Comparative Study on the Uniform Energy Deposition Achievable via Optimized Plasmonic Nanoresonator Distributions

Plasmonic nanoresonators of core–shell composition and nanorod shape were optimized to tune their absorption cross-section maximum to the central wavelength of a short laser pulse. The number density distribution of randomly located nanoresonators along a laser pulse-length scaled target was numerically optimized to maximize the absorptance with the criterion of minimal absorption difference between neighboring layers illuminated by two counter-propagating laser pulses. Wide Gaussian number density distribution of core–shell nanoparticles and nanorods enabled to improve the absorptance with low standard deviation; however, the energy deposited until the overlap of the two laser pulses exhibited a considerable standard deviation. Successive adjustment resulted in narrower Gaussian number density distributions that made it possible to ensure almost uniform distribution of the deposited energy integrated until the maximal overlap of the two laser pulses. While for core–shell nanoparticles the standard deviation of absorptance could be preserved, for the nanorods it was compromised. Considering the larger and polarization independent absorption cross-section as well as the simultaneously achievable smaller standard deviation of absorptance and deposited energy distribution, the core–shell nanoparticles outperform the nanorods both in optimized and adjusted nanoresonator distributions. Exception is the standard deviation of deposited energy distribution considered for the complete layers that is smaller in the adjusted nanorod distribution. Optimization of both nanoresonator distributions has potential applications, where efficient and uniform energy deposition is crucial, including biomedical applications, phase transitions, and even fusion.

     

Mathematical model for the self-organization of neural networks

Mutual inhibition between neurons combined with a learning principle similar to that proposed by Hebb is shown to secure a powerful selforganizing property for neural networks. Numerical analysis reveals that the system investigated always organizes itself into the same final state from any arbitrarily chosen initial state.