Sensores másicos para la detección de agentes de guerra química y biológica

Tesis doctoral inédita, leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física Aplicada. Fecha de lectura: 13-07-201

Sensors and systems for environmental monitoring and control

Editorial titulado Sensors and systems for environmental monitoring and control, publicado en la Journal of sensors, en 2017. Se analiza el valor de controlar con monitores especializados los contaminantes de origen químico orgánico e inorgánico.Editorial entitled Sensors and systems for environmental monitoring and control, published in the Journal of sensors, in 2017. The value of controlling pollutants of organic and inorganic chemical origin with specialized monitors is analyzed.peerReviewe

Nanocrystalline tin oxide nanofibers deposited by a novel focused electrospinning method. Application to the detection of TATP precursors

A new method of depositing tin dioxide nanofibers in order to develop chemical sensors is presented. It involves an electrospinning process with in-plane electrostatic focusing over micromechanized substrates. It is a fast and reproducible method. After an annealing process, which can be performed by the substrate heaters, it is observed that the fibers are intertwined forming porous networks that are randomly distributed on the substrate. The fiber diameters oscillate from 100 nm to 200 nm and fiber lengths reach several tens of microns. Each fiber has a polycrystalline structure with multiple nano-grains. The sensors have been tested for the detection of acetone and hydrogen peroxide (precursors of the explosive triacetone triperoxide, TATP) in air in the ppm range. High and fast responses to these gases have been obtained. © 2014 by the authors; licensee MDPI, Basel, Switzerland.This work has been supported by the Spanish Science and Innovation Ministry under the projects TEC2010-21357-C05-04 and TEC2013-48147-C6-4-R. Authors want to thank University of Extremadura for SEM and XRD analysis. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)Peer Reviewe

Love-Wave Sensors Combined with Microfluidics for Fast Detection of Biological Warfare Agents

The following paper examines a time-efficient method for detecting biological warfare agents (BWAs). The method is based on a system of a Love-wave immunosensor combined with a microfluidic chip which detects BWA samples in a dynamic mode. In this way a continuous flow-through of the sample is created, promoting the reaction between antigen and antibody and allowing a fast detection of the BWAs. In order to prove this method, static and dynamic modes have been simulated and different concentrations of BWA simulants have been tested with two immunoreactions: phage M13 has been detected using the mouse monoclonal antibody anti-M13 (AM13), and the rabbit immunoglobulin (Rabbit IgG) has been detected using the polyclonal antibody goat anti-rabbit (GAR). Finally, different concentrations of each BWA simulants have been detected with a fast response time and a desirable level of discrimination among them has been achieved.This work was supported by the Spanish Science and Innovation Ministry under the project TEC2010-21357-C05-04, and a postdoctoral fellowship at the National Autonomous University of Mexico.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)Peer reviewe

Optimization of multilayer graphene-based gas sensors by ultraviolet photoactivation

Nitrogen dioxide (NO2) is a potential hazard to human health at low concentrations, below one part per million (ppm). NO2 can be monitored using gas sensors based on multi-layered graphene operating at ambient temperature. However, reliable detection of concentrations on the order of parts per million and lower is hindered by partial recovery and lack of reproducibility of the sensors after exposure. We show how to overcome these longstanding problems using ultraviolet (UV) light. When exposed to NO2, the sensor response is enhanced by 290 % − 550 % under a 275 nm wavelength light emitting diode irradiation. Furthermore, the sensor’s initial state is completely restored after exposure to the target gas. UV irradiation at 68 W/m2 reduces the NO2 detection limit to 30 parts per billion (ppb) at room temperature. We investigated sensor performance optimization for UV irradiation with different power densities and target gases, such as carbon oxide and ammonia. Improved sensitivity, recovery, and reproducibility of UV-assisted graphene-based gas sensors make them suitable for widespread environmental applications

Real-time monitoring of breath biomarkers with a magnetoelastic contactless gas sensor: a proof of concept

In the quest for effective gas sensors for breath analysis, magnetoelastic resonance-based gas sensors (MEGSs) are remarkable candidates. Thanks to their intrinsic contactless operation, they can be used as non-invasive and portable devices. However, traditional monitoring techniques are bound to slow detection, which hinders their application to fast bio-related reactions. Here we present a method for real-time monitoring of the resonance frequency, with a proof of concept for real-time monitoring of gaseous biomarkers based on resonance frequency. This method was validated with a MEGS based on a Metglass 2826 MB microribbon with a polyvinylpyrrolidone (PVP) nanofiber electrospun functionalization. The device provided a low-noise (RMS = 1.7 Hz), fast (<2 min), and highly reproducible response to humidity (Delta f = 46-182 Hz for 17-95% RH), ammonia (Delta f = 112 Hz for 40 ppm), and acetone (Delta f = 44 Hz for 40 ppm). These analytes are highly important in biomedical applications, particularly ammonia and acetone, which are biomarkers related to diseases such as diabetes. Furthermore, the capability of distinguishing between breath and regular air was demonstrated with real breath measurements. The sensor also exhibited strong resistance to benzene, a common gaseous interferent in breath analysis

Theoretical analysis of ferromagnetic bilayer structures for realization of stop bands in spin wave transmission spectrum.

