbon from Agrowastes for the Removal of Acetic Acid from an Aqueous Solution

In this study, activated carbons prepared from agrowastes by chemical activation were used to remove acetic acid from an aqueous solution through a batch process. The prepared adsorbents were characterized by SEM, XRD, FT-IR, and point of zero charge (pHpzc). The effects of adsorbent dosage, initial concentration, and contact time were considered. Equilibrium data was tested using Langmuir, Freundlich, Temkin, and Frenkel–Halsey–Hill models. The degree of adsorption of acetic acid increased for both adsorbents as contact time, and adsorbent dosage and initial concentration were increased. The adsorption data were described well by the (Freundlich=Frenkel–Halsey–Hill) models with the highest regression coefficient of $R^2=0.9961$ and $R^2=0.9951$ for Rice Husk Activated Carbon (RH-AC) and Potato Peels Activated Carbon (PP-AC), respectively. This suggests a multilayer through the existence of a heterogeneous pore distribution in the adsorbent surface. Kinetic data agreed well with pseudosecond-order ( $R^2=0.999$ and $R^2=0.994$ ) RH-AC and PP-AC, correspondingly. This indicates that the adsorption process was chemisorption in nature. The regeneration studies showed that the adsorbents prepared could be renewed and reused before losing their adsorbing affinity for acetic acid.

Field-Effect Transistor-Based Biosensors for Environmental and Agricultural Monitoring

The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called "Sustainable Development Goals" of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review

Monitoring water quality is an essential tool for the control of pollutants and pathogens that can cause damage to the environment and human health. However, water quality analysis is usually performed in laboratory environments, often with the use of high-cost equipment and qualified professionals. With the progress of nanotechnology and the advance in engineering materials, several studies have shown, in recent years, the development of technologies aimed at monitoring water quality, with the ability to reduce the costs of analysis and accelerate the achievement of results for management and decision-making. In this work, a review was carried out on several low-cost developed technologies and applied in situ for water quality monitoring. Thus, new alternative technologies for the main physical (color, temperature, and turbidity), chemical (chlorine, fluorine, phosphorus, metals, nitrogen, dissolved oxygen, pH, and oxidation–reduction potential), and biological (total coliforms, Escherichia coli, algae, and cyanobacteria) water quality parameters were described. It was observed that there has been an increase in the number of publications related to the topic in recent years, mainly since 2012, with 641 studies being published in 2021. The main new technologies developed are based on optical or electrochemical sensors, however, due to the recent development of these technologies, more robust analyses and evaluations in real conditions are essential to guarantee the precision and repeatability of the methods, especially when it is desirable to compare the values with government regulatory standards.

Management and health risk assessment of chemical contamination events in water distribution systems using PSO

An accidental or intentional contamination event can raise health and sociopolitical concerns, erode public trust, and affect the operation of water distribution systems. In this regard, emergency management plans are required to describe the necessary measures in order to deal with a threat. This study was carried out to investigate the best ways to manage intrusion in a water distribution network. In this research, the optimal management approach to deal with chemical contamination in a water distribution network was examined under three scenarios using the particle swarm optimization method. In each scenario, three management solutions were used to manage the contamination, including closing the pipe, opening the fire hydrant, and using a combination of pipe closure and fire hydrant opening. Contamination risk impact on consumers' health was assessed in the network's emergency status and after implementation of the best pollution management scenarios. The results showed that in the benchmark network, pipe closure was slightly more successful than opening of the fire hydrant valve. In pollution management of a real network, pipe closure was less effective than the hydrant opening in all scenarios. Generally, all applied scenarios were successful in reducing the contamination risk among the exposed people, so that carcinogenic and non-carcinogenic risks reduced by 100% in all scenarios compared to the non-management state.

