A novel enzyme based biosensor for catechol detection in water samples using artificial neural network

Development of surface molecular-imprinted electrochemical sensor for palmitic acid with machine learning assistance

Palmitic acid (PA) is a kind of saturated high fatty acid, which is involved in physiological safety and food quality. A surface molecularly imprinted polymer (MIP) electrochemical sensor was prepared on MXene surface using dopamine (DA) as functional monomer. The electrode was modified with gold nanoparticles (AuNPs), ferrocene-graphene oxide-multiwalled carbon nanotubes (Fc-GO-MWCNT) composite to enhance the electroactive area and conductivity. The sensor was characterized by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), electrochemical impedance spectroscopy (EIS) and Differential pulse voltammetry (DPV), respectively. The parameters concerning this assay and various regeneration conditions have been carefully studied. The sensor can detect PA in the range of 1 nM-1 mM (R2 = 0.995), the limit of detection (LOD) is 0.48 nM (S/N = 3), and the limit of quantification (LOQ) is 1.61 nM. The artificial neural network (ANN) model in machine learning is further used to analyze the data collected by the sensor. The results show that the back propagation (BP) neural network in ANN is more suitable for the intelligent analysis of PA. The practicality of the sensor was confirmed by detecting PA in pork samples. This is the first MIP-based electrochemical sensor for PA, and it has great potential in practical applications.

Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease

Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12 mM, 22 mM, and 32 mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.

Selective bioelectronic sensing of pharmacologically relevant quinones using extracellular electron transfer in Lactiplantibacillus plantarum

Redox-active small molecules containing quinone functional groups play important roles as pharmaceuticals, but can be toxic if overdosed. Despite the need for a fast and quantitative method to detect quinone and its derivatives, current sensing strategies are often slow and struggle to differentiate between structural analogs. Leveraging the discovery that microorganisms use certain quinones to perform extracellular electron transfer (EET), we investigated the use of Lactiplantibacillus plantarum as a whole-cell bioelectronic sensor to selectively sense quinone analogs. By tailoring the native EET pathway in L. plantarum, we enabled quantitative quinone sensing of 1,4-dihydroxy-2-naphthoic acid (DHNA) - a gut bifidogenic growth stimulator. We found that L. plantarum could respond to environmental DHNA within seconds, producing concentration-dependent electrical signals. This sensing capacity was robust in different assay media and allowed for continuous monitoring of DHNA concentrations. In a simulated gut environment containing a mixed pool of quinone derivatives, this tailored EET pathway can selectively sense pharmacologically relevant quinone analogs, such as DHNA and menadione, amongst other structurally similar quinone derivatives. We also developed a multivariate model to describe the mechanism behind this selectivity and found a predictable correlation between quinone physiochemical properties and the corresponding electrical signals. Our work presents a new concept to selectively sense quinone using whole-cell bioelectronic sensors and opens the possibility of using probiotic L. plantarum for bioelectronic applications in human health.

Novel Approaches to Enzyme-Based Electrochemical Nanobiosensors

Electrochemistry is a genuinely interdisciplinary science that may be used in various physical, chemical, and biological domains. Moreover, using biosensors to quantify biological or biochemical processes is critical in medical, biological, and biotechnological applications. Nowadays, there are several electrochemical biosensors for various healthcare applications, such as for the determination of glucose, lactate, catecholamines, nucleic acid, uric acid, and so on. Enzyme-based analytical techniques rely on detecting the co-substrate or, more precisely, the products of a catalyzed reaction. The glucose oxidase enzyme is generally used in enzyme-based biosensors to measure glucose in tears, blood, etc. Moreover, among all nanomaterials, carbon-based nanomaterials have generally been utilized thanks to the unique properties of carbon. The sensitivity can be up to pM levels using enzyme-based nanobiosensor, and these sensors are very selective, as all enzymes are specific for their substrates. Furthermore, enzyme-based biosensors frequently have fast reaction times, allowing for real-time monitoring and analyses. These biosensors, however, have several drawbacks. Changes in temperature, pH, and other environmental factors can influence the stability and activity of the enzymes, affecting the reliability and repeatability of the readings. Additionally, the cost of the enzymes and their immobilization onto appropriate transducer surfaces might be prohibitively expensive, impeding the large-scale commercialization and widespread use of biosensors. This review discusses the design, detection, and immobilization techniques for enzyme-based electrochemical nanobiosensors, and recent applications in enzyme-based electrochemical studies are evaluated and tabulated.

