In 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.
The conductance change of nanowire field-effect transistors is considered a highly sensitive probe for surface charge. However, Debye screening of relevant physiological liquid environments challenge device performance due to competing screening from the ionic liquid and nanowire charge carriers. We discuss this effect within Thomas-Fermi and Debye-Huckel theory and derive analytical results for cylindrical wires which can be used to estimate the sensitivity of nanowire surface-charge sensors. We study the interplay between the nanowire radius, the Thomas-Fermi and Debye screening lengths, and the length of the functionalization molecules. The analytical results are compared to finite-element calculations on a realistic geometry.Comment: 4 pages including 2 figures. Accepted for AP
A single charge screening model of surface charge sensors in liquids (De Vico et al., Nanoscale, 2011, 3, 706-717) is extended to multiple charges to model the effect of the charge distributions of analyte proteins on FET sensor response. With this model we show that counter-intuitive signal changes (e.g. a positive signal change due to a net positive protein binding to a p-type conductor) can occur for certain combinations of charge distributions and Debye lengths. The new method is applied to interpret published experimental data on Streptavidin (Ishikawa et al., ACS Nano, 2009, 3, 3969-3976) and Nucleocapsid protein (Ishikawa et al., ACS Nano, 2009, 3, 1219-1224).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.