Transcutaneous Flexible Sensor for In Vivo Photonic Detection of pH and Lactate

Clinical research shows that frequent measurements of both pH and lactate can help guide therapy and improve patient outcome. However, current methods of sampling blood pH and lactate make it impractical to take readings frequently (due to the heightened risk of blood infection and anemia). As a solution, we have engineered a subcutaneous pH and lactate sensor (PALS) that can provide continuous, physiologically relevant measurements. To measure pH, a sheet containing a pH-sensitive fluorescent dye is placed over 400 and 465 nm light-emitting diodes (LEDs) and a filter-coated photodetector. The filter-coated photodetector collects an emitted signal from the dye for each LED excitation, and the ratio of the emitted signals is used to monitor pH. To measure lactate, two sensing sheets comprising an oxygen-sensitive phosphorescent dye are each mounted to a 625 nm LED. One sheet additionally comprises the enzyme lactate oxidase. The LEDs are sequentially modulated to excite the sensing sheets, and their phase shift at the LED drive frequency is used to monitor lactate. In vitro results indicate that PALS successfully records pH changes from 6.92 to 7.70, allowing for discrimination between acidosis and alkalosis, and can track lactate levels up to 9 mM. Both sensing strategies exhibit fast rise times (< 5 min) and stable measurements. Multianalyte in vitro models of physiological disorders show that the sensor measurements consistently quantify the expected pathophysiological trends without cross talk; in vivo rabbit testing further indicates usefulness in the clinical setting.

Using data mining techniques to isolate chemical intrusion in water distribution systems

The security of water distribution systems has become the subject of an increasing volume of research over the last decade. Data analysis and machine learning are linked to hydraulic and quality modeling for improving the capacity of water utilities to save lives when faced with the contamination of water networks. This research applies k-nearest neighbor and random forest algorithms to estimate the location of contamination sources at near-real time. Epanet and Epanet-MSX software are used to simulate intrusions of pesticide into water distribution system and the interaction with compounds already present in water bulk. Different pesticide concentrations are considered in the simulations, and chlorine monitoring occurs through placed quality sensors. The results show that random forest can localize [Formula: see text] of contamination scenarios, while the KNN algorithm found [Formula: see text]. Finally, an assessment of contamination spread is made for a better understanding of the impacts of non-localized contamination.

Detection of low numbers of bacterial cells in a pharmaceutical drug product using Raman spectroscopy and PLS-DA multivariate analysis

Fast and non-invasive approach to detect drug product (DP) samples with low numbers of bacteria within the primary packaging. The method combines Raman spectroscopy and partial least squared discriminant analysis (RS-PLS-DA).

Spectrofluorimetric-based approach to screen urine contamination in drinking water: A step toward the development of screening method for leptospirosis

Hygiene hypothesis and sanitization are two important pivots of modern civilization. The drinking water should be free from urine and stool contamination. Coliform test is popular for understanding feces contamination. However, understanding urine contamination in drinking water is a difficult task. On the other hand, urine contamination can cause disease like leptospirosis. It occurs mainly in animals and infects humans through contaminated water, food, and soil and causes serious consequences. Rat urine is the most common source of such disease outbreaks. Further, sophisticated laboratories with high-end technologies may not be present at the site of disease outbreaks. In this context, we have proposed a spectrofluorimetric approach to screen urine contamination in water. The screening method can sense up to 156 nl/ml of rat urine.

A peroxidase-like activity-based colorimetric sensor array of noble metal nanozymes to discriminate heavy metal ions

Heavy metal ions (HMIs), including Cu²⁺, Ag⁺, Cd²⁺, Hg²⁺, and Pb²⁺ from the environment pose a threat to human beings and can cause a series of life-threatening diseases. Therefore, colorimetric sensors with convenience and flexibility for HMI discrimination are still required. To provide a solution, a peroxidase-like activity-based colorimetric sensor array of citrate-capped noble metal nanozymes (osmium, platinum, and gold) has been fabricated. Some studies reported that some HMIs could interact with the noble metal nanozymes leading to a change in their peroxidase-like activity. This phenomenon was confirmed in our work. Based on this principle, different concentrations of HMIs (Cu²⁺, Ag⁺, Cd²⁺, Hg²⁺, and Pb²⁺) were discriminated. Moreover, their practical application has been tested by discriminating HMIs in tap water and SiYu lake water. What is more, as an example of the validity of our method to quantify HMIs at nanomolar concentrations, the LOD of Hg²⁺ was presented. To sum up, our study not only demonstrates the differentiation ability of this nanozyme sensor array but also gives hints for using nanozyme sensor arrays for further applications.