Recent Prospects of Carbonaceous Nanomaterials-Based Laccase Biosensor for Electrochemical Detection of Phenolic Compounds

Phenolic compounds (PhCs) are ubiquitously distributed phytochemicals found in many plants, body fluids, food items, medicines, pesticides, dyes, etc. Many PhCs are priority pollutants that are highly toxic, teratogenic, and carcinogenic. Some of these are present in body fluids and affect metabolism, while others possess numerous bioactive properties such as retaining antioxidant and antimicrobial activity in plants and food products. Therefore, there is an urgency for developing an effective, rapid, sensitive, and reliable tool for the analysis of these PhCs to address their environmental and health concern. In this context, carbonaceous nanomaterials have emerged as a promising material for the fabrication of electrochemical biosensors as they provide remarkable characteristics such as lightweight, high surface: volume, excellent conductivity, extraordinary tensile strength, and biocompatibility. This review outlines the current status of the applications of carbonaceous nanomaterials (CNTs, graphene, etc.) based enzymatic electrochemical biosensors for the detection of PhCs. Efforts have also been made to discuss the mechanism of action of the laccase enzyme for the detection of PhCs. The limitations, advanced emerging carbon-based material, current state of artificial intelligence in PhCs detection, and future scopes have also been summarized.

Schiff-Base Chemistry-Coupled Catechol Oxidase-Like Nanozyme Reaction as a Universal Sensing Mode for Ultrasensitive Biosensing

Expanding sensing modes and improving catalytic performance of nanozyme-based analytical chemistry are beneficial to realizing the desired biosensing of analytes. Herein, Schiff-base chemistry coupled with a novel catechol oxidase-like nanozyme (CHzyme) is designed and constructed, exhibiting two main advantages, including (1) improving catalytic performance by nearly 2-fold compared with only the oxidase-like role of CHzyme; (2) increasing the designability of the output signal by signal transduction of cascade reaction. Thereafter, the substrate sensing modes based on a cascade reaction between the CHzyme-catalyzed reaction and Schiff-base chemistry are proposed and comprehensively studied, containing catalytic substrate sensing mode, competitive substrate sensing mode, and generated substrate sensing mode, expecting to be employed in environmental monitoring, food analyses, and clinical diagnoses, respectively. More meaningfully, the generated substrate sensing mode is successfully applied to construct a cascade reaction coupling ratiometric fluorescent immunoassay for the detection of clenbuterol, increasing 15-fold in detection sensitivity compared with the traditional enzyme-linked immunosorbent assay. It is expected that the expanded universal substrate sensing modes and the Schiff-base chemistry-enhanced nanozyme can enlighten the exploration of innovative biosensors.

Biopolymer Composites with Sensors for Environmental and Medical Applications

One of the biggest economic and environmental sustainability problems is the over-reliance on petroleum chemicals in polymer production. This paper presents an overview of the current state of knowledge on biopolymers combined with biosensors in terms of properties, compounding methods and applications, with a focus on medical and environmental aspects. Therefore, this article is devoted to environmentally friendly polymer materials. The paper presents an overview of the current state of knowledge on biopolymers combined with biosensors in terms of properties, compounding methods and applications, with a special focus on medical and environmental aspects. The paper presents the current state of knowledge, as well as prospects. The article shows that biopolymers made from renewable raw materials are of great interest in various fields of science and industry. These materials not only replace existing polymers in many applications, but also provide new combinations of properties for new applications. Composite materials based on biopolymers are considered superior to traditional non-biodegradable materials due to their ability to degrade when exposed to environmental factors. The paper highlights the combination of polymers with nanomaterials which allows the preparation of chemical sensors, thus enabling their use in environmental or medical applications due to their biocompatibility and sensitivity. This review focuses on analyzing the state of research in the field of biopolymer-sensor composites.

