A flow injection system based on chalcogenide glass sensors for the determination of heavy metals

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.

Advances in Sweat Wearables: Sample Extraction, Real-Time Biosensing, and Flexible Platforms

Wearable biosensors for sweat-based analysis are gaining wide attention due to their potential use in personal health monitoring. Flexible wearable devices enable sweat analysis at the molecular level, facilitating noninvasive monitoring of physiological states via real-time monitoring of chemical biomarkers. Advances in sweat extraction technology, real-time biosensors, stretchable materials, device integration, and wireless digital technologies have led to the development of wearable sweat-biosensing devices that are light, flexible, comfortable, aesthetic, affordable, and informative. Herein, we summarize recent advances of sweat wearables from the aspects of sweat extraction, fabrication of stretchable biomaterials, and design of biosensing modules to enable continuous biochemical monitoring, which are essential for a biosensing device. Key chemical components of sweat, sweat capture methodologies, and considerations of flexible substrates for integrating real-time biosensors with electronics to bring innovations in the art of wearables are elaborated. The strategies and challenges involved in improving the wearable biosensing performance and the perspectives for designing sweat-based wearable biosensing devices are discussed.

Heavy Metal/Toxins Detection Using Electronic Tongues

The growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of statistical representativeness to natural water samples. The accuracy of ANN results improves monotonically with the statistical representativeness of the training set (error decreases by ∼5×), while the expanded neural network architecture contributes a further factor of 2-3.5 decrease in error when trained with the most representative sample set. Results using the most statistically accurate set of training samples (which retain environmentally relevant ion concentrations but avoid the potential interference of humic acids) demonstrated accurate, unbiased quantification of nitrate and ammonium at natural environmental levels (±20% down to <10 μM), as well as the major ions Na(+), K(+), Ca(2+), Mg(2+), Cl(-), and SO4(2-), in unprocessed samples. These results show promise for the development of new in situ instrumentation for the support of scientific field work.

Miniaturized tools and devices for bioanalytical applications: an overview

This article presents an overview of various miniaturized devices and technologies developed by our group. Innovative, fast and cheap procedures for the fabrication of laboratory microsystems based on commercially available materials are reported and compared with well-established microfabrication techniques. The modules fabricated and tested in our laboratory can be used independently or they can be set up in different configurations to form functional measurement systems. We also report further applications of the presented modules e.g. disposable poly(dimethylsiloxane) (PDMS) microcuvettes, fibre optic detectors, potentiometric sensors platforms, microreactors and capillary electrophoresis (CE) microchips as well as integrated microsystems e.g. double detection microanalytical systems, devices for studying enzymatic reactions and a microsystem for cell culture and lysis.

Use of sequential injection analysis to construct an electronic-tongue

An electronic tongue based on the transient response of an array of non-specific-response potentiometric sensors was developed. A sequential injection analysis (SIA) system was used in order to automate its training and operation. The use of the transient recording entails the dynamic nature of the sensor's response, which can be of high information content, of primary ions and also of interfering ions; these may better discriminated if the kinetic resolution is added. This work presents the extraction of significant information contained in the transient response of a sensor array formed by five all-solid-state potentiometric sensors. The tool employed was the Fourier transform, from which a number of coefficients were fed into an artificial neural network (ANN) model, used to perform a quantitative multidetermination. The studied case was the analysis of mixtures of calcium, sodium and potassium. Obtained performance is compared with the more traditional automated electronic tongue using final steady-state potentials.

Electronic tongues in flow analysis

This review presents the evolution of recent flow-based analytical systems, characterized by the use of arrays of sensors as a detection scheme. For the proper processing of the complex responses generated, the systems require the use of advanced chemometric treatment, in which received the term "electronic tongue". Applications employing the flow injection analysis (FIA) and sequential injection analysis (SIA) are described. Chronologically, the progresses made by different research groups are shown, emphasizing their final applications in real problem solving.

Quantification of Ternary Mixtures of Heavy Metal Cations from Metallochromic Absorbance Spectra Using Neural Network Inversion

A new method based on artificial neural networks (ANN) for the processing of spectrophotometric data is proposed and illustrated on the example of the simultaneous quantification of ternary mixtures of zinc, cadmium, and mercury cations in aqueous solutions. Three types of commercially available metallochromic indicators were used as a simple model setup to create spectral data analogous to those normally received from an optical sensor array. In conventional ANN training methods for chemical sensors based on spectrophotometric data, a calibration is established by mathematically correlating the measured optical signal as network input with the concentration of the calibration sample as network output. In several situations, however, especially when dealing with mixed sample solutions, the relationship between a measured absorption spectrum and the corresponding ion concentrations is ambiguous, resulting in an "ill-posed problem". On the other hand, if the training direction is reversed by correlating known sample concentrations with measured optical signals, the relationship becomes reasonable for the ANN to obtain its structure. The proposed model illustrated in this paper is based on a more reasonable direct mapping and estimation by artificial neural network inversion (ANNI). In the training step, sample mixtures of known concentrations are optically measured to construct networks correlating the input data (ion concentrations) and the output data (absorption spectra). In the estimation step, the ion concentrations of unknown samples are estimated using the constructed ANN. The measured spectra of the unknown samples are fed to the output layer, and the appropriate input concentrations are determined by ANNI. When training the ANN system with 143 ternary mixtures of Zn2+, Cd2+, and Hg2+ in a concentration range from 1 to 100 microM, root-mean-square errors of prediction (RMSEP) of 0.45 (Zn2+), 0.96 (Cd2+), and 0.32 microM (Hg2+) were observed for the estimation of concentrations in 30 test samples, using the ANNI procedure. This newly proposed model, which involves the construction of an ANN based on direct mapping and estimation by ANNI, opens up one way to overcome the limitations of nonselective sensors, allowing the use of more easily accessible semiselective receptors to realize smart chemical sensing systems.