Search

Field-effect transistors based on single-walled carbon nanotubes (SWNTs) and graphene can function as highly sensitive nanoscale (bio)sensors in solution. Here, we compare experimentally how SWNT and graphene transistors respond to changes in the composition of the aqueous electrolyte in which they are immersed. We show that the conductance of SWNTs and graphene is strongly affected by changes in the ionic strength, the pH, and the type of ions present, in a manner that can be qualitatively different for graphene and SWNT devices. We show that this sensitivity to electrolyte composition results from a combination of different mechanisms including electrostatic gating, Schottky-barrier modifications, and changes in gate capacitance. Interestingly, we find strong evidence that the sensor response to changes in electrolyte composition is affected by a high density of ionizable groups on both the underlying substrate and the carbon surfaces. We present a model based on the (regulated) surface charge associated with these ionizable groups that explains the majority of our data. Our findings have significant implications for interpreting and optimizing sensing experiments with nanocarbon transistors. This is particularly true for complex biological samples such as cell extracts, growth media, or bodily fluids, for which the complete composition of the solution needs to be considered.

We report an experimental study of 1/f noise in liquid-gated graphene transistors. We show that the gate dependence of the noise is well described by a charge-noise model, whereas Hooge's empirical relation fails to describe the data. At low carrier density, the noise can be attributed to fluctuating charges in close proximity to the graphene, while at high carrier density it is consistent with noise due to scattering in the channel. The charge noise power scales inversely with the device area, and bilayer devices exhibit lower noise than single-layer devices. In air, the observed noise is also consistent with the charge-noise model.

To fully understand animal transcription networks, it is essential to accurately measure the spatial and temporal expression patterns of transcription factors and their targets. We describe a registration technique that takes image-based data from hundreds of Drosophila blastoderm embryos, each costained for a reference gene and one of a set of genes of interest, and builds a model VirtualEmbryo. This model captures in a common framework the average expression patterns for many genes in spite of significant variation in morphology and expression between individual embryos. We establish the method's accuracy by showing that relationships between a pair of genes' expression inferred from the model are nearly identical to those measured in embryos costained for the pair. We present a VirtualEmbryo containing data for 95 genes at six time cohorts. We show that known gene-regulatory interactions can be automatically recovered from this data set and predict hundreds of new interactions.