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The current socio-environmental and health problems that have arisen from the research of the presence of microplastics in the human body expose the need not only to reduce the use of plastics but to eliminate those that are already there. This project, which is developed by the Department of Micro Nano Technology of the UPC and the Superior Council of Scientific Research, tries to provide a solution in the elimination of microplastics in water. The proposed solution consists of a reusable device capable of filtering, detecting and eliminating microplastics. The device, manufactured in the clean room of the UPC, is based on interdigitated aluminum electrodes with a ceramic structure full of air voids deposited on top. In this work, the followed manufacturing process is explained and the impedance response of the device is characterized under the different manufacturing steps and in the detection of microplastics. The results obtained exhibit that the structure is able to trap and sense microplastics. La actual problemática socioambiental y de salubridad que ha surgido a partir del estudio de la presencia de microplásticos en el cuerpo humano, pone en evidencia la necesidad no sólo de reducir el uso de plásticos sino de eliminar los ya existentes. Este proyecto, desarrollado en el departamento de Micro y Nano Tecnología de la UPC junto con el Consejo Superior de Investigaciones Científicas, intenta dar una solución en la eliminación de microplásticos en el agua. La solución propuesta consiste en un dispositivo reutilizable capaz de filtrar, detectar y eliminar microplásticos. El dispositivo, fabricado en la sala blanca de la UPC, consiste en unos electrodos de aluminio con una estructura cerámica llena de huecos de aire depositada encima. En este trabajo se explica el proceso de fabricación seguido y se realiza la caracterización de la respuesta de impedancia del dispositivo en los distintos pasos de fabricación y en la detección de microplásticos. Los resultados obtenidos indican que la estructura es capaz de atrapar y percibir microplásticos. L'actual problemàtica socioambiental i de salubritat que ha sorgit a partir de l'estudi de la presència de microplàstics al cos humà, posa en evidència la necessitat no només de reduir l'ús de plàstics sinó d'eliminar els que ja hi ha. Aquest projecte, desenvolupat en el departament de Micro i Nano Tecnologia de la UPC juntament amb el Consell Superior d'Investigacions Científiques intenta donar una solució per a l'eliminació de microplàstics en l'aigua. La solució proposada consisteix en un dispositiu reutilitzable capaç de filtrar, detectar i eliminar microplàstics. El dispositiu, fabricat a la sala blanca de la UPC, consisteix en una estructura ceràmica plena de buits d'aire dipositada a sobre d'uns elèctrodes d'alumini. En aquest treball s'explica el procés de fabricació seguit i es realitza la caracterització de la resposta d'impedància del dispositiu en els diversos passos de fabricació i en la detecció de microplàstics. Els resultats obtinguts indiquen que l'estructura és capaç d'atrapar i percebre microplàstics.

The objective of this paper is to apply a closed-loop control based on dielectric excitation to MOX gas sensors in order to improve their response time. The control implements a feedback loop in which temperature modulations keep constant the sensor reactance, measured at constant temperature. The required fast temperature switching has been implemented on MEMS microhotplates. The mean temperature generated by the control is the new output signal. This technique is applied to an in-house sensor made of WO3 nanowires decorated with gold nanoparticles to detect NH3 and to a commercial MEMS MOX sensor (CCS801).

