Modelling electronic transport in monocrystalline metal oxide gas sensors: from the surface kinetics to the experimental response

Gas sensing systems and devices based on metal oxides are widely spreading due to their high performance in terms of sensor response and relatively low costs. Despite several experimental studies and molecular simulations are available in the literature, a tool that can quickly predict the macroscopic sensor response, and potentially be used for predictive purposes, is still missing. In this work, we present a modelling approach based on finite-element simulations, using material electrical properties available in the literature. In a first approach, we derive the surface electron trap concentration from fitting the global sensor response. Then, we improve the model by considering the actual time-dependent experimental response. We consider sensors based on single SnO2 nanowires and show how our model predicts with a good agreement the experimental response vs. NO2, as a function of the working temperature and gas concentration, and additionally provides many other physical quantities of interest, such as the conduction band edge bending, the space charge and the width of the depletion layer. We further discuss ideas for improving the model and thus increasing its predictive potential with an engineering perspective

Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations

Modelling and Simulation of Silicon Nanowire-Based Electron Devices for Computation and Sensing

Silicon Nanowires (SiNWs) are considered the fundamental component blocks of future nanoelectronics. Many interesting properties have gained them such a prominent position in the investigation in recent decades. Large surface-to-volume ratio, bio-compatibility, band-gap tuning are among the most appealing features of SiNWs. More importantly, in the ongoing process of dimension miniaturization, SiNWs compatibility with the existing and reliable silicon technology stands as a fundamental advantage. Consequently, the employment of SiNWs spred in several application fields: from computational logic where SiNWs are used to realize transistors, to bio-chemical sensing and nanophotonic applications. In this thesis work we concentrate our attention on the employment of SiNWs in computational logic and bio-chemical sensing. In particular, we aim at giving a contribution in the modelling and simulation of SiNW-based electron devices. Given the current intense investigation of new devices, the modelling of their electrical behaviour is strongly required. On one side, modelling procedures could give an insight on the physical phenomena of transport in nanometer scale systems where quantum effects are dominant. On the other side, the availability of compact models for actual devices can be of undeniable help in the future design process. This work is divided into two parts. After a brief introduction on Silicon Nanowires, the main fabrication techniques and their properties, the first part is dedicated to the modelling of Multiple-Independent Gate Transistors, a new generation of devices arisen from the composition of Gate-All-Around Transistors, finFETs and Double-Gate Transistors. Interesting applications resulting from their employment are Vertically-stacked Silicon Nanowire FETs, known to have an ambipolar behaviour, and Silicon Nanowire Arrays. We will present a compact numerical model for composite Multiple-Independent Gate Transistors which allows to compute current and voltages in complex structures. Validation of the model through simulation proves the accuracy and the computational efficiency of the resulting model. The second part of the thesis work is instead devoted to Silicon Nanowires for bio-chemical sensing. In this respect, major attention is given to Porous Silicon (PS), a non-crystalline material which demonstrated peculiar features apt for sensing. Given its not regular microscopic morphology made of a complex network of crystalline and non-crystalline regions, PS has large surface-to-volume ratio and a relevant chemical reactivity at room temperature. In this work we start from the fabrication of PS nanowires at Istituto Nazionale di Ricerca Metrologica in Torino (I.N.Ri.M.) to devise two main models for PSNWs which can be used to understand the effects of porosity on electron transport in these structures. The two modelling procedures have different validity regimes and efficiently take into account quantum effects. Their description and results are presented. The last part of the thesis is devoted to the impact of surface interaction of molecular compounds and dielectric materials on the transport properties of SiNWs. Knowing how molecules interact with silicon atoms and how the conductance of the wire is affected is indeed the core of SiNWs used for bio-chemical sensing. In order to study the phenomena involved, we performed ab-initio simulations of silicon surface interacting with SO2 and NO2 via the SIESTA package, implementing DFT code. The calculations were performed at Institut de Ciencia De Materials de Barcelona (ICMAB-CSIC) using their computational resources. The results of this simulation step are then exploited to perform simulation of systems made of an enormous quantity of atoms. Due to their large dimensions, atomistic simulations are not affordable and other approaches are necessary. Consequently, calculations with physics-based softwares on a larger spatial scale were adopted. The description of the obtained results occupies the last part of the work together with the discussion of the main theoretical insight gained with the conducted study

Development of potentiostat platform for complex voltammetric measurements and self-adjustable electrolysis

With rapid technological progress, electrochemical methods are being developed and improved throughout the years. Outdated models of electrochemical equipment like potentiostats are replaced with newer models with more functions and capability. Since those outdated models are programmed using programming language like C++, users can utilize the source code to program new features to the outdated potentiostats, extending its period of use. We developed methods enabling users to create new functionality of an electrochemical analytical system using its software development platform on MATLAB� to perform complex voltammetric measurements and self-adjustable electrolysis. Complex voltammetric measurement is defined by running chronoamperometry under arbitrary potential waveform rather than the traditional constant voltage chronoamperometry or triangular potential waveform used in cyclic voltammetry. Self-adjustable electrolysis is demonstrated as a proof-of-concept via a nanowire array electrode, monitoring, and self-adjusting potentiostat parameters throughout the experiment with the execution of a control loop. The development of arbitrary waveform allows the potential improvement of sensitivity in electroanalytical methods for detecting compounds and their redox potential, whereas the development of self-adjustable electrolysis facilitates in the self-optimization of method parameters in electrochemical measurement through a control loop mechanism