Recent advancements in enzyme-incorporated nanomaterials: Synthesis, mechanistic formation, and applications

Over the past decade, nanotechnology has been developed and employed across various entities. Among the numerous nanostructured material types, enzyme-incorporated nanomaterials have shown great potential in various fields, as an alternative to biologically derived as well as synthetically developed hybrid structures. The mechanism of incorporating enzyme onto a nanostructure depends on several factors including the method of immobilization, type of nanomaterial, as well as operational and environmental conditions. The prospects of enzyme-incorporated nanomaterials have shown promising results across various applications, such as biocatalysts, biosensors, drug therapy, and wastewater treatment. This is due to their excellent ability to exhibit chemical and physical properties such as high surface-to-volume ratio, recovery and/or reusability rates, sensitivity, response scale, and stable catalytic activity across wide operating conditions. In this review, the evolution of enzyme-incorporated nanomaterials along with their impact on our society due to its state-of-the-art properties, and its significance across different industrial applications are discussed. In addition, the weakness and future prospects of enzyme-incorporated nanomaterials were also discussed to guide scientists for futuristic research and development in this field.

Prediction of abrasive wears behavior of dental composites using an artificial neural network

Resin composites are widely used as dental restorative materials since dental parts are subjected to prolonged wear and ultimately need to be replaced. The objective of this study is to analyze the potential of the feed-forward back propagation artificial neural network (ANN) in assessing the wear of dental composite materials when immersed in chewable tobacco solution, by utilizing the in-vitro test results of the pin-on-disc tribometer [ASTM G99-04]. In this study, four different dental composite material specimens are dipped in a chewable tobacco solution for a few days, and the specimens are removed from the solution for conducting the wear test. Three different training procedures are used to simulate ANN models for predicting the wear of dental composite specimens. The Bayesian regularization training algorithm outperforms the other algorithms significantly. The findings of the ANN modeling were prominently matching with the results of the experiments; therefore, parametric analysis was used based on the model's predicted values.

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 µm were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10–15 µm had the highest effects on fatigue life improvement.

A highly efficient rGO grafted MoS2 nanocomposite for dye adsorption and electrochemical detection of hydroquinone in wastewater

Scheme depicting the synthesis and the fabrication of rGO–MoS2 nanocomposite-based enzymatic biosensor for estimation of hydroquinone.

Simultaneous Detection of Dihydroxybenzene Isomers Using Electrochemically Reduced Graphene Oxide-Carboxylated Carbon Nanotubes/Gold Nanoparticles Nanocomposite

An electrochemical sensor based on electrochemically reduced graphene oxide (ErGO), carboxylated carbon nanotubes (cMWCNT), and gold nanoparticles (AuNPs) (GCE/ErGO-cMWCNT/AuNPs) was developed for the simultaneous detection of dihidroxybenzen isomers (DHB) hydroquinone (HQ), catechol (CC), and resorcinol (RS) using differential pulse voltammetry (DPV). The fabrication and optimization of the system were evaluated with Raman Spectroscopy, SEM, cyclic voltammetry, and DPV. Under optimized conditions, the GCE/ErGO-cMWCNT/AuNPs sensor exhibited a linear concentration range of 1.2–170 μM for HQ and CC, and 2.4–400 μM for RS with a detection limit of 0.39 μM, 0.54 μM, and 0.61 μM, respectively. When evaluated in tap water and skin-lightening cream, DHB multianalyte detection showed an average recovery rate of 107.11% and 102.56%, respectively. The performance was attributed to the synergistic effects of the 3D network formed by the strong π–π stacking interaction between ErGO and cMWCNT, combined with the active catalytic sites of AuNPs. Additionally, the cMWCNT provided improved electrocatalytic properties associated with the carboxyl groups that facilitate the adsorption of the DHB and the greater amount of active edge planes. The proposed GCE/ErGO-cMWCNT/AuNPs sensor showed a great potential for the simultaneous, precise, and easy-to-handle detection of DHB in complex samples with high sensitivity.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.

Potential applications of halophilic microorganisms for biological treatment of industrial process brines contaminated with aromatics

Saline wastewater contaminated with aromatic compounds can be frequently found in various industrial sectors. Those compounds need to be degraded before reuse of wastewater in other process steps or release to the environment. Halophiles have been reported to efficiently degrade aromatics, but their application to treat industrial wastewater is rare. Halophilic processes for industrial wastewater treatment need to satisfy certain requirements: a continuous process mode, low operational expenditures, suitable reactor systems and a monitoring and control strategy. The aim of this review is to provide an overview of halophilic microorganisms, principles of aromatic biodegradation, and sources of saline wastewater containing aromatics and other contaminants. Finally, process examples for halophilic wastewater treatment and potential process monitoring strategies are discussed. To further illustrate the significant potential of halophiles for saline wastewater treatment and to facilitate development of ready-to-implement processes, future research should focus on scale-up and innovative process monitoring and control strategies.