[ES] El trabajo realizado durante este proyecto se puede dividir en dos grandes bloques. El primero está dedicado a la síntesis y caracterización de materiales mesoporosos de óxido de silicio (MCM-41 nanoparticulada y MCM-41 microparticulada) los cuales están teniendo un gran éxito en el campo de los sensores químicos. El segundo bloque es el estudio del comportamiento de colorantes (Pireno, Rodamina B, etc.), como sensores frente a diferentes sustancias explosivas (nitroaromáticas y peroxídicas). La mayor parte de estos estudios de selectividad se realizaron en disolventes orgánicos o una mezcla de distintos disolventes. Este segundo bloque se puede dividir en dos partes según la liberación de colorante. La primera parte simula una liberación de colorante debido al desplazamiento de este por la presencia de explosivo. En este caso la polaridad tanto del explosivo (TATP) como del material, el disolvente y el colorante utilizado son parámetros importantes para el correcto funcionamiento. La segunda parte es una liberación controlada del colorante, la base de esta parte es la misma que la anterior con la diferencia de la utilización de puertas de pireno ancladas al material mesoporoso. La función de estas puertas es la de retener el colorante y dejarlo salir solo en presencia del explosivo, [EN] The work performed during this project is divided in two groups. The first one is dedicated to the synthesis and characterization of mesoporous silica materials (nanoparticle MCM-41 and microparticle MCM-41). These materials have a big success in the section of chemical sensors. The second block is the study of dyes (Pyrene, Rhodamine B, etc.) like a sensors for some explosives (nitroaromátic and peroxidic). Most of the selectivity studies had done in organic solvents or a mixture of different solvents. The second block can be divided into two parts according to the dye release. The first part simulates a dye release due to the displacement of the explosive presence. In this case the polarity of the explosive and material, solvent and the dye used is the most important parameters for correct operation. The second part is a controlled release of dye, being the fundament of this part the same as above. The difference is that in this release was used pyrene gates anchored to the mesoporous material. The function of these gates is to retain the dye and then release it only in presence of the explosive, [CA] El treball realitzat durant aquest projecte es pot dividir en dos grans blocs, el primer està dedicat a la síntesi i caracterització de materials mesoporosos d'òxid de silici (MCM-41 nanoparticulats i MCM-41 microparticulats) els quals tenen un gran èxit en el camp dels sensors químics. El segon bloc va estudiar el comportament de colorants (Piré, Rodamina B, etc.), com a sensors davant de diferents substàncies explosives (nitroaromátiques i peroxídiques). La major part d'aquest estudis de selectivitat es van realitzar en dissolvents orgànics o una mescla de distints dissolvents. Aquest segon bloc es pot dividir en dos parts segons l'alliberament del colorant. La primera part simula un alliberament de colorant a causa del desplaçament d’aquest pel explosiu. En aquest cas la polaritat tant del explosiu (TATP) com del material, el dissolvent, el colorant utilitzat són paràmetres més importants per al correcte funcionament. La segona part és un alliberament controlat del colorant, la base d'aquesta part és la mateixa que l'anterior amb la diferència de l´utilització de portes de piré ancorades al material mesoporoso. La funció d'aquestes portes és la de retenir el colorant i deixar ho eixir tan sols en presència de l'explosiu

Most mammals sniff to detect odors, but little is known how the periodic inhale and exhale that make up a sniff helps to improve odor detection. In this combined experimental and theoretical study, we use fluid mechanics and machine olfaction to rationalize the benefits of sniffing at different rates. We design and build a bellows and sensor system to detect the change in current as a function of odor concentration. A fast sniff enables quick odor recognition, but too fast a sniff makes the amplitude of the signal comparable to noise. A slow sniff increases signal amplitude but delays its transmission. This trade-off may inspire the design of future devices that can actively modulate their sniffing frequency according to different odors., Sniff frequency naturally varies with animal type due to allometric scaling. Using data from live animals and a machine olfactory system, Spencer et al. reveal a deeper reason for sniffing with implications for designing gas detectors: the sniff is adapted to efficient odor detection.

Metal oxide (MOx) electro-chemical gas sensors are a sensible choice for many applications, due to their tunable sensitivity, their space-efficiency and their low price. Publicly available sensor datasets streamline the development and evaluation of novel algorithm and circuit designs, making them particularly valuable for the Artificial Olfaction / Mobile Robot Olfaction community. In 2013, Vergara et al. published a dataset comprising 16 months of recordings from a large MOx gas sensor array in a wind tunnel, which has since become a standard benchmark in the field. Here we report a previously undetected property of the dataset that limits its suitability for gas classification studies. The analysis of individual measurement timestamps reveals that gases were recorded in temporally clustered batches. The consequential correlation between the sensor response before gas exposure and the time of recording is often sufficient to predict the gas used in a given trial. Even if compensated by zero-offset-subtraction, residual short-term drift contains enough information for gas classification. We have identified a minimally drift-affected subset of the data, which is suitable for gas classification benchmarking after zero-offset-subtraction, although gas classification performance was substantially lower than for the full dataset. We conclude that previous studies conducted with this dataset very likely overestimate the accuracy of gas classification results. For the 17 potentially affected publications, we urge the authors to re-evaluate the results in light of our findings. Our observations emphasize the need to thoroughly document gas sensing datasets, and proper validation before using them for the development of algorithms., Comment: 12 pages, 3 figures

