Electron energy-loss spectroscopy of surface plasmon activity in wrinkled gold structures.

The surface plasmon response of a cross-sectional segment of a wrinkled gold film is studied using electron energy loss spectroscopy (EELS). EELS data demonstrate that wrinkled gold structures act as a suitable substrate for surface plasmons to propagate. The intense surface variations in these structures facilitate the resonance of a wide range of surface plasmons, leading to the broadband surface plasmon response of these geometries from the near-infrared to visible wavelengths. The metallic nanoparticle boundary element method toolbox is used to simulate plasmon eigenmodes in these structures. Eigenmode simulations show how the diverse morphology of the wrinkled structure leads to its high spectral complexity. Micron-sized structural features that do not provide interactions between segments of the wrinkle have only a small effect on the surface plasmon resonance response, whereas nanofeatures strongly affect the resonant modes of the geometry. According to eigenmode calculations, different eigenenergy shifts around the sharp folds contribute to the broadband response and infrared activity of these structures; these geometrical features also support higher energy (shorter wavelength) symmetric and anti-symmetric plasmon coupling across the two sides of the folds. It is also shown that additional plasmon eigenstates are introduced from hybridization of modes across nanogaps between structural features in close proximity to each other. All of these factors contribute to the broadband response of the wrinkled gold structures.

Alignment-invariant signal reality reconstruction in hyperspectral imaging using a deep convolutional neural network architecture.

The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments' broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters of the source, signal convolution by the optical transfer function and high frequency noise at once using a single training data set. Here, EELSpecNet performance in reducing noise, and restoring the original reality of the spectra is evaluated for near zero-loss electron energy loss spectroscopy signals in Scanning Transmission Electron Microscopy. EELSpecNet demonstrates to be more efficient and more robust than the currently widely used Bayesian statistical method, even in harsh conditions (e.g. high signal broadening, intense high frequency noise).

Advances in ultrahigh-energy resolution EELS: phonons, infrared plasmons and strongly coupled modes.

Nowadays, sub-50 meV atom-wide electron probes are routinely produced for electron energy loss spectroscopy in transmission electron microscopes due to monochromator technology advances. We review how gradual improvements in energy resolution enabled the study of very low-energy excitations such as lattice phonons, molecular vibrations, infrared plasmons and strongly coupled hybrid modes in nanomaterials. Starting with the theoretical framework needed to treat inelastic electron scattering from phonons in solids, we illustrate contributions in detecting optical surface phonons in photonic structures. We discuss phonon mapping capabilities in real and reciprocal space, and the localized phonon response near nano-/atomic-scale structural features. We also survey the progress of aloof spectroscopy in studying vibrations in organic materials and applications in measuring local temperature and photonic density of states in single nanostructures using phonon scattering. We then turn towards studies on infrared plasmons in metals and semiconductors. Spectroscopy analyses now extend towards probing extremely complex broadband platforms, the effects of defects and nanogaps, and some far-reaching investigations towards uncovering plasmon lifetime and 3D photonic density of states. In doped semiconductors, we review research on the use of the electron probe to correlate local doping concentration and atomic-scale defects with the plasmonic response. Finally, we discuss advances in studying strong coupling phenomena in plasmon-exciton and plasmon-phonon systems. Overall, the wealth of information gained extends our knowledge about nanomaterial properties and elementary excitations, illustrating the powerful capabilities of high-energy resolution scanning transmission electron microscopy-electron energy loss spectrometry.