Graphene based hyperbolic metamaterial for tunable mid-infrared biosensing.

Plasmonic biosensors, operating in the mid-infrared (mid-IR) region, are well-suited for highly specific and label-free optical biosensing. The principle of operation is based on detecting the shift in resonance wavelength caused by the interaction of biomolecules with the surrounding medium. However, metallic plasmonic biosensors suffer from poor signal transduction and high optical losses in the mid-IR range, leading to low sensitivity. Here, we introduce a hyperbolic metamaterial (HMM) biosensor, that exploits the strong, tunable, mid-IR localization of graphene plasmons, for detecting nanometric biomolecules with high sensitivity. The HMM stack consists of alternating graphene/Al2O3 multilayers, on top of a gold grating structure with rounded corners, to produce plasmonic hotspots and enhance sensing performance. Sensitivity and figure-of-merit (FOM) can be systematically tuned, by varying the structural parameters of the HMM stack and the doping levels (Fermi energy) in graphene. Finite-difference time-domain (FDTD) analysis demonstrates that the proposed biosensor can achieve sensitivities as high as 4052 nm RIU-1 (refractive index unit) with a FOM of 11.44 RIU-1. We anticipate that the reported graphene/Al2O3 HMM device will find potential application as a mid-IR, highly sensitive plasmonic biosensor, for tunable and label-free detection.

A whole body statistical shape model for radio frequency simulation.

The development of ultra low power wireless sensors for customized wearable and implantable medical devices requires patient specific models for radio frequency simulation to understand wave propagation in the body. In practice, the creation of a patient specific whole-body model is difficult and time consuming to create. It is therefore necessary to establish a method for studying a population in a statistical manner. In this paper, we present a statistical shape model for the whole body for RF simulation. It is built from 10 male and 10 female subjects of varying size and height. This model has the ability to instantiate a new surface mesh with the parameters allowed by the training set. This model would provide shapes of varying sizes for studies, without the requirement of obtaining subject specific whole body models. Results from finite-differences time-domain simulation are presented on the extreme shapes from the model and demonstrate the need for a full understanding of the range in body shapes.