Publications

Polypropylene (PP), thermoplastic polyurethane (TPU), polyethylene terephthalate glycol (PETG) and polylactic acid (PLA) 3D printed specimens, which are intrinsically non-electroresponsive materials, have been converted into electroresponsive electrodes applying a low-pressure oxygen plasma treatment. After complete chemical, morphological and electrochemical characterization, plasma treated samples have been applied as integrated electrochemical sensors for detecting dopamine and serotonin by cyclic voltammetry and chronoamperometry. Results show differences in the sensing behavior, which have been explained on the basis of the chemical structure of the pristine materials. While plasma treated PLA exhibits the highest performance as electrochemical sensor in terms of sensitivity (lowest limits of detection and quantification) and selectivity (against uric acid and ascorbic acid as interfering substances), plasma treated PP displays the poorest behavior due to its low polarity compared to PLA 3D-printed electrodes. Instead, plasma treated TPU and PETG shows a very good response, much closer to PLA, as sensitive electrodes towards neurotransmitter molecules (dopamine and serotonin). Overall, results open a new door for the fabrication of electrochemical conductive sensors using intrinsically insulating materials, without the need of chemical functionalization processes.

JTD Keywords: 3d printing, Amines, Ascorbic acid, Chemical characterization, Cyclic voltammetry, Dopamine, Electrochemical characterizations, Electrochemical sensor s, Electrochemical sensors, Electrode materials, Electroresponsive materials, Low-pressure oxygen-plasma treatments, Morphological characterization, Multiwalled carbon nanotubes (mwcn), Neurophysiology, Oxygen, Oxygen plasmas, Plastic bottles, Polyethylene terephthalate glycol, Polyethylene terephthalate glycols, Polyethylene terephthalates, Polylact i c acid, Polylactic acid, Polylactic acid pla, Polyols, Polypropylene, Polypropylene oxides, Polypropylenes, Polyurethanes, Reinforced plastics, Supercapacitors, Thermoplast i c polyurethane, Thermoplastic polyurethane, Thermoplastic polyurethanes

Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.Copyright © 2022. Published by Elsevier B.V.

JTD Keywords: calibration, chemical sensors, drone, dynamic olfactometry, electronic nose, odourquantification, olfaction, volatile organic-compounds, wwtp, Calibration, Chemical sensors, Drone, Dynamic olfactometry, Electronic nose, Environmental monitoring, Odour quantification, Olfaction, Variable selection methods, Wwtp

Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

Perinatal asphyxia is a major cause of severe brain damage and death. For its prenatal identification, Doppler ultrasound has been used as a surrogate marker of fetal hypoxia. However, Doppler evaluation cannot be performed continuously. We have evaluated the performance of a miniaturized multiparametric sensor aiming to evaluate tissular oxygen and pH changes continuously in an umbilical cord occlusion (UCO) sheep model. The electrochemical sensors were inserted in fetal hindlimb skeletal muscle and electrochemical signals were recorded. Fetal hemodynamic changes and metabolic status were also monitored during the experiment. Additionally, histological assessment of the tissue surrounding the sensors was performed. Both electrochemical sensors detected the pO2 and pH changes induced by the UCO and these changes were correlated with hemodynamic parameters as well as with pH and oxygen content in the blood. Finally, histological assessment revealed no signs of alteration on the same day of insertion. This study provides the first evidence showing the application of miniaturized multiparametric electrochemical sensors detecting changes in oxygen and pH in skeletal muscular tissue in a fetal sheep model.

JTD Keywords: continuous monitoring of acid-base status, diagnosis, doppler, electrochemical sensors, growth restriction, high-risk pregnancies, human-fetus, management, responses, tissue ph, Continuous monitoring of acid-base status, Electrochemical sensors, High-risk pregnancies, Umbilical cord occlusion, Umbilical-cord occlusion

Flexible electrochemical sensors based on electroactive materials have emerged as powerful analytical tools for biomedical applications requiring bioanalytes detection. Within this context, 3D printing is a remarkable technology for developing electrochemical devices, due to no design constraints, waste minimization, and batch manufacturing with high reproducibility. However, the fabrication of 3D printed electrodes is still limited by the in-house fabrication of conductive filaments, which requires the mixture of the electroactive material with melted of thermoplastic polymer (e.g., polylactic acid, PLA). Herein, a simple approach is presented for preparing electrochemical dopamine (DA) biosensors. Specifically, the surface of 3D-printed PLA specimens, which exhibit an elastic modulus and a tensile strength of 3.7 +/- 0.3 GPa and 47 +/- 1 MPa, respectively, is activated applying a 0.5 m NaOH solution for 30 min and, subsequently, poly(3,4-ethylenedioxythiophene) is polymerized in situ using aqueous solvent. The detection of DA with the produced sensors has been demonstrated by cyclic voltammetry, differential pulse voltammetry, and chronoamperometry. In summary, the obtained results reflect that low-cost electrochemical sensors, which are widely used in medicine and biotechnology, can be rapidly fabricated using the proposed approach that, although based on additive manufacturing, does not require the preparation of conductive filaments.

