Analysis of Field-Effect Biosensors using Self-Consistent 3D Drift-Diffusion and Monte-Carlo Simulations

AbstractField-effect biosensors based on nanowires enjoy considerable popularity due to their high sensitivity and direct electrical readout [1]. However, crucial issues such as the influence of the biomolecules on the charge-carrier transport or the binding of molecules to the surface have not been described satisfactorily yet in a quantitative manner. In order to analyze these effects, we present simulation results based on a 3D macroscopic transport model coupled with Monte-Carlo simulations for the bio-functionalized surface layer. Excellent agreement with measurement data has been found, while detailed study of the influence of the most prominent biomolecules, namely double-stranded DNA and single-stranded DNA, on the current through the semiconductor transducer has been carried out

Silicon nanowire field-effect transistors for the detection of proteins

In this dissertation I present results on our efforts to increase the sensitivity and selectivity of silicon nanowire ion-sensitive field-effect transistors for the detection of biomarkers, as well as a novel method for wireless power transfer based on metamaterial rectennas for their potential use as implantable sensors. The sensing scheme is based on changes in the conductance of the semiconducting nanowires upon binding of charged entities to the surface, which induces a field-effect. Monitoring the differential conductance thus provides information of the selective binding of biological molecules of interest to previously covalently linked counterparts on the nanowire surface. In order to improve on the performance of the nanowire sensing, we devised and fabricated a nanowire Wheatstone bridge, which allows canceling out of signal drift due to thermal fluctuations and dynamics of fluid flow. We showed that balancing the bridge significantly improves the signal-to-noise ratio. Further, we demonstrated the sensing of novel melanoma biomarker TROY at clinically relevant concentrations and distinguished it from nonspecific binding by comparing the reaction kinetics. For increased sensitivity, an amplification method was employed using an enzyme which catalyzes a signal-generating reaction by changing the redox potential of a redox pair. In addition, we investigated the electric double layer, which forms around charges in an electrolytic solution. It causes electrostatic screening of the proteins of interest, which puts a fundamental limitation on the biomarker detection in solutions with high salt concentrations, such as blood. We solved the coupled Nernst-Planck and Poisson equations for the electrolyte under influence of an oscillating electric field and discovered oscillations of the counterion concentration at a characteristic frequency. In addition to exploring different methods for improved sensing capabilities, we studied an innovative method to supply power to implantable biosensors wirelessly, eliminating the need for batteries. A metamaterial split ring resonator is integrated with a rectifying circuit for efficient conversion of microwave radiation to direct electrical power. We studied the near-field behavior of this rectenna with respect to distance, polarization, power, and frequency. Using a 100 mW microwave power source, we demonstrated operating a simple silicon nanowire pH sensor with light indicator

Field-Effect Sensors

This Special Issue focuses on fundamental and applied research on different types of field-effect chemical sensors and biosensors. The topics include device concepts for field-effect sensors, their modeling, and theory as well as fabrication strategies. Field-effect sensors for biomedical analysis, food control, environmental monitoring, and the recording of neuronal and cell-based signals are discussed, among other factors

Low-Dimensional Materials for Disruptive Microwave Antennas Design

This chapter is devoted to a complete analysis of remarkable electromagnetic properties of nanomaterials suitable for antenna design miniaturization. After a review of state of the art mesoscopic scale modeling tools and characterization techniques in microwave domain, new approaches based on wideband material parameters identification (complex permittivity and conductivity) will be described from impedance equivalence formulation achievement by de-embedding techniques applicable in integrated technology or in free space. A focus on performances of 1D materials such as vertically aligned multi-wall carbon nanotube (VA-MWCNT) bundles, from theory to technology, will be presented as a disruptive demonstration for defense and civil applications as in radar systems

ICN Annual Report 2010

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Nanoscale Architectures for Smart Bio-Interfaces: Advances and Challenges