Designing of Nanomaterials-Based Enzymatic Biosensors: Synthesis, Properties, and Applications

Among the many biological entities employed in the development of biosensors, enzymes have attracted the most attention. Nanotechnology has been fostering excellent prospects in the development of enzymatic biosensors, since enzyme immobilization onto conductive nanostructures can improve characteristics that are crucial in biosensor transduction, such as surface-to-volume ratio, signal response, selectivity, sensitivity, conductivity, and biocatalytic activity, among others. These and other advantages of nanomaterial-based enzymatic biosensors are discussed in this work via the compilation of several reports on their applications in different industrial segments. To provide detailed insights into the state of the art of this technology, all the relevant concepts around the topic are discussed, including the properties of enzymes, the mechanisms involved in their immobilization, and the application of different enzyme-derived biosensors and nanomaterials. Finally, there is a discussion around the pressing challenges in this technology, which will be useful for guiding the development of future research in the area.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

An Overview of Optical and Electrochemical Sensors and Biosensors for Analysis of Antioxidants in Food during the Last 5 Years

Antioxidants are a group of healthy substances which are useful to human health because of their antihistaminic, anticancer, anti-inflammatory activity and inhibitory effect on the formation and the actions of reactive oxygen species. Generally, they are phenolic complexes present in plant-derived foods. Due to the valuable nutritional role of these mixtures, analysis and determining their amount in food is of particular importance. In recent years, many attempts have been made to supply uncomplicated, rapid, economical and user-friendly analytical approaches for the on-site detection and antioxidant capacity (AOC) determination of food antioxidants. In this regards, sensors and biosensors are regarded as favorable tools for antioxidant analysis because of their special features like high sensitivity, rapid detection time, ease of use, and ease of miniaturization. In this review, current five-year progresses in different types of optical and electrochemical sensors/biosensors for the analysis of antioxidants in foods are discussed and evaluated well. Moreover, advantages, limitations, and the potential for practical applications of each type of sensors/biosensors have been discussed. This review aims to prove how sensors/biosensors represent reliable alternatives to conventional methods for antioxidant analysis.

Internet of Nonthermal Food Processing Technologies (IoNTP): Food Industry 4.0 and Sustainability

With the introduction of Industry 4.0, and smart factories accordingly, there are new opportunities to implement elements of industry 4.0 in nonthermal processing. Moreover, with application of Internet of things (IoT), smart control of the process, big data optimization, as well as sustainable production and monitoring, there is a new era of Internet of nonthermal food processing technologies (IoNTP). Nonthermal technologies include high power ultrasound, pulsed electric fields, high voltage electrical discharge, high pressure processing, UV-LED, pulsed light, e-beam, and advanced thermal food processing techniques include microwave processing, ohmic heating and high-pressure homogenization. The aim of this review was to bring in front necessity to evaluate possibilities of implementing smart sensors, artificial intelligence (AI), big data, additive technologies with nonthermal technologies, with the possibility to create smart factories together with strong emphasis on sustainability. This paper brings an overview on digitalization, IoT, additive technologies (3D printing), cloud data storage and smart sensors including two SWOT analysis associated with IoNTPs and sustainability. It is of high importance to perform life cycle assessment (LCA), to quantify (En)—environmental dimension; (So)—social dimension and (Ec)—economic dimension. SWOT analysis showed: potential for energy saving during food processing; optimized overall environmental performance; lower manufacturing cost; development of eco-friendly products; higher level of health and safety during food processing and better work condition for workers. Nonthermal and advanced thermal technologies can be applied also as sustainable techniques working in line with the sustainable development goals (SDGs) and Agenda 2030 issued by United Nations (UN).

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug infusion in anaesthesiology. The fouling effect on electrodes discourages the possibility of continuous online monitoring of propofol since polymerization of the surface produces sensor drift. Several approaches have been proposed to limit the phenomenon at the biochemical interface; instead, here, we present a novel ML-based calibration procedure. In this paper, we analyse a dataset of 600 samples acquired through staircase cyclic voltammetry (SCV), resembling the scenario of continuous monitoring of propofol, both in PBS and in undiluted human serum, to demonstrate that ML-based model solves electrode fouling of anaesthetics. The proposed calibration approach is based on Gaussian radial basis function support vector classifier (RBF-SVC) that achieves classification accuracy of 98.9% in PBS, and 100% in undiluted human serum. The results prove the ability of the ML-based model to correctly classify propofol concentration in the therapeutic range between 1μM and 60μM with levels of 10μM, continuously up to ten minutes, with one sample every 30s.