Metal Oxide (MOX) gas sensors rely on chemical reactions that occur efficiently at high temperatures, resulting in too-demanding power requirements for certain applications. Operating the sensor under a Pulsed-Temperature Operation (PTO), by which the sensor heater is switched ON and OFF periodically, is a common practice to reduce the power consumption. However, the sensor performance is degraded as the OFF periods become larger. Other research works studied, generally, PTO schemes applying waveforms to the heater with time periods of seconds and duty cycles above 20%. Here, instead, we explore the behaviour of PTO sensors working under aggressive schemes, reaching power savings of 99% and beyond with respect to continuous heater stimulation. Using sensor sensitivity and the limit of detection, we evaluated four Ultra Low Power (ULP) sensors under different PTO schemes exposed to ammonia, ethylene, and acetaldehyde. Results show that it is possible to operate the sensors with total power consumption in the range of microwatts. Despite the aggressive power reduction, sensor sensitivity suffers only a moderate decline and the limit of detection may degrade up to a factor five. This is, however, gas-dependent and should be explored on a case-by-case basis since, for example, the same degradation has not been observed for ammonia. Finally, the run-in time, i.e., the time required to get a stable response immediately after switching on the sensor, increases when reducing the power consumption, from 10 minutes to values in the range of 10-20 hours for power consumptions smaller than 200 microwatts.

Calibration costs still limit the mass deployment of smart systems based on low-cost chemical sensors. Recent cost-efficient calibration strategies skip the acquisition of a full set of calibration samples when building new models. For example, calibration transfer and calibration update take advantage of models previously built using a reference system. Wireless sensor network calibration can also leverage the effects of intra-node correlations. In this chapter, I review such strategies together with public datasets that are valuable for benchmarking calibration methodologies for low-cost chemical sensors.

A novel paper-based potentiometric sensor with an enhanced response for the detection of glucose in biological fluids is presented. The electrode consists on platinum sputtered on a filter paper and a Nafion membrane to immobilize the enzyme glucose oxidase. The response obtained is proportional to the logarithm of the concentration of glucose, with a sensitivity of -119 +/- 8 mV.decade(-1), a linear range that spans from 10(-4)M to 10(-2.5) M and a limit of detection of 10(-4.5) M of glucose. It is shown that Nafion increases the sensitivity of the technique while minimizing interferences. Validation with human serum samples shows an excellent agreement when compared to standard methods. This approach can become an interesting alternative for the development of simple and affordable devices for point of care and home-based diagnostics.

DOI: 10.1016/j.snb.2016.07.182 URL: http://www.sciencedirect.com/science/article/pii/S0925400516310711 Filiació URV: SI Ultra-pure monolayer-capped gold nanoparticles for chemical gas sensing devices were prepared by a novel two-step process: a physical vapour deposition technique was first employed to make dispersed ultra-pure size-controlled gold nanoparticles, and this step was followed by a coating process for functionalization of the gold nanoparticles with an organic ligand, specifically dodecanethiol. X-ray photoelectron spectroscopy proved that the nano-assemblies had high purity. Chemical sensing devices based on these nano-assemblies showed Schottky-diode behaviour. We believe this is the first observation of Schottky-diodes fabricated from nanomaterials based on metallic nanoparticles. Gas sensing experiments demonstrated that these devices were suitable for detecting volatile organic compounds.

Soft sensors have increasingly gained attention in the chemical industry in recent years. In this work, a data-based soft sensor for on-line continuous distillate composition prediction of a dividing wall column is developed. This B. Sc. thesis has been written in english for BASF in Germany within the Erasmus+ Program. Outgoing

A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.