Volcanology & seismolog

PDE models of field-effect sensors

Die Verwendung von Nanodrähten als biologisch sensitive Feldeffekttransistoren und somit als markierungsfreie Sensoren birgt eine Reihe von vielversprechenden Anwendungen wie zum Beispiel die Detektion von DNA oder, wie kürzlich in Blut nachgewiesen, von Krankheitsmarkern. Doch trotz des enormen experimentellen Entwicklungsfortschrittes und dem Versuch diese Technologie massenproduktionstauglich zu machen, fehlt ein umfassendes quantitatives Verständins, welches nur durch mathematische Modellierung und durch Simulationen erreicht werden kann. Das mathematische Modell muss dabei zweierlei Dinge berücksichtigen: Den Aufbau solcher Sensoren und das Detektionsprinzip. Der Aufbau besteht grundlegend aus einer nichtleitenden Schicht auf der der halbleitende Nanodraht aufgelegt ist. Der Nanodraht selbst ist wiederum mit einer nichtleitenden Schicht überzogen und der ganze Sensor ist einer wässrigen Lösung ausgesetzt, welche die zu detektierenden Moleküle enthält. Um alle geometrischen Eigenheiten des Sensors zu berücksichtigen muss das Modellierungsgebiet als dreidimensional angenommen werden. Die vorkommenden Materialien können dann mit einem System von partiellen Differentialgleichungen beschrieben werden. Dieses System ist auf der Poisson Gleichung aufgebaut und beinhaltet die Poisson-Boltzmann Gleichung und das Drift-Diffusion-Poisson System. Mit diesen Gleichungen können das elektrostatische Potential und die Ladungsträgerkonzentrationen selbstkonsistent berechnet und somit der Strom durch den Sensor bestimmt werden. Das Detektionsprinzip und somit auch die Sensitivität des Sensors ist von dem, durch den Wechsel der Ladung in einer Grenzschicht, welcher durch die Bindung der zu detektierenden Moleküle an die an der Oberfläche befindlichen Moleküle zu Stande kommt, induzierten, Feldeffekt abhängig. Da diese Moleküle um einige Größenordnungen kleiner sind als die Gesamtgröße des Sensors würde eine herkömmliche Methode zur Lösung des Gleichungssystems, zum Beispiel die finite Volumen Methode, eine sehr feine Auflösung brauchen. Daher ist eine Multiskalenmethode angeraten welche nicht nur das Auflösungsproblem löst sondern gleichzeitig auch den Vorteil mit sich bringt, dass verschiedene Methoden zur Berechnung der Gegenionen in der Grenzschicht verwendet werden können. Nichtsdestotrotz ist das zu lösende lineare System, welches aus der finiten Volumen Methode resultiert, nach wie vor groß. Daher wurde eine Parallelisierungsmethode entwickelt. Parallelisierungsmethoden die auf dem FETI Algorithmus aufbauen haben den Vorteil, dass das Schnittstellenproblem durch Lagrange Multiplikatoren gelöst wird und somit die Sprungbedingungen der Multiskalenmethode in einer intuitiven Art implementiert werden können. Abschließend kann dieses Model dazu benutzt werden, die Sensitivität in Abhängigkeit von geometrischen und physischen Eigenschaften zu optimieren.Nanowires used as biologically sensitive field-effect transistors (biofets) are promising labelfree sensing devices with a wide range of applications, e.g., the detection of DNA or disease markers, recently even in whole blood. Despite the experimental progress in recent years and the push towards mass fabrication, quantitative understanding of the devices has been missing and hence mathematical modeling and simulation are crucial for physical understanding and optimal sensing. Therefore, a mathematical model has to include two major parts, the structure of such devices and the sensing mechanism. The structure is based on a dielectric bulk layer carrying the semiconducting nanowire. The nanowire itself is covered by a second, thin dielectric layer. The whole dielectric surface is functionalized with probe molecules and is exposed to an aqueous solution containing the target molecules. To capture all geometry properties the model domain needs to be in 3d. The different materials are simulated by a system of partial differential equations based on the Poisson equation consisting of the Poisson-Boltzmann equation and the drift-diffusion-Poisson system. A solution of these equations exists and is locally unique around thermal equilibrium. The electrostatic potential and the charge densities are self-consistently computed and hence the current through the device is obtained. The sensing mechanism and hence the sensitivity of the sensor is based on a field effect induced by a change of the boundary layer charge due to binding of target molecules to probe molecules. Since these molecules are some orders of magnitude smaller than the structure of the nanowire, a usual approach, such as the finite volume method, would need a very fine resolution. Hence a multiscale method is recommended which has the advantage that not only the resolution problem is avoided, it also makes it possible to use various methods for the computation of the concentration of counter ions in the boundary layer. Nonetheless, the resulting linear problem, after discretization with the finite volume scheme, is still large and hence a parallelization technique has been developed. Parallelization techniques based on the FETI method have the advantage that the interface problem is solved by Lagrange multipliers and hence the implementation of the jump conditions from the multiscale method is straightforward. In the end, this model can be used to determine the optimal point of sensitivity regarding the geometry as well as the physical properties of such devices