Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics

Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things.

Molecular Communication (MC) is a bio-inspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors

Transmitter and Receiver Architectures for Molecular Communications: A Survey on Physical Design with Modulation, Coding, and Detection Techniques

Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them.This work was supported in part by the European Research Council (ERC) Projects MINERVA under Grant ERC-2013-CoG #616922 and MINERGRACE under Grant ERC-2017-PoC #780645

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

Silicon Nanowires for Biosensor Applications

Nanostrukturen haben in den letzten Jahrzehnten durch konsequente Förderung wie der im Jahr 2000 gestarteten National Nanotechnology Initiative der USA oder des deutschen Pendants Aktionsplan Nanotechnologie erhebliches Aufsehen, nicht nur in der Wissenschaft, sondern auch in der technischen und wirtschaftlichen Umsetzung erfahren. In Kombination mit biologischen Systemen, deren Funktionalität sich auf der Größenordnung von Nanometern abspielt, finden nanotechnologische Entwicklungen auf dem Gebiet der Medizin ein großes technisches Anwendungsgebiet. Diese Arbeit widmet sich der Untersuchung und technischen Entwicklung von Siliziumnanodrähten als Sensoren für zukünftige medizinische Anwendungen. Im Gegensatz zu Sensoren die auf dotierten Nanodrähten basieren, wurden hier undotierte Nanodrähte untersucht, die mit geringerem Produktionsaufwand auskommen und mittels Schottky-Barrieren als Feldeffekttransistoren nutzbar sind. Deren Eigenschaften wurden im Hinblick auf pH und Biosensorik theoretisch und experimentell untersucht, sowie technisch in ein lab-on-chip sowie ein kompaktes Multiplexer-Messgerät integriert. In einem zweiten, separaten Teil wurden die Eigenschaften undotierter Nanodrähte für die optische Spektroskopie theoretisch modelliert. Die Inhalte beider Teile werden im folgenden kurz zusammengefasst. Um die elektrischen Sensoreigenschaften der Siliziumnanodrähte zu untersuchen, wurden zunächst Computermodelle der Drähte erstellt, mit deren Hilfe der Elektronentransport in flüssiger Umgebung quantenmechanisch modelliert wurde. Die dafür erstellten Modellvorstellungen waren für die sich daran anschließenden experimentellen Untersuchungen des Rauschverhaltens, der pH-Sensitivität sowie der Biosensoreigenschaften sehr vorteilhaft. Mit Hilfe einer neu entwickelten Messmethode konnte der optimale Arbeitspunkt der Sensoren ermittelt werden, sowie die hohe Sensorqualität mittels einer empirischen mathematischen Beschreibung des zu erwartenden Sensorsignals eingeordnet werden. Weiterhin wurden für die Medizintechnik relevante Messungen von Thrombin durchgeführt. Damit ist für den hier beschriebenen Sensortyp ein proof-of-concept für neuartige medizinische Messelemente gelungen. Um die kleinen Abmessungen der Sensoren darüber hinaus technisch nutzbar zu machen, wurden sie in ein lab-on-chip System integriert, in welchem sie als Sensoren für den pH-Wert sowie die ionische Konzentration in Nanoliter-Tropfen verwendet wurden. Desweiteren wurde in Kooperation mit dem Institut für Aufbau- und Verbindungstechnik ein portables Messgerät entwickelt, welches die parallele Messung mehrerer Nanodrahtsensoren ermöglicht. Im zweiten Teil der Arbeit wird eine theoretische Untersuchung zur Eignung von Silizium-Nanodrähten als Messsonden (Probes) für die optische Spektroskopie vorgestellt. Dazu wurde eine Methode entwickelt mittels derer es möglich ist, Raman und Infrarotspektren von Nanostrukturen mittels Molekulardynamik zu berechnen. Die Methode wurde auf undotierte Silizium-Nanodrähte augewendet und zeigt, dass die Oberflächenbeschaffenheit der Drähte die optischen Spektren entscheidend beeinflusst. Damit konnte die Relevanz von Halbeiter-Nanostrukturen auch für Anwendungen in der optischen Spektroskopie gezeigt werden.:I Introduction: Sensing with Nanostructures 1 Introduction 2 Field effect transistors as electronic sensor elements 3 Packaging: Connecting Nano and Macro 4 Nanostructures as transducers in optical spectroscopy II Electronic sensing with Schottky Barrier silicon nanowires 5 Schottky-Barrier silicon nanowire field effect transistors 6 ISFET measurement principles 7 pH and Biosensing with silicon nanowires 8 Thrombin sensing 9 Silicon nanowire FETs in a Lab-on-a-Chip device 10 Multiplexer sensing platform 11 Experimental methods III Simulating optical spectra of silicon nanowires 12 Theoretical fundamentals 13 Computational Methods 14 Results 15 Bibliography 16 AnhangNanostructures have attracted great attention not only in scientific research, but also in engineering applications during the last decades. Especially in combination with biological systems, whose complex function is controlled from nanoscale building blocks, nanotechnological developments find a huge field of applications in the medical sector. This work is dedicated to the functional understanding and technical implementation of silicon nanowires for future medical sensor applications. In contrast to doped silicon nanowire based sensors, this work is focussed on pure, undoped silicon nanowires, which have lower demands on production techniques and use Schottky-barriers as electric field detectors. The pH and biosensing capabilities of such undoped silicon nanowire field effect transistors were investigated theoretically and experimentally and further integrated in a lab-on-a-chip device as well as a small-scale multiplexer measurement device. In a second separate part, the optical sensing properties of undoped silicon nanowires were theoretically modeled. The main contents of both parts are shortly described in the following paragraphs. A multiscale model of silicon nanowire FETs to describe the charge transport in liquid surrounding in a quantum mechanical framework was developed to investigate the sensing properties of the nanowire sensors in general. The model set the basis for the understanding of the subsequent experimental investigations of noise characterization, pH sensitivity and biosensing properties. With the help of a novel gate sweeping measurement method the optimal working point of the sensors was determined and the high sensor quality could be quantified in terms of an empirical mathematical model. The sensor was then used for measurements of medically relevant concentrations of the Thrombin protein, providing a proof-of-concept for medical applications for our newly developed sensor. In order to exploit the small size of our sensors for technical applications we integrated the devices in lab-on-a-chip system with a microfluidic droplet generation module. There they were used to measure the pH and ionic concentration of droplets. Finally a portable multiplex measurement device for silicon nanowire sensors as well as other ion sensitive FETs was developed in cooperation with the IAVT at TU Dresden (Institut für Aufbau- und Verbindungstechnik). The second part of this thesis investigates the usability of silicon nanowires for optical sensor applications from a theoretical point of view. Therefore a method for the extraction of Raman and Infrared spectra from molecular dynamics simulations was developed. The method was applied to undoped silicon nanowires and shows that the surface properties of the nanowires has a significant effect on optical spectra. These results demonstrate the relevance of semiconductor nanostructures for applications in optical spectroscopy.:I Introduction: Sensing with Nanostructures 1 Introduction 2 Field effect transistors as electronic sensor elements 3 Packaging: Connecting Nano and Macro 4 Nanostructures as transducers in optical spectroscopy II Electronic sensing with Schottky Barrier silicon nanowires 5 Schottky-Barrier silicon nanowire field effect transistors 6 ISFET measurement principles 7 pH and Biosensing with silicon nanowires 8 Thrombin sensing 9 Silicon nanowire FETs in a Lab-on-a-Chip device 10 Multiplexer sensing platform 11 Experimental methods III Simulating optical spectra of silicon nanowires 12 Theoretical fundamentals 13 Computational Methods 14 Results 15 Bibliography 16 Anhan