A novel mechanism for obtaining the band-stop zones in a microwave frequency region based on a bilayer ferromagnetic structure was theoretically evaluated by the magnetostatic approximation, for magnetostatic surface waves (MSSW) propagating along two-layer thin-film structure. The analysis has revealed spatial evolution and periodical redistribution of MSSW energy in the waveguide system. Energy exchange periodicity was used for suppression of MSSW propagation within a narrow microwave frequency regions

Novel SH-SAW Biosensors for Ultra-Fast Recognition of Growth Factors

In this study, we investigated a label-free time efficient biosensor to recognize growth factors (GF) in real time, which are of gran interesting in the regulation of cell division and tissue proliferation. The sensor is based on a system of shear horizontal surface acoustic wave (SH-SAW) immunosensor combined with a microfluidic chip, which detects GF samples in a dynamic mode. In order to prove this method, to our knowledge not previously used for this type of compounds, two different GFs were tested by two immunoreactions: neurotrophin-3 and fibroblast growth factor-2 using its polyclonal antibodies. GF detection was conducted via an enhanced sequential workflow to improve total test time of the immunoassay, which shows that this type of biosensor is a very promising method for ultra-fast recognition of these biomolecules due to its great advantages: portability, simplicity of use, reusability, low cost, and detection within a relatively short period of time. Finally, the biosensor is able to detect FGF-2 growth factor in a concentration wide range, from 1–25 μg/mL, for a total test time of ~15 min with a LOD of 130 ng/mThis research was funded by the Spanish Ministry of Ciencia, Innovación y Universidades, under the projects: PID-2019-105337RB-C21 and RTI-2018-095856-B-C2Peer reviewe

Improving Sensitivity of a Chemoresistive Hydrogen Sensor by Combining ZIF-8 and ZIF-67 Nanocrystals

In the present work, nanostructures of zeolitic imidazolate frameworks (ZIF-8 and ZIF-67) were combined to obtain a novel chemoresistive sensor, improving the sensitivity of ZIF-67 and facilitating measurement of ZIF-8 by decreasing the resistivity. The sensor detected concentrations as low as 10 ppm of hydrogen increasing its resistivity about 4.5 times. The response of the sensor was compared with a similar chemoresistive sensor based exclusively on ZIF-67, and the sensitivity was around three times higher in the case of the sensor with ZIFs combination.This research is supported by project DGAPA-PAPIIT IA-103016 from Universidad Nacional Autónoma de México and the project TEC-2013-48147 (AEI/FEDER, EU) from Ministerio de Economía y Competitividad of Spain. A. Sainz-Vidal thanks CONACYT for the postdoctoral fellowship at CCADET-UNAM

An Artificial Olfactory System for Toxic Compounds Classification using Machine Learning Techniques

6 páginas, 6 figurasThe long-term exposure to nitrogen dioxide produces harmful effects for humans and any living being. Thus, in security applications, sensor arrays are required for detecting nitrogen dioxide by interfering gas classification. In this work, a compact and intelligent electronic nose (e-nose) based on a Shear-Horizontal Surface Acoustic Wave (SH-SAW) sensor array is proposed for sensing, classifying, and calibrating toxic chemicals. Different carbon-based nanostructured materials are deposited as sensitive layers providing excellent outcomes by mass and elastic changes in this type of sensors. The HS-SAW sensors achieve a high sensitivity, fast response, and reproducibility to different toxic gases such as nitrogen dioxide, carbon monoxide, ammonia, benzene and acetone. The gas flows were controlled by an automated system that consists of four mass flow controllers to obtain the desired concentrations. The e-nose provides an efficient performance with supervised machine learning techniques. Outcomes indicate that Linear Discrimination Analysis (LDA) performs a 90% precise discrimination on test dataset and provides a clear discrimination of NO with interfering toxic compounds. On the other hand, K-Nearest Neighbors (KNN) and Logistic Regression (LR) also achieve excellent classification scores (95% and 79% respectively). Decision surface for toxic compounds of different classification algorithms were also performed achieving good classification. An evaluation and comparison of the prediction methods: Partial Least Square (PLS), Artificial Neural Networks (ANNs) and cascade of ANNs are accomplished. The ANN cascade results show that this technique is an excellent candidate for an accurate prediction and classification of NO. Therefore, the designed and validated e-nose is a promising on-line tool of analysis for environmental applications.Spanish Ministry of Science and Innovation for financing the project RTI2018-095856-B-C22 (AEI/FEDER