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. Our investigation reveals that plasmonic nanospheres composed of sodium and aluminum produce a large field intensity enhancement of ≃103-105, boosting the harmonic generation process. Our findings indicate that poor metals hold great promise for advanced extreme ultraviolet nano-sources with potential applications in nano-spectroscopy.

Controlled Growth of Large SiO2 Shells onto Semiconductor Colloidal Nanocrystals: A Pathway Toward Photonic Integration.

The growth of SiO2 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. Indeed, the use of a sequential full-factorial design allows us to carefully tune the growth of SiO2 shells to large values while maintaining a reduced size dispersion. Moreover, we show that the growth of a dielectric shell alone can be beneficial in terms of emission efficiency for the nanocrystal. We also demonstrate that, according to our modeling, the subsequent growth of two shells with increasing refractive index leads to an improved emission efficiency already at a reduced SiO2 sphere radius.

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). The method in this paper utilizes three features extracted from the IMFs-the first difference of time, the first difference of phase, and the normalized energy-for data categorization using support vector machine (SVM) classifiers. Two classifiers were trained for each subject, one for valence and another for arousal. The mean accuracies yielded for valence and arousal were 75.86% and 75.31%, respectively. The results of this study verify previous findings by other researchers that these three features are useful in emotion recognition when applied to previously recorded EEG data, though we add the caveat that subject-specific classifiers are needed instead of generalized, global classifiers.