Recording Temporal Signals with Minutes Resolution Using Enzymatic DNA Synthesis

Employing DNA as a high-density data storage medium has paved the way for next-generation digital storage and biosensing technologies. However, the multipart architecture of current DNA-based recording techniques renders them inherently slow and incapable of recording fluctuating signals with subhour frequencies. To address this limitation, we developed a simplified system employing a single enzyme, terminal deoxynucleotidyl transferase (TdT), to transduce environmental signals into DNA. TdT adds nucleotides to the 3'-ends of single-stranded DNA (ssDNA) in a template-independent manner, selecting bases according to inherent preferences and environmental conditions. By characterizing TdT nucleotide selectivity under different conditions, we show that TdT can encode various physiologically relevant signals such as Co²⁺, Ca²⁺, and Zn²⁺ concentrations and temperature changes in vitro. Further, by considering the average rate of nucleotide incorporation, we show that the resulting ssDNA functions as a molecular ticker tape. With this method we accurately encode a temporal record of fluctuations in Co²⁺ concentration to within 1 min over a 60 min period. Finally, we engineer TdT to allosterically turn off in the presence of a physiologically relevant concentration of calcium. We use this engineered TdT in concert with a reference TdT to develop a two-polymerase system capable of recording a single-step change in the Ca²⁺ signal to within 1 min over a 60 min period. This work expands the repertoire of DNA-based recording techniques by developing a novel DNA synthesis-based system that can record temporal environmental signals into DNA with a resolution of minutes.

Detecting Climate Driven Changes in Chlorophyll-a Using High Frequency Monitoring: The Impact of the 2019 European Heatwave in Three Contrasting Aquatic Systems

The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.

Paper-based platforms for microbial electrochemical cell-based biosensors: A review

The development of low-cost analytical devices for on-site water quality monitoring is a critical need, especially for developing countries and remote communities in developed countries with limited resources. Microbial electrochemical cell-based (MXC) biosensors have been quite promising for quantitative and semi-quantitative (often qualitative) measurements of various water quality parameters due to their low cost and simplicity compared to traditional analytical methods. However, conventional MXC biosensors often encounter challenges, such as the slow establishment of biofilms, low sensitivity, and poor recoverability, making them unable to be applied for practical cases. In response, MXC biosensors assembled with paper-based materials demonstrated tremendous potentials to enhance sensitivity and field applicability. Furthermore, the paper-based platforms offer many prominent features, including autonomous liquid transport, rapid bacterial adhesion, lowered resistance, low fabrication cost (<$1 in USD), and eco-friendliness. Therefore, this review aims to summarize the current trend and applications of paper-based MXC biosensors, along with critical discussions on their field applicability. Moreover, future advancements of paper-based MXC biosensors, such as developing a novel paper-based biobatteries, increasing the system performance using an unique biocatalyst, such as yeast, and integrating the biosensor system with other advanced tools, such as machine learning and 3D printing, are highlighted.

Rapid Potentiometric Detection of Chemical Oxygen Demand Using a Portable Self-Powered Sensor Chip

Chemical oxygen demand (COD) is an important indicator of organic pollutants in water bodies. Most of the present testing methods have the disadvantages of having complicated steps, being time-consuming, and using toxic and hazardous substances. In this work, rapid potentiometric detection of chemical oxygen demand (COD) using a portable self-powered sensor chip was successfully developed. The indium tin oxide (ITO) electrode was etched by laser, and the photocatalytic materials TiO₂/CuS and Pt were modified onto the photoanode and the cathode to prepare the sensor chip. Based on the principle of photocatalytic degradation, organic pollutants can be oxidized by TiO₂/CuS, and the concentration will affect the generated voltage. The quantitative detection of COD in the range of 0.05-50 mg/L can be rapidly achieved within 5 min by a miniature device. Besides good portability and sensitivity, the proposed sensor also has the advantages of environmental friendliness and ease of use, which is an ideal choice for the on-site detection of water pollution.

