472 research outputs found
An adaptive multilevel Monte Carlo algorithm for the stochastic drift-diffusion-Poisson system
Full text linkWe present an adaptive multilevel Monte Carlo algorithm for solving the
stochastic drift-diffusion-Poisson system with non-zero recombination rate. The
a-posteriori error is estimated to enable goal-oriented adaptive mesh
refinement for the spatial dimensions, while the a-priori error is estimated to
guarantee \red{linear} convergence of the error. In the adaptive mesh
refinement, efficient estimation of the error indicator gives rise to better
error control. For the stochastic dimensions, we use the multilevel Monte Carlo
method to solve this system of stochastic partial differential equations.
Finally, the advantage of the technique developed here compared to uniform mesh
refinement is discussed using a realistic numerical example
Quantifying signal changes in nano-wire based biosensors
Get PDFIn this work, we present a computational methodology for predicting the change in signal (conductance sensitivity) of a nano-BioFET sensor (a sensor based on a biomolecule binding another biomolecule attached to a nano-wire field effect transistor) upon binding its target molecule. The methodology is a combination of the screening model of surface charge sensors in liquids developed by Brandbyge and co-workers [Sørensen et al., Appl. Phys. Lett., 2007, 91, 102105], with the PROPKA method for predicting the pH-dependent charge of proteins and protein-ligand complexes, developed by Jensen and co-workers [Li et al., Proteins: Struct., Funct., Bioinf., 2005, 61, 704-721, Bas et al., Proteins: Struct., Funct., Bioinf., 2008, 73, 765-783]. The predicted change in conductance sensitivity based on this methodology is compared to previously published data on nano-BioFET sensors obtained by other groups. In addition, the conductance sensitivity dependence from various parameters is explored for a standard wire, representative of a typical experimental setup. In general, the experimental data can be reproduced with sufficient accuracy to help interpret them. The method has the potential for even more quantitative predictions when key experimental parameters (such as the charge carrier density of the nano-wire or receptor density on the device surface) can be determined (and reported) more accurately. © 2011 The Royal Society of Chemistry
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