Nano/Bio-Receiver Architectures and Detection Methods for Molecular Communications

Internet of Nano Things (IoNT) is an emerging technology, which aims at extending the connectivity into nanoscale and biological environments with collaborative networks of artificial nanomachines and biological entities integrated into the Internet. To enable the IoNT and its groundbreaking applications, such as real-time intrabody health monitoring, it is imperative to devise nanoscale communication techniques with low-complexity transceiver architectures. Bio-inspired molecular communications (MC), which uses molecules to transfer information, is the most promising technique to realise IoNT due to its inherent biocompatibility and reliability in physiologically-relevant environments. Despite the substantial body of work concerning MC, the implications of an interface between MC channel and practical MC transceiver architectures are largely neglected, leading to a major gap between theory and practice. As the first step to remove this discrepancy, in this thesis, I develop a realistic analytical ICT model for microfluidic MC with surface-based receivers as a convection-diffusion-reaction system. In the second part, I focus on biological MC receivers, which can be implemented in living cells using synthetic biology tools. In this direction, I theoretically develop low-complexity and reliable MC detection methods exploiting the various statistics of the stochastic ligand-receptor interactions at the membrane of biological MC receivers. The estimation and detection theoretical analysis of these detection methods demonstrate that even single type of receptors can provide sufficient statistics to overcome the receptor saturation problem, cope with the interference of non-cognate molecules, and simultaneously sense the concentration of multiple types of ligands. I also propose synthetic receptor designs for the transduction of decision statistics into a representation by concentration of intracellular molecules, and design chemical reaction networks performing decoding with intracellular reactions. Finally, I fabricate a micro/nanoscale MC receiver based on graphene field-effect transistor biosensors and perform its ICT characterisation in a custom-designed microfluidic MC system with the information encoded into the concentration of DNAs. This experimental platform is the first practical demonstration of micro/nanoscale MC, and can serve as a testbed for developing realistic MC methods

Odor-Based Molecular Communications: State-of-the-Art, Vision, Challenges, and Frontier Directions

Humankind mimics the processes and strategies that nature has perfected and uses them as a model to address its problems. That has recently found a new direction, i.e., a novel communication technology called molecular communication (MC), using molecules to encode, transmit, and receive information. Despite extensive research, an innate MC method with plenty of natural instances, i.e., olfactory or odor communication, has not yet been studied with the tools of information and communication technologies (ICT). Existing studies focus on digitizing this sense and developing actuators without inspecting the principles of odor-based information coding and MC, which significantly limits its application potential. Hence, there is a need to focus cross-disciplinary research efforts to reveal the fundamentals of this unconventional communication modality from an ICT perspective. The ways of natural odor MC in nature need to be anatomized and engineered for end-to-end communication among humans and human-made things to enable several multi-sense augmented reality technologies reinforced with olfactory senses for novel applications and solutions in the Internet of Everything (IoE). This paper introduces the concept of odor-based molecular communication (OMC) and provides a comprehensive examination of olfactory systems. It explores odor communication in nature, including aspects of odor information, channels, reception, spatial perception, and cognitive functions. Additionally, a comprehensive comparison of various communication systems sets the foundation for further investigation. By highlighting the unique characteristics, advantages, and potential applications of OMC through this comparative analysis, the paper lays the groundwork for exploring the modeling of an end-to-end OMC channel, considering the design of OMC transmitters and receivers, and developing innovative OMC techniques