In this paper, we report the benefits of working with photonic molecules in macroporous silicon photonic crystals. In particular, we theoretically and experimentally demonstrate that the optical properties of a resonant peak produced by a single photonic atom of 2.6 µm wide can be sequentially improved if a second and a third cavity of the same length are introduced in the structure. As a consequence of that, the base of the peak is reduced from 500 nm to 100 nm, while its amplitude remains constant, increasing its Q-factor from its initial value of 25 up to 175. In addition, the bandgap is enlarged almost twice and the noise within it is mostly eliminated. In this study we also provide a way of reducing the amplitude of one or two peaks, depending whether we are in the two- or three-cavity case, by modifying the length of the involved photonic molecules so that the remainder can be used to measure gas by spectroscopic methods.

Gas sensing is nowadays a key actor in pollution observation and detection of chemical toxic agents or explosives. All these applications require the shortest possible time response. Very recently, a control of the surface potential in gas sensors based on metal oxides has experimentally shown to dramatically improve the time response of metal-oxide gas sensors. The proposed control is inspired in sigma-delta modulators. This paper aims at studying the resulting dynamics in the sensor from a theoretical point of view. Using state space models, it is shown how the state variables, namely the concentrations of ionized species in the sensing layer, evolve with time in open and closed loop configuration. This analysis studies how it is possible to alter the dynamics of the overall system, while at the same time keeping some important characteristics of sigma-delta modulators, such as quantization noise-shaping. Numerical simulations validate the obtained results.

Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models.

Polymeric sensors play an increasingly important role in monitoring the environment we live in, providing relevant information for a host of applications. Among them, significant efforts have been made to fabricate polymeric sensors useful for healthcare-related application fields, such as the sensitive detection of biomolecules and cellular interfacing. Within the well-established field of biomedical polymeric sensors, surface modification and/or functionalization using plasma is just emerging as a technology to improve selectivity and sensitivity in the biodetection process. Treatments based on plasma irradiation of polymer surfaces, which have been traditionally applied for cleaning, etching, activating or cross-linking, are currently being used to induce the formation of electrocatalytic species able to promote the oxidation of, for example, bioanalytes and/or gas molecules harmful for human health. Here, we summarize the main advances in the utilization of plasma technologies for the fabrication of polymeric sensors for advanced biomedical applications (e.g. humidity, temperature, pH, neurotransmitter, and glucose sensors).

"This is the peer reviewed version of the following article: Buendía JJ, Fabregat G, Castedo A, Llorca J, Alemán C. Plasma-treated polyethylene as electrochemical mediator for enzymatic glucose sensors: Toward bifunctional glucose and dopamine sensors. Plasma Process Polym. 2017;e1700133, which has been published in final form at https://doi.org/10.1002/ppap.201700133. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving The application of inert and insulating low density polyethylene (LDPE) in electrochemical detection is null. However, in a recent study it was found that reactive species formed onto the surface of plasma-treated LDPE and other polymers promote the electrocatalytic oxidation of dopamine. In this work, we examine the role of plasma-treated LDPE as mediator in enzymatic glucose biosensors based on Glucose oxidase and glass carbon substrate. Results indicate that plasma-induced changes facilitate the electrocommunication between the enzyme and the substrate. The chronoamperometric response of these sensors prove their bifunctionality since the oxidation of glucose to gluconolactone, which is catalyzed by the GOx, coexists with the oxidation of dopamine that is electrocatalytized by the plasma activated LDPE surface.

Review DOI: 10.3390/s17020391 URL: http://www.mdpi.com/1424-8220/17/2/391 Filiació URV: SI Memòria This paper presents a focused review on the nanomaterials and associated transduction schemes that have been developed for the selective detection of hydrogen sulfide. It presents a quite comprehensive overview of the latest developments, briefly discusses the hydrogen sulfide detection mechanisms, identifying the reasons for the selectivity (or lack of) observed experimentally. It critically reviews performance, shortcomings, and identifies missing or overlooked important aspects. It identifies the most mature/promising materials and approaches for achieving inexpensive hydrogen sulfide sensors that could be employed in widespread, miniaturized, and inexpensive detectors and, suggests what research should be undertaken for ensuring that requirements are met.