JTD Keywords: 3d printers, Additive manufacturing, Amines, Batch manufacturing, Biomedical applications, Chronoamperometry, Conducting polymer, Conducting polymers, Conductive filaments, Conservation, Cyclic voltammetry, Differential pulse voltammetry, Electroactive material, Electrochemical biosensor, Electrochemical devices, Electrochemical sensors, Electrodes, Electron emission, Flexible electrode, High reproducibility, Medical applications, Neurophysiology, Poly-3 ,4-ethylenedioxythiophene, Polyesters, Polylactic aci, Sodium hydroxide, Tensile strength, Thermoplastic polymer

© 2020 Wiley-VCH GmbH Conducting polymers have been increasingly used as biologically interfacing electrodes for biomedical applications due to their excellent and fast electrochemical response, reversible doping–dedoping characteristics, high stability, easy processability, and biocompatibility. These advantageous properties can be used for the rapid detection and eradication of infections associated to bacterial growth since these are a tremendous burden for individual patients as well as the global healthcare system. Herein, a smart nanotheranostic electroresponsive platform, which consists of chloramphenicol (CAM)-loaded in poly(3,4-ethylendioxythiophene) nanoparticles (PEDOT/CAM NPs) for concurrent release of the antibiotic and real-time monitoring of bacterial growth is presented. PEDOT/CAM NPs, with an antibiotic loading content of 11.9 ± 1.3% w/w, are proved to inhibit the growth of Escherichia coli and Streptococcus sanguinis due to the antibiotic release by cyclic voltammetry. Furthermore, in situ monitoring of bacterial activity is achieved through the electrochemical detection of β-nicotinamide adenine dinucleotide, a redox active specie produced by the microbial metabolism that diffuse to the extracellular medium. According to these results, the proposed nanotheranostic platform has great potential for real-time monitoring of the response of bacteria to the released antibiotic, contributing to the evolution of the personalized medicine.

JTD Keywords: bacterial detection, chloramphenicol, conducting polymers, drug, drug release, electrochemical sensors, electrochemistry, electrostimulated release, mechanism, peptide, polythiophene, sensor, sulfonate, Bacterial detection, Chloramphenicol, Conducting polymers, Controlled-release, Drug release, Electrochemical sensors, Electrostimulated release, Polythiophene

© 2021, The Author(s). Background: One of the most prevalent causes of fetal hypoxia leading to stillbirth is placental insufficiency. Hemodynamic changes evaluated with Doppler ultrasound have been used as a surrogate marker of fetal hypoxia. However, Doppler evaluation cannot be performed continuously. As a first step, the present work aimed to evaluate the performance of miniaturized electrochemical sensors in the continuous monitoring of oxygen and pH changes in a model of acute hypoxia-acidosis. Methods: pH and oxygen electrochemical sensors were evaluated in a ventilatory hypoxia rabbit model. The ventilator hypoxia protocol included 3 differential phases: basal (100% FiO2), the hypoxia-acidosis period (10% FiO2) and recovery (100% FiO2). Sensors were tested in blood tissue (ex vivo sensing) and in muscular tissue (in vivo sensing). pH electrochemical and oxygen sensors were evaluated on the day of insertion (short-term evaluation) and pH electrochemical sensors were also tested after 5 days of insertion (long-term evaluation). pH and oxygen sensing were registered throughout the ventilatory hypoxia protocol (basal, hypoxia-acidosis, and recovery) and were compared with blood gas metabolites results from carotid artery catheterization (obtained with the EPOC blood analyzer). Finally, histological assessment was performed on the sensor insertion site. One-way ANOVA was used for the analysis of the evolution of acid-based metabolites and electrochemical sensor signaling results; a t-test was used for pre- and post-calibration analyses; and chi-square analyses for categorical variables. Results: At the short-term evaluation, both the pH and oxygen electrochemical sensors distinguished the basal and hypoxia-acidosis periods in both the in vivo and ex vivo sensing. However, only the ex vivo sensing detected the recovery period. In the long-term evaluation, the pH electrochemical sensor signal seemed to lose sensibility. Finally, histological assessment revealed no signs of alteration on the day of evaluation (short-term), whereas in the long-term evaluation a sub-acute inflammatory reaction adjacent to the implantation site was detected. Conclusions: Miniaturized electrochemical sensors represent a new generation of tools for the continuous monitoring of hypoxia-acidosis, which is especially indicated in high-risk pregnancies. Further studies including more tissue-compatible material would be required in order to improve long-term electrochemical sensing.

