Resonant third-harmonic generation driven by out-of-equilibrium electron dynamics in sodium-based near-zero index thin films.

We investigate resonant third-harmonic generation in near-zero index thin films driven out-of-equilibrium by intense optical excitation. Adopting the Landau weak coupling formalism to incorporate electron-electron and electron-phonon scattering processes, we derive a novel set of hydrodynamic equations accounting for collision-driven nonlinear dynamics in sodium. By perturbatively solving hydrodynamic equations, we model third-harmonic generation by a thin sodium film, finding that such a nonlinear process is resonant at the near-zero index resonance of the third-harmonic signal.

Harnessing collisional nonlinearity for enhanced harmonic generation by ultraviolet plasmonic nanoparticles.

We investigate the contribution of inelastic electron collisions to nonlinear (NL) dynamics in ultraviolet plasmonic nanoparticles, exploring their potential for harmonic generation. Employing the Landau weak coupling formalism to model radiation-driven electron dynamics in sodium and aluminum, we account for both electron-electron and electron-phonon scattering processes by a set of hydrodynamic equations, which we solve perturbatively to obtain third-order NL susceptibilities. Furthermore, we model high harmonic generation enhanced by localized surface plasmons in nanospheres composed of such poor metals, demonstrating their efficient operation for extreme ultraviolet generation.

Controlled Growth of Large SiO Shells onto Semiconductor Colloidal Nanocrystals: A Pathway Toward Photonic Integration.

The growth of SiO shells on semiconductor nanocrystals is an established procedure and it is widely employed to provide dispersibility in polar solvents, and increased stability or biocompatibility. However, to exploit this shell to integrate photonic components on semiconductor nanocrystals, the growth procedure must be finely tunable and able to reach large particle sizes (around 100 nm or above). Here, we demonstrate that these goals are achievable through a design of experiment approach.

Emotion Recognition for Brain Machine Interface: Non-linear Spectral Analysis of EEG Signals Using Empirical Mode Decomposition.

Emotions are a fundamental part of the human experience but currently there are no methods that can objectively detect and categorize them. This study utilizes the empirical mode decomposition (EMD) method to categorize emotions from encephalography (EEG) recordings. In the past, EMD has proven to be a very useful signal analysis tool because of its ability to decompose nonstationary signals, like those from an EEG, into component signals with varying frequency content called intrinsic mode functions (IMFs).