Analytical performance of functional nanostructured biointerfaces for sensing phenolic compounds

Electrochemical biointerfaces are constructed with a wide range of nanomaterials and conducting polymers that strongly affect the analytical performance of biosensors. The analysis of progress toward electrochemical sensing platforms offers opportunities to provide devices for commercial use. The investigation of different methods for the synthesis of phenol biointerfaces leads to design challenges in the field of monitoring phenolic compounds. This paper review the innovative strategies and feature techniques in the construction of phenolic compound biosensors. The focus was made on the preparation methods of nanostructures and nanomaterials design for catalytic improvements of sensing interfaces. The paper also provides a comprehensive overview in the field of enzyme immobilization approaches at solid supports and technical formation of polymer nanocomposites, as well as applications of hybrid organic-inorganic nanocomposites in phenolic biosensors. This review also highlights the recent progress in the electrochemical detection of phenolic compounds and summarizes analytical performance parameters including sensitivity, storage stability, limit of detection, linear range, and Michaelis-Menten kinetic analysis. It also emphasizes advances from the past decade including technical challenges for the construction of suitable biointerfaces for monitoring phenolic compounds.

Multiwalled Carbon Nanotube-N-Doped Graphene/Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) Nanohybrid for Electrochemical Application in Intelligent Sensors and Supercapacitors

In this study, we reported the preparation of a conducting polymeric/inorganic nanohybrid consisting of multiwalled carbon nanotubes (MWCNT), N-doped graphene (NGr), and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), and its electrochemical application in intelligent sensors and supercapacitors. The multilayer thin film of the PEDOT:PSS-supported MWCNT-NGr nanohybrid was prepared by a facile layer-by-layer assembly strategy. The obtained conducting polymeric/inorganic nanohybrid modified electrode displayed superior electron transfer ability and a high specific surface area, which was used for electrochemical applications in intelligent sensors and supercapacitors. Remarkably, the fabricated amaranth sensor exhibited a broad linear range of 0.05-10 μM with a limit of detection of 0.015 μM under the optimal conditions. With the help of the response surface methodology, multivariate optimization was used as a substitute for the traditional single variable optimization to reflect the complete real effects of multivariate optimization in a sensing platform. Machine learning implemented by hybrid genetic algorithm-artificial neural network was used as an intelligent analysis model to replace the traditional regression analysis model for realizing intelligent analysis and output of sensing system. The MWCNT-NGr/PEDOT:PSS modified electrode exhibited a considerable specific capacitance of 6.5 mF cm^-2 at a current density of 2.0 mA cm^-2. The proposed results provided a new thought for a nanosensing platform equipped with a supercapacitor as a self-powered electrochemical energy storage system and machine learning as an intelligent analysis and output system in the near future.

Intensification of Dihydroxybenzenes Degradation over Immobilized TiO2 Based Photocatalysts under Simulated Solar Light

The work is focused on the assessment of possible methods for intensification of photocatalytic degradation of common water borne pollutants. Solar photocatalysis poses certain limitations for large scale application with several possible reactor designs which have shown an optimal performance. In the current study, a comparison between two types of pilot scale reactors was made: a flat-plate cascade reactor (FPCR) and tubular reactor with a compound parabolic collector (CPC). Apart from the reactor design, another aspect of possible intensification was a photocatalyst formulation. The efficiency of photocatalytic films that consisted of pure TiO2 nanoparticles was compared to the efficiency of films that consisted of TiO2/CNT composites. Intensification assessment was performed via detailed kinetic modelling, combining the optical properties of films, irradiation conditions and reactor mass balance. Intensification was expressed via intensification indices. Results showed the advantage of the CPC-based reactor design and an unbiased effect of sensitizing agent (CNT) in the photocatalytic film formulation.

Construction of a sensitive and specific lead biosensor using a genetically engineered bacterial system with a luciferase gene reporter controlled by pbr and cadA promoters

Background

A bacterial biosensor refers to genetically engineered bacteria that produce an assessable signal in the presence of a physical or chemical agent in the environment.