JTD Keywords: acute hypoxia-acidosis, continuous monitoring of acid-base status, continuous monitoring of acid–base status, electrochemical sensors, high-risk pregnancies, Acute hypoxia-acidosis, Continuous monitoring of acid–base status, Electrochemical sensors, High-risk pregnancies

© 2020 Elsevier B.V. Semi-interpenetrated nanogels (NGs) able to release and sense diclofenac (DIC) have been designed to act as photothermal agents with the possibility to ablate cancer cells using mild-temperatures (<45 °C). Combining mild heat treatments with simultaneous chemotherapy appears as a very promising therapeutic strategy to avoid heat resistance or damaging the surrounding tissues. Particularly, NGs consisted on a poly(N-isopropylacrylamide) (PNIPAM) and dendritic polyglycerol (dPG) mesh containing a semi-interpenetrating network (SIPN) of poly(hydroxymethyl 3,4-ethylenedioxythiophene) (PHMeEDOT). The PHMeEDOT acted as photothermal and conducting agent, while PNIPAM-dPG NG provided thermoresponsivity and acted as stabilizer. We studied how semi-interpenetration modified the physicochemical characteristics of the thermoresponsive SIPN NGs and selected the best condition to generate a multifunctional photothermal agent. The thermoswitchable conductiveness of the multifunctional NGs and the redox activity of DIC could be utilized for its electrochemical detection. Besides, as proof of the therapeutic concept, we investigated the combinatorial effect of photothermal therapy (PTT) and DIC treatment using the HeLa cancer cell line in vitro. Within 15 min NIR irradiation without surpassing 45 °C we were able to kill 95% of the cells, demonstrating the potential of SIPN NGs as drug carriers, sensors and agents for mild PTT.

JTD Keywords: cells, cellulose, conducting polymers, controlled delivery, diclofenac, efficiency, electrochemical oxidation, electrochemical sensors, nanogels, nanoparticles, photothermal therapy, pnipam, poly(3,4-ethylenedioxythiophene), Conducting polymers, Electrochemical sensors, Nanogels, Photothermal therapy

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

JTD Keywords: Unmanned aircraft systems, Remotely piloted aircraft systems, Chemical sensors, Gas sensors, Environmental monitoring, Industrial emission monitoring

The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.

JTD Keywords: Gas detectors, Chemical sensors, Signal processing, Machine learning, Time series analysis

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

JTD Keywords: Machine olfaction, Odor robots, Chemical sensors, Bayesian inference

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on metal oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of ethanol, ethylene, carbon monoxide, or methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available.

JTD Keywords: Calibration transfer, Chemical sensors, Direct Standardization, Electronic nose, MOX sensors, Public dataset

A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R2 close to 1. To show how the humidity–temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors.

JTD Keywords: Electronic nose, Chemical sensors, Humidity, Temperature, Decorrelation, Wireless e-nose, MOX sensors, Energy band model, Home monitoring

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

JTD Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing

Poly(vinylchloride) (PVC) is the most common polymer matrix used in the fabrication of ion-selective electrodes (ISEs). However, the surfaces of PVC-based sensors have been reported to show membrane instability. In an attempt to overcome this limitation, here we developed two alternative methods for the preparation of highly stable and robust ion-selective sensors. These platforms are based on the selective electropolymerization of poly(3,4-ethylenedioxythiophene) (PEDOT), where the sulfur atoms contained in the polymer covalently interact with the gold electrode, also permitting controlled selective attachment on a miniaturized electrode in an array format. This platform sensor was improved with the crosslinking of the membrane compounds with poly(ethyleneglycol) diglycidyl ether (PEG), thus also increasing the biocompatibility of the sensor. The resulting ISE membranes showed faster signal stabilization of the sensor response compared with that of the PVC matrix and also better reproducibility and stability, thus making these platforms highly suitable candidates for the manufacture of robust implantable sensors.