A Versatile Method of Resolution Enhancement for Tactile Sensor Array Used as Synthetic Skin: Modeling and Implementation

Tactile sensors are one of the major devices that enable robotic systems to interact with the surrounding environment. In particular, modern assistive robotic systems need to carry out many human-like activities. Thus, it is desired to have sensor arrays that can acquire different environmental information just like human skin. In the past two decades, various types of tactile sensor arrays have been developed to acquire different physical properties, such as temperature, force, and geometric shapes. However, though the measurement of a single sensor can be accurate, the planar resolution is limited due to fabrication difficulties. This research aims to propose a mathematical model to describe the behavior of a tactile sensor based on experimental and statistical analyses. Moreover, to develop a versatile algorithm that can be applied to different tactile sensor arrays to enhance the limited resolution. With the proposed algorithm, the resolution can be increased up to twenty times if multiple measurements are available.;To verify if the algorithm can be used for tactile sensor arrays that are used in robotic system, a 16 x 10 force sensing array (FSR) is adopted. The force was first measured by a scanning data acquisition device integrated by a synthesized electronic circuit, DAQ station and an interface developed using MATLAB. The acquired two dimensional measurements were then processed by the Proposed Resolution Enhancement Algorithm (PREA) to enhance the resolution. The proposed algorithm can be used to improve the resolution for single image or multiple measurements. In this study, the developed MATLAB scripts can automatically identify the location of the targeted objects if multiple measurements are recorded. As a result, the resolution of the sensor is increased and it can be used as synthetic skin to identify accurate shapes of objects and applied forces

Development of High Resolution Tools for Investigating Cardiac Arrhythmia Dynamics

Every year 300,000 Americans die due to sudden cardiac death. There are many pathologies, acquired and genetic, that can lead to sudden cardiac death. Regardless of the underlying pathology, death is frequently the result of ventricular tachycardia and/or fibrillation (VT/VF). Despite decades of research, the mechanisms of ventricular arrhythmia initiation and maintenance are still incompletely understood. A contributing factor to this lack of understanding is the limitations of the investigative tools used to study VT/VF. Arrhythmias are organ level phenomena that are governed by cellular interactions and as such, near cellular levels of resolution are needed to tease out their intricacies. They are also behaviors that are not limited by region, but dynamically affect the entirety of the heart. For these reasons, high-resolution methodologies capable of measuring electrophysiology of the whole entirety of the ventricles will play an important role in gaining a complete understanding of the principles that govern ventricular arrhythmia dynamics. They will also be essential in the development of novel therapies for arrhythmia management. In this dissertation, I first present the validation and characterization of a novel capacitive electrode design that overcomes the key limitations faced by modern implantable cardiac devices. I then outline the construction, methodologies, and open-source tools of an improved optical panoramic mapping system for small mammalian cardiac electrophysiology studies. I conclude with a small mammal study of the relationship between action potential duration restitution dynamics and the mechanisms of maintenance in ventricular arrhythmias

Long period fiber grating, thin coating of graphene and silver nanowires, and corrosion sensing for life-cycle assessment of steel structures

This study aims to develop and validate a compact, integrated lab-on-sensor system for simultaneous measurement of strain, temperature and corrosion-induced mass loss in steel structures and concrete reinforcement elements in order to assess their life-cycle performance. The sensing system operates based on the principle of long period fiber gratings (LPFG) that are responsive to both thermal and mechanical deformation, and the change in refractive index of any medium surrounding the optical fiber. To fabricate a LPFG sensor for strain and temperature measurement, a CO2 laser aided fiber grating system was assembled. To enable mass loss measurement, a low pressure chemical vapor deposition (LPCVD) system was built to synthesize a graphene/silver nanowire composite film as flexible transparent electrode for the electroplating of a thin Fe-C layer on the curve surface of a LPFG sensor. Together with two LPFG sensors in LP06 and LP07 modes for simultaneous strain and temperature measurement, three Fe-C coated LPFG sensors were multiplexed and deployed inside three miniature, coaxial steel tubes to measure critical mass losses through the penetration of tube walls and their corresponding corrosion rates in the life cycle of an instrumented steel component. A Fe-C coated LPFG sensor was submerged in a NaCl solution and calibrated for stress corrosion cracking under three strain levels. The corrosion mechanism of the Fe-C layer was investigated and the distribution of cracks (width, length and spacing) were characterized and correlated with the wavelength change of the sensor. Thermal, loading and accelerated corrosion tests were conducted to validate the functionality, sensitivity, accuracy, and robustness of the proposed sensing system and demonstrate its feasibility in in situ applications --Abstract, page iii