A Bayesian estimation method for variational phase-field fracture problems

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently refined mesh) as the replacement of measurement data will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependencies of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. In several numerical examples our proposed framework is substantiated and the obtained load-displacement curves, that are usually the target functions, are matched with the reference values. © 2020, The Author(s)

Frequency dependence of dielectrophoretic fabrication of single-walled carbon nanotube field-effect transistors

A new theoretical model for the dielectrophoretic (DEP) fabrication of single-walled carbon nanotubes (SWCNTs) is presented. A different frequency interval for the alignment of wide-energy-gap semiconductor SWCNTs is obtained, exhibiting a considerable difference from the prevalent model. Two specific models are study, namely the spherical model and the ellipsoid model, to estimate the frequency interval. Then, the DEP process is performed and the obtained frequencies (from the spherical and ellipsoid models) are used to align the SWCNTs. These empirical results confirm the theoretical predictions, representing a crucial step towards the realization of carbon nanotube field-effect transistors (CNT-FETs) via the DEP process based on the ellipsoid model. © 2020, The Author(s)

Investigation of Device Parameters for Field-Effect DNA-Sensors by Three-Dimensional Simulation

The development of a DNA field-effect transistor (DNAFET) simulator is described and implications on device structure and future experiments are discussed. In DNAFETs the gate structure is replaced by a layer of immobilized single-stranded DNA molecules which act as surface probe molecules [1, 2]. When complementary DNA strands bind to the receptors, the charge distribution near the surface of the device changes, modulating current transport through the device and enabling detection (cf. Fig. 1 and 5). Arrays of DNAFETs can be used for detecting singlenucleotide polymorphisms and for DNA sequencing. The advantage of DNAFETs over optical methods of detection is that DNAFETs allow direct, label-free operation

Noise and Fluctuations in Nanowire Biosensors

Abstract: This work deals with the stochastic simulation of a nanowire biosensor surface and the surrounding liquid domain for DNA detection. The objective is an analysis of the fluctuations and of the biological noise induced by the inherent randomness of the hybridization process at the surface. We consider a coupled system of diffusion-reaction equations to model the movement of DNA oligomers as well as the hybridization processes at the functionalized surface of the sensor. Since analytical solutions cannot be derived, numerical investigation is necessary. Here, we present an algorithm different from the already published one i

An extensible TCAD optimization framework combining gradient based and genetic optimizers,” presented at the

ABSTRACT Our Simulation Environment for Semiconductor Technology Analysis (Siesta) is a flexible, user programmable tool for optimization and inverse modeling of semiconductor devices. It is easily customizable through an interactive, object-oriented and functional scripting language. Dynamic load balancing enables to take advantage of a cluster of hosts with minimal requirements on the software infrastructure. Our approach combines the advantages of gradient based and evolutionary algorithm optimizers into one framework. Gradient based optimizers are well-suited for finding local extrema. Evolutionary algorithm optimizers add the capability of finding global extrema and thus make unattended optimizations without guessing starting values possible. Experiments can be interactively set up and tested. Bindings for the most common simulation tools are provided, and new bindings can easily be integrated taking advantage of the object-oriented and functional design. Results of experiments are saved in an object database and can be interactively retrieved as starting points for further computations or for visualizations. The user may impose arbitrary constraints (as functions defined on the parameter space) on the set in which solutions are searched. Evaluating the constraints before any simulation tool is called and getting rid of useless combinations of parameter values saves computation time and eliminates the risk of the simulation tools being called with input values that might lead to unforeseen behavior. The combination of gradient based and evolutionary algorithm optimizers enables many new optimization strategies and includes convenient handling of results

Fluctuations due to association and dissociation processes at nanowire- biosensor surfaces and their optimal design

Abstract In this work, we calculate the effect of the binding and unbinding of molecules at the surface of a nanowire biosensor on the signal-to-noise ratio of the sensor. We model the fluctuations induced by association and dissociation of target molecules by a stochastic differential equation and extend this approach to a coupled diffusion-reaction system. Where possible, analytic solutions for the signal-to-noise ratio are given. Stochastic simulations are performed wherever closed forms of the solutions cannot be derived. Starting from parameters obtained from experimental data, we simulate DNA hybridization at the sensor surface for different target molecule concentrations in order to optimize the sensor design