Methods

We have designed and evaluated a bacterial biosensor expressing a luciferase reporter gene controlled by pbr and cadA promoters in Cupriavidus metallidurans (previously termed Ralstonia metallidurans) containing the CH34 and pI258 plasmids of Staphylococcus aureus, respectively, and that can be used for the detection of heavy metals. In the present study, we have produced and evaluated biosensor plasmids designated pGL3-luc/pbr biosensor and pGL3-luc/cad biosensor, that were based on the expression of luc+ and under the control of the cad promoter and the cadC gene of S. aureus plasmid pI258 and pbr promoter and pbrR gene from plasmid pMOL30 of Cupriavidus metallidurans.

Results

We found that the pGL3-luc/pbr biosensor may be used to measure lead concentrations between 1–100 μM in the presence of other metals, including zinc, cadmium, tin and nickel. The latter metals did not result in any significant signal. The pGL3-luc/cad biosensor could detect lead concentrations between 10 nM to 10 μM.

Conclusions

This biosensor was found to be specific for measuring lead ions in both environmental and biological samples.

Polyphenol oxidase-based electrochemical biosensors: A review

The detection of phenolic compounds is relevant not only for their possible benefits to human health but also for their role as chemical pollutants, including as endocrine disruptors. The required monitoring of such compounds on-site or in field analysis can be performed with electrochemical biosensors made with polyphenol oxidases (PPO). In this review, we describe biosensors containing the oxidases tyrosinase and laccase, in addition to crude extracts and tissues from plants as enzyme sources. From the survey in the literature, we found that significant advances to obtain sensitive, robust biosensors arise from the synergy reached with a diversity of nanomaterials employed in the matrix. These nanomaterials are mostly metallic nanoparticles and carbon nanostructures, which offer a suitable environment to preserve the activity of the enzymes and enhance electron transport. Besides presenting a summary of contributions to electrochemical biosensors containing PPOs in the last five years, we discuss the trends and challenges to take these biosensors to the market, especially for biomedical applications.

Paralytic Shellfish Toxins (PST)-Transforming Enzymes: A Review

Paralytic shellfish toxins (PSTs) are a group of toxins that cause paralytic shellfish poisoning through blockage of voltage-gated sodium channels. PSTs are produced by prokaryotic freshwater cyanobacteria and eukaryotic marine dinoflagellates. Proliferation of toxic algae species can lead to harmful algal blooms, during which seafood accumulate high levels of PSTs, posing a health threat to consumers. The existence of PST-transforming enzymes was first remarked due to the divergence of PST profiles and concentrations between contaminated bivalves and toxigenic organisms. Later, several enzymes involved in PST transformation, synthesis and elimination have been identified. The knowledge of PST-transforming enzymes is necessary for understanding the processes of toxin accumulation and depuration in mollusk bivalves. Furthermore, PST-transforming enzymes facilitate the obtainment of pure analogues of toxins as in natural sources they are present in a mixture. Pure compounds are of interest for the development of drug candidates and as analytical reference materials. PST-transforming enzymes can also be employed for the development of analytical tools for toxin detection. This review summarizes the PST-transforming enzymes identified so far in living organisms from bacteria to humans, with special emphasis on bivalves, cyanobacteria and dinoflagellates, and discusses enzymes' biological functions and potential practical applications.

High-Density 2-μm-Pitch pH Image Sensor With High-Speed Operation up to 1933 fps

Various biosensing platforms for real-time monitoring and mapping of chemical signals in neural networks have been developed based on CMOS process technology. Despite their achievements, however, there remains a demand for an advanced method that can offer detailed insights into cellular functions with higher spatiotemporal resolution. Here, we present a pH image sensor that employs a high-density array of 256 × 256 pixels and readout circuitry designed for fast operation. The sensor's characteristics, such as the pH sensitivity of 55.1 mV/pH and higher frame speed of 1933 fps, are experimentally demonstrated and compared to those of state-of-the-art pH image sensors. Among them, our sensor presents the smallest pitch of 2 μm with a significantly high operation speed. This sensor can successfully detect a pH change, but also transform the measured data to a two-dimensional image series in real time. The practical spatial resolution of images is investigated by an evaluation method that we first propose in this paper. By this method, we confirm that our sensor can discriminate objects distanced over 4 μm apart, which is twice bigger than the pixel pitch. In order to analyze the degraded resolution and image blur, a capacitive coupling effect at an ion-sensitive membrane is suggested as the main factor and demonstrated by simulation.