DOI: 10.1016/j.snb.2016.07.182 URL: http://www.sciencedirect.com/science/article/pii/S0925400516310711 Filiació URV: SI Ultra-pure monolayer-capped gold nanoparticles for chemical gas sensing devices were prepared by a novel two-step process: a physical vapour deposition technique was first employed to make dispersed ultra-pure size-controlled gold nanoparticles, and this step was followed by a coating process for functionalization of the gold nanoparticles with an organic ligand, specifically dodecanethiol. X-ray photoelectron spectroscopy proved that the nano-assemblies had high purity. Chemical sensing devices based on these nano-assemblies showed Schottky-diode behaviour. We believe this is the first observation of Schottky-diodes fabricated from nanomaterials based on metallic nanoparticles. Gas sensing experiments demonstrated that these devices were suitable for detecting volatile organic compounds.

This is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems, VOL 157, (2016) DOI 10.1016/j.chemolab.2016.07.004, A method for online decorrelation of chemical sensor signals from the e ects of envi- ronmental 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 humid- ity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor air for two years in the residence of one of the authors and it col- lected data continuously during 537 days with a sampling rate of 1 samples per second. To estimate the e ects 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 con rmed that the most statistically signi cant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coe cient of determination R2 close to 1. To show how the humidity-temperature correc- tion 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 ltered signal of the chemical sensors., This work has been supported by the California Institute for Telecommunications and Information Technology (CALIT2) under Grant Number 2014CSRO 136. JF acknowledges the support of the Marie Curie Actions and the Agency for Business Competitiveness of the Government of Catalonia (ACCI O) for the grant TECSPR15-1-0031; and the Spanish MINECO program, grant TEC2014-59229-R (SIGVOL). RH, TM, and IR-L acknowledge the partial support by 3a Convocatoria de Proyectos de Cooperacion Interuniversitaria UAM{Banco Santander con EEUU. NR would like to acknowledge partial support by ONR grant N000141612252. TM acknowledges CNPq grant 234817/2014- 3 for partial support. We are also thankful to Flavia Huerta who collected data examples during the summer of 2015.

The main existent tool to monitor chemical environ- ments in a continuous mode is gas sensor arrays, which have been popularized as electronic noses (enoses). To design and validate these monitoring systems, it is necessary to make use of machine learning techniques to deal with large amounts of heterogeneous data and extract useful information from them. Therefore, enose data present several challenges for each of the steps involved in the design of a machine learning system. Some of the machine learning tasks involved in this area of research include generation of operational patterns, detection anomalies, or classification and discrimination of events. In this work, we will review some of the machine learning approaches adopted in the literature for enose data analysis, and their application to three different tasks: single gas classification under tightly-controlled operating conditions, gas binary mixtures classification in a wind tunnel with two independent gas sources, and human activity monitoring in a NASA spacecraft cabin simulator.

The objective of this letter is to show the first results obtained with a control designed to keep the average surface potential of a nanostructured layerconstant. This condition is equivalent to keeping constant the resistivity of the layer measured at a constant reference temperature. The proposed closedloop control achieves this objective by adequately changing the average temperature of the temperature waveforms applied to the nanostructured layer. Experiments are shown on which the control is applied to a layer made of Au-functionalized WO3 nanoneedles.

Stabilization of metal nanoparticles (MNPs) in polymeric matrices of different types has proven to be one of the most promising strategies to prevent their aggregation and to retain their properties. Polymer-stabilized MNPs (PSMNPs) and those based on polymer-metal nanocomposite materials are starting to find wide application in various fields of science and technology. In this paper, we demonstrate that metal-polymer nanocomposite membranes (MPNCMs) containing MNPs can easily be prepared in an ion-exchange such as, for example, sulfonated polyetherether ketone (SPEEK) matrix by using the polymeric membranes as nanoreactors for synthesis and to characterize the composition and structure of the formed MNPs. Metal ions (or metal ion complexes) are first incorporated into the polymeric matrix where they undergo reduction, leading to formation of corresponding MPNCMs. Since this technique allows successive metal loading-reduction cycles to be carried out, it enables synthesis of both monometallic and bimetallic (e.g., core-shell) MNPs. The proposed approach is illustrated by synthesis and characterization of MPNCMs containing both monometallic and bimetallic core-shell MNPs, formed by combinations of Pd, Pt, Co, Ni, and Cu, along with their application in electrochemical sensor and biosensor constructions.