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Describes the basic principles of biosensors and the history of their development. Assesses their current impact and state of the biosensor market, and looks ahead to future developments.

Much progress has been made in the past few years in improving the corrosion‐resistance of decorative nickel‐chromium‐plated articles, especially those subjected to outdoor exposure. There now exists a wide range of alternative finishes, all of which are alleged to improve the corrosion‐resistance to some extent. To evaluate these finishes completely, manufacturers are required to spend much time, money and energy. Manufacturers generally wish to improve their standard of corrosion‐resistance of decorative deposits with no increase in cost, and with some of the more complex systems for corrosion protection this is now possible by an overall reduction in nickel thickness.

Molecular imprinting is a generic technology, which introduces recognition properties into synthetic polymers using appropriate templates. Over the last two decades molecularly imprinted polymers (MIPs) have become a focus of interest for scientists engaged in the development of biological and chemical sensors. This is due to the many and considerable advantages they possess in comparison to natural receptors, enzymes and antibodies such as superior stability, low cost and ease of preparation. This brief review covers recent achievements and potential applications of imprinted sensors with specific reference to the environment and biotechnology.

The present study aims to summarize different non-invasive techniques for continuous glucose monitoring (CGM) in diabetic patients using glucose-oxidase biosensors. In diabetic patients, the self-monitoring of blood glucose (BG) levels through minimally invasive techniques provides a quick method of measuring their BG concentration, unlike conventional laboratory measurements. The drawbacks of minimally invasive techniques include physical pain, anxiety and reduced patient compliance. To overcome these limitations, researchers shifted their attention towards the development of a pain-free and non-invasive glucose monitoring system, which showed encouraging results.

This study reviews the development of minimally and non-invasive method for continuous glucose level monitoring in diabetic or hyperglycemic patients. Specifically, glucose monitoring using non-invasive techniques, such as spectroscopy-based methods, polarimetry, fluorescence, electromagnetic variations, transdermal extraction-based methods and using body fluids, has been discussed. The various strategies adopted for improving the overall specificity and performance of biosensors are discussed.

In conclusion, the technology of glucose oxidase-based biosensors for glucose level monitoring is becoming a strong competitor, probably because of high specificity and selectivity, low cost and increased patient compliance. Many industries currently working in this field include Google, Novartis and Microsoft, which demonstrates the significance and strong market potential of self-monitored glucose-oxidase-based biosensors in the near future.

This review paper summarizes comprehensive strategies for continuous glucose monitoring (CGM) in diabetic patients using non-invasive glucose-oxidase biosensors. Non-invasive techniques received significant research interest because of high sensitivity and better patient compliance, unlike invasive ones. Although the results from these innovative devices require frequent calibration against direct BG data, they might be a preferable candidate for future CGM. However, the challenges associated with designing accurate level sensors to biomonitor BG data easily and painlessly needs to be addressed.

Molecularly imprinted polymers (MIPs) have aroused focus in medicinal chemistry in recent decades, especially for biomedical applications. Considering the exceptional abilities to immobilize any guest of medical interest (antibodies, enzymes, etc.), MIPs is attractive to substantial research efforts in complementing the quest of biomimetic recognition systems. This study aims to review the key-concepts of molecular imprinting, particularly emphasizes on the conformational adaptability of MIPs beyond the usual description of molecular recognition. The optimal morphological integrity was also outlined in this review to acknowledge the successful sensing activities by MIPs.

This review highlighted the fundamental mechanisms and underlying challenges of MIPs from the preparation stage to sensor applications. The progress of electrochemical and optical sensing using molecularly imprinted assays has also been furnished, with the evolvement of molecular imprinting as a research hotspot.

The lack of standard synthesis protocol has brought about an intriguing open question in the selection of building blocks that are biocompatible to the imprint species of medical interest. Thus, in this paper, the shortcomings associated with the applications of MIPs in electrochemical and optical sensing were addressed using the existing literature besides pointing out possible solutions. Future perspectives in the vast development of MIPs also been postulated in this paper.

The present review intends to furnish the underlying mechanisms of MIPs in biomedical diagnostics, with the aim in electrochemical and optical sensing while hypothesizing on future possibilities.

This paper aims to give an overview about the state of the art and novel technologies used in gas sensing. It also discusses the miniaturization potential of some of these technologies in a comparative way.

In this article, the authors state the most of the methods used in gas sensing discuss their advantages and disadvantages and at last the authors discuss the ability of their miniaturization comparing between them in terms of their sensing parameters like sensitivity, selectivity and cost.

In this article, the authors will try to cover most of the important methods used in gas sensing and their recent developments. The authors will also discuss their miniaturization potential trying to find the best candidate among the different types for the aim of miniaturization.

In this article, the authors will review most of the methods used in gas sensing and discuss their miniaturization potential delimiting the research to a certain type of technology or application.

In this paper, hybrid multienzyme biosensor system, which detects analyte through molecular conversion into signal response, is analyzed and presented. The biokinetic effects of pertinent parameters such as Michaelis–Menten constant, inhibitor inhibition and substrate inhibition modulus on biochemical reactions are investigated.

Biochemical reaction models are described by five nonlinear equations for bisubstrate amperometric system analyzed adopting the regular perturbation method.

Results obtained reveal that increasing Michaelis–Menten constant of oxygen causes a significant decrease in hydrogen peroxide concentration while increasing Michaelis–Menten constant of glucose shows increasing effect on oxygen concentration. Hence, results obtained from this work serve as reference for further analysis of concentration models and offer useful insight to relevant applications such as food safety, environmental and biomedical applications.

This work serves as reference for further analysis of concentration models and offers useful insight to relevant applications such as food safety, environmental and biomedical applications.

This paper examines the effect of biokinetic parameters on the concentration of the hybrid multienzyme biosensor. Here the effects of parameters such as inhibitor inhibition, substrate inhibition and Michealis–Menten were investigated on substrate, inhibition and product concentrations. It is illustrated from result that inhibitor parameter slows enzymatic catalytic reaction while substrate enhances reaction. This study applied approximate analytical scheme to investigate the biokinetic effects, adopting the regular perturbation scheme.

Sensors have gained a wide theoretical interest and practical application in biomedicine. They can be used for very different purposes and may offer unique possibilities. The paper gives a broad summary about the biomedical sensor application fields and about their technologies. Finally, the present status and perspectives are summarised.

Discusses intelligent materials, intelligent material‐based sensors, their transducing methods, and different kinds of transducers used with smart material‐based sensors. Assumes that the future of intelligent sensors will almost totally depend on intelligent chemistry and intelligent instrumentation. Molecular recognition will widen the horizons of smart systems with the help of VLSI‐based design and fabrication. Discusses different sensor mechanisms, such as ENFETs, immunoFETs, ISFETs and chemFETs and takes a detailed look at potentiometric, amperometric and optical biosensors.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.