JTD Keywords: Biomedicine, Electrochemistry, Endoscope, Implantable device, Ion-selective electrode (ISE) sensor, Ischemia, pH detection, Biocompatibility, Chemical sensors, Electrochemistry, Electrodes, Electropolymerization, Endoscopy, Functional polymers, Implants (surgical), Ion selective electrodes, Medical applications, Polyvinyl chlorides, Stabilization, Biomedical applications, Biomedicine, Implantable devices, Ion selective sensors, Ischemia, Membrane instability, pH detection, Poly(3 ,4 ethylenedioxythiophene) (PEDOT), Ion selective membranes

Here we have developed a simple method for the fabrication of disposable implantable all-solid-state ion-selective electrodes (ISE) in an array format without using complex fabrication equipment or clean room facilities. The electrodes were designed in a needle shape instead of planar electrodes for a full contact with the tissue. The needle-shape platform comprises 12 metallic pins which were functionalized with conductive inks and ISE membranes. The modified microelectrodes were characterized with cyclic voltammetry, scanning electron microscope (SEM), and optical interferometry. The surface area and roughness factor of each microelectrode were determined and reproducible values were obtained for all the microelectrodes on the array. In this work, the microelectrodes were modified with membranes for the detection of pH and nitrate ions to prove the reliability of the fabricated sensor array platform adapted to an endoscope.

JTD Keywords: Chemical sensors, Cyclic voltammetry, Electrochemistry, Endoscopy, Fabrication, Implants (surgical), Microelectrodes, Needles, Nitrates, Scanning electron microscopy, Biomedicine, Fabricated sensors, Fabrication equipment, Implantable devices, Implantable sensors, Optical interferometry, Planar electrode, Roughness factor, Ion selective electrodes

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. EU Funded Project NEUROCHEM (Bio-ICT-FET- 216916) has developed novel computing paradigms and biologically motivated artefacts for chemical sensing taking inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built featuring a large scale sensor array (65K elements) in conducting polymer technology mimicking the olfactory receptor neuron layer, and abstracted biomimetic algorithms have been implemented in an embedded system that interfaces the chemical sensors. The embedded system integrates computational models of the main anatomic building blocks in the olfactory pathway: The olfactory bulb, and olfactory cortex in vertebrates (alternatively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions. Finally, the algorithmic models are tested with an odour robot with navigation capabilities in mixed chemical plumes.

JTD Keywords: Antennal lobes, Artificial olfaction, Computational neuroscience, Olfactory bulbs, Plume tracking, Abstracting, Actuators, Algorithms, Biomimetic processes, Chemical sensors, Conducting polymers, Data processing, Flavors, Odors, Robots, Smart sensors, Embedded systems

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.

JTD Keywords: Chemical sensors, Electronic nose, Intelligent sensors, Measurement techniques, Sensor arrays, Sensor systems

The authors are working toward the design of a device for the detection of oxygen, following a discrete and an integrated instrumentation implementation. The discrete electronics are also used for preliminary analysis, to confirm the validity of the conception of system, and its set-up would be used in the characterization of the integrated device, waiting for the chip fabrication. This paper presents the design of a small and portable potentiostat integrated with electrodes, which is cheap and miniaturized, which can be applied for on-site measurements for the simultaneous detection of O/sub 2/ and temperature in water systems. As a first approach a discrete PCB has been designed based on commercial discrete electronics and specific oxygen sensors. Dissolved oxygen concentration (DO) is an important index of water quality and the ability to measure the oxygen concentration and temperature at different positions and depths would be an important attribute to environmental analysis. Especially, the objective is that the sensor and the electronics can be integrated in a single encapsulated device able to be submerged in environmental water systems and be able to make multiple measurements. For our proposed application a small and portable device is developed, where electronics and sensors are miniaturized and placed in close proximity to each other. This system would be based on the sensors and electronics, forming one module, and connected to a portable notebook to save and analyze the measurements on-line. The key electronics is defined by the potentiostat amplifier, used to fix the voltage between the working (WE) and reference (RE) electrodes following an input voltage (Vin). Vin is a triangular signal, programmed by a LabView/sup c / interface, which is also used to represent the CV transfers. To obtain a smaller and compact solution the potentiostat amplifier has also been integrated defining a full custom ASIC amplifier, which is in progress, looking for a point-of-care device. These circuits have been designed with a 0.13 mu m technology from ST Microelectronics through the CMP-TIMA service.

JTD Keywords: Amplifiers, Application specific integrated circuits, Chemical sensors, Electrodes, Portable instruments, Temperature measurement, Water sources

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage process for sea bream (Sparus Aurata) is explored. It is shown that a single sensor under temperature modulation is able to find a correlation with the fish spoilage process

JTD Keywords: Gas sensors, Electrochemical sensors, Chromatography

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. In this work, the state of the art concerning biologically inspired computation for chemical sensing will be reviewed. Instead of reviewing the whole body of computational neuroscience of olfaction, we restrict this review to the application of models to the processing of real chemical sensor data.

JTD Keywords: Computational Intelligence, Chemical Sensors