Adaptive monitoring approach to assess dissolved organic matter dynamics during rainfall events

Rainfall events induce water quality transformation in river systems influenced by the watershed land use and hydrology dynamics. In this context, an adaptive monitoring approach (AMA) is used to assess non-point sources (NPS) of pollution events, through dissolved organic matter (DOM) contribution. The case study is a monitoring site in a semi-urban watershed characterized by NPS contribution. An integrated quali-quantitative method for DOM based on dissolved organic carbon (DOC) content, spectroscopic techniques of excitation-emission fluorescence (EEF), and UV-visible absorbance is proposed. The results indicate a mix of allochthonous and autochthonous DOM characteristics from NPS sources associated to vegetation area influence (A₂₈₅/DOC of 15.43 L (g cm)^-1 and SUVA₂₅₄ of 2.11 L (mg m)^-1). The EEF signals showed more humic-like than protein-like characteristics with peaks A and C (approximately 5.72 r.u.) more intense than peaks B, T₁, and T₂ (approximately 4.33 r.u.), indicating NPS from the soil leachate. The absorbance ratio values indicate a mix of organic compounds with greater proportion of refractory characteristics with high aromaticity and molecular weight (approximately A₃₀₀/A₄₀₀ of 4.15 and A₂₅₀/A₃₆₅ of 4.48), associated with the surface wash-off of accumulated residual and subsurface soil erosion, which contribute to complex organic matter structures. The fluorescence indexes, overall, indicated allochthonous sources with intermediate humic characteristics (FI ≈ 1.43, BIX ≈ 0.65, and HIX ≈ 7.98). The proposed integrated optical property strategy represents an opportunity for better understanding of DOM dynamic assessment for identifying potential mitigation techniques for organic pollution control and improving water quality conditions.

A Combined pH-Impedance System Suitable for Portable Continuous Sensing

In this paper, a combined pH and impedance sensing system suitable for portable measurements is presented. The sensor outputs are converted directly to frequency or pulse width. The pH sensor is based on a voltage clamp topology that uses charging and discharging capacitors, voltage window comparators, and an SR-Latch to convert the output to frequency. The impedance to frequency sensor is based on current and voltage comparators and an SR-Latch. The pH system based on ISFET transistors is experimentally verified with on chip electrodes while the impedance sensor is characterized with discrete electronic components. The portable system is implemented with two chips and an external multi-electrode array into a portable system. Resistance, capacitance, and pH are experimentally measured using buffer solutions to simulate a water quality monitoring application. The system is implemented in a portable format and all modules, excluding the commercial microprocessor, consume an average power of 56 μW with an area of 0.006 mm ² using a 180 nm technology.

Regenerable ZnO/GaAs Bulk Acoustic Wave Biosensor for Detection of Escherichia coli in "Complex" Biological Medium

A regenerable bulk acoustic wave (BAW) biosensor is developed for the rapid, label-free and selective detection of Escherichia coli in liquid media. The geometry of the biosensor consists of a GaAs membrane coated with a thin film of piezoelectric ZnO on its top surface. A pair of electrodes deposited on the ZnO film allows the generation of BAWs by lateral field excitation. The back surface of the membrane is functionalized with alkanethiol self-assembled monolayers and antibodies against E. coli. The antibody immobilization was investigated as a function of the concentration of antibody suspensions, their pH and incubation time, designed to optimize the immunocapture of bacteria. The performance of the biosensor was evaluated by detection tests in different environments for bacterial suspensions ranging between 10³ and 10⁸ CFU/mL. A linear dependence between the frequency response and the logarithm of E. coli concentration was observed for suspensions ranging between 10³ and 10⁷ CFU/mL, with the limit of detection of the biosensor estimated at 10³ CFU/mL. The 5-fold regeneration and excellent selectivity towards E. coli detected at 10⁴ CFU/mL in a suspension tinted with Bacillus subtilis at 10⁶ CFU/mL illustrate the biosensor potential for the attractive operation in complex biological media.