We present a theory for the multiple scattering of light by obstacles situated over a rough surface. This problem is important for applications in biological and chemical sensors. To keep the formulation of this theory simple, we study scalar waves. This theory requires knowledge of the scattering operator (t-matrix) for each of the obstacles as well as the reflection operator for the rough surface. The scattering operator gives the field scattered by the obstacle due to an exciting field incident on the scatterer. The reflection operator gives the field reflected by the rough surface due to an exciting field incident on the rough surface. We apply this general theory for the special case of point scatterers and a slightly rough surface with homogeneous Dirichlet and Neumann boundary conditions. We show examples that demonstrate the utility of this theory.

We present a simple, ultra-rapid and robust method to create sharp nanostructures-nanopetals-in a shape memory polymer substrate demonstrating unprecedented enhancements for surface enhanced sensing over large surface areas. These bimetallic nanostructures demonstrate extremely strong surface plasmon resonance effects due to the high density multifaceted petal structures that increase the probability of forming nanogaps. We demonstrate that our nanopetals exhibit extremely strong surface plasmons, confining the emission and enhancing the fluorescence intensity of the nearby high-quantum yield fluorescein by >4000×. The enhancements are confined to the extremely small volumes at the nanopetal borders. This enables us to achieve single molecule detection at relatively high and physiological concentrations. © 2010 American Institute of Physics.

(Figure Presented) Controllable biaxial and uniaxial nanowrinkles (see figure) are fabricated by a simple two-step approach - metal deposition and subsequent heating - based on shape memory polymer (prestressed polystyrene) sheets. The wavelengths of the wrinkles can be tuned by controlling the thickness of deposited metal. The ready integration of the nanowrinkles into microchannels and their effectiveness in surface enhanced sensing is demonstrated. © 2009 Wiley-VCH Verlag GmbH & Co. KGaA.

Abstract The eastern North American margin community seismic experiment (ENAM‐CSE) was conceived to target the ENAM Geodynamic Processes at Rifting and Subducting Margins (GeoPRISMS) primary site with a suite of both active‐ and passive‐source seismic data that would shed light on the processes associated with rift initiation and evolution. To fully understand the ENAM, it was necessary to acquire a seismic dataset that was both amphibious, spanning the passive margin from the continental interior onto the oceanic portion of the North American plate, and multiresolution, enabling imaging of the sediments, crust, and mantle lithosphere. The ENAM‐CSE datasets were collected on‐ and offshore of North Carolina and Virginia over a series of cruises and land‐based deployments between April 2014 and June 2015. The passive‐source component of the ENAM‐CSE included 30 broadband ocean‐bottom seismometers (OBSs) and 3 onshore broadband instruments. The broadband stations were deployed contemporaneously with those of the easternmost EarthScope Transportable Array creating a trans‐margin amphibious seismic dataset. The active‐source portion of the ENAM‐CSE included several components: (1) two onshore wide‐angle seismic profiles where explosive shots were recorded on closely spaced geophones; (2) four major offshore wide‐angle seismic profiles acquired with an airgun source and short‐period OBSs (SPOBSs), two of which were extended onland by deployments of short‐period seismometers; (3) marine multichannel seismic (MCS) data acquired along the four lines of SPOBSs and a series of other profiles along and across the margin. During the cruises, magnetic, gravity, and bathymetric data were also collected along all MCS profiles. All of the ENAM‐CSE products were made publicly available shortly after acquisition, ensuring unfettered community access to this unique dataset.