Marco Tosca

Laser-driven fusion reactions for the development of bright particle sources or the production of short-lived isotopes remain an active research theme. We propose a comprehensive analysis and a fast modeling approach to highlight optimization strategies on the fusion yields stemming from advanced target designs. Particle-in-cell simulations were supplemented with an inline Monte Carlo approach to evaluate the proton–boron fusion events under direct laser illumination for planar and spherical target geometries. The time evolution and the spatial localization of the nuclear reactions are systematically compared to reveal a multi-phase mechanism in the folded geometries. For both architectures, a shared initial phase of fusion occurs in the laser spot vicinity, following from ions accelerated by the laser radiation pressure forward into the target. Meanwhile, the circulation of hot electrons issued from the laser-plasma interaction leads to the development of sheath fields along the target walls. In a closed geometry, the corresponding accelerated ions may be directed toward dense parts of the target, significantly increasing the number of created α-particles. We analyze the energy distribution of the ion populations issued from the different acceleration mechanisms and assess the corresponding efficiencies in the fusion processes. The use of a double cavity allows for further exploitation of the sheath field acceleration, raising the α yield almost an order of magnitude higher than for the planar case.

Developing novel memristive systems aims to implement key principles of biological neuron assemblies – plasticity, adaptivity, and self‐organization – into artificial devices for parallel, energy‐efficient computing. Solid‐state memristive devices, such as crossbar arrays and percolated nanoparticle (NP) networks, already demonstrate these properties. However, closer similarity to neural networks is expected from liquid‐state systems, including polymer melts, which remain largely unexplored. Here, the resistive switching in silver/poly(ethylene glycol) (Ag/PEG) nanofluids, prepared by depositing gas‐aggregated Ag NPs into PEGs of varying molecular mass, is investigated. These systems form long‐range conductive NP bridges with reconfigurable resistance states in response to an electric field. The zeta‐potential of Ag NPs and molecular mobility of PEG determine the prevalence of low resistance (ohmic) state, high resistance states (poor conductance) or intermediate transition states governed by space‐charge‐limited conduction or electron tunneling. The occurrence of these states is given by the interparticle gaps, which are determined by the conformation of PEG molecules adsorbed on the NPs. It is presented, for the first time, an equivalent circuit model for the Ag/PEG system. These findings pave the way to adopt polymer melts as matrices for neuromorphic engineering and bio‐inspired electronics.

Novel targets were implemented in the beam-driven (in-target) proton-boron fusion with beneficial characteristics (chemical composition and density) compared to the commonly used boron nitride. A fusion-generated alpha particle flux of up to ( 5 × 10 7 sr − 1 J − 1 ) was generated via the proton-boron fusion reactions when irradiating the targets with a moderate laser system ( 10 J , 800 fs , 10 19 W cm − 2 ). Normalized by laser energy, the alpha particle flux generated in this experiment is comparable with the highest alpha particle yields reported in the literature. We present experimental results with supporting simulations and calculations of the expected alpha particle yield.

High‐temperature plasmonics deals with optically active nanostructures that can withstand high temperatures. A conventional approach relying on standalone noble metal nanoparticles fails to deliver refractory plasmonic nanomaterials, and an alternative route envisions metal nitrides. The main challenge remains the development of advanced synthesis techniques and the insight into thermal stability under real‐life application conditions. Here, hafnium nitride nanoparticles (HfN NPs) can be produced by gas aggregation using reactive magnetron sputtering, a technique with a small environmental footprint are shown. As‐deposited NPs are of 10 nm mean size and consist of stoichiometric, crystalline fcc HfN. They are characterized by optical absorption below 500 nm caused by interband transitions and in the red/near‐infrared (NIR) region due to intraband transitions and localized surface plasmon resonance (LSPR). The optical response can be engineered by tuning the NP composition as predicted by finite‐difference time‐domain (FDTD) calculations. Going beyond the state‐of‐the‐art, the HfN NP thermal stability is focued under ultrahigh vacuum (UHV) and in air. During UHV annealing to 850 °C, the NPs retain their morphology, chemical and optical properties, which makes them attractive in space mission and other applications. During air annealing to 800 °C, HfN NPs remain stable until 250 °C, which sets a limit for air‐mediated use.

An experimental platform for laser-driven ion (sub-MeV) acceleration and potential applications was commissioned at the HiLASE laser facility. The auxiliary beam of the Bivoj laser system operating at a GW level peak power (~10 J in 5–10 ns) and 1–10 Hz repetition rate enabled a stable production of high-current ion beams of multiple species (Al, Ti, Fe, Si, Cu, and Sn). The produced laser–plasma ion sources were fully characterized against the laser intensity on the target (10¹³–10¹⁵ W/cm²) by varying the laser energy, focal spot size, and pulse duration. The versatility and tuneability of such high-repetition-rate laser–plasma ion sources are of potential interest for user applications. Such a statistically accurate study was facilitated by the large amount of data acquired at the high repetition rate (1–10 Hz) provided by the Bivoj laser system.

Sputtering of Zr in Ar/N ₂ produces stoichiometric, crystalline, conductive, and plasmonic ZrN nanoparticles that can be directly loaded into liquids.

Conventional computation techniques face challenges of deviations in Moore's law and the high‐power consumption of data‐centric computation tasks. Neuromorphic engineering attempts to overcome these issues by taking inspiration from neuron assemblies, ranging from distributed synaptic plasticity through orchestration of oscillator‐like action potential toward avalanche dynamics. Although solid networks of nanoparticles (NPs) are proven to replicate fingerprints of criticality and brain‐like dynamics, the aspect of dynamic spatial reconfigurations in the connectivity of networks remains unexplored. In this work, Ag/poly(ethylene glycol) (PEG) nanofluids are demonstrated as potential systems to mimic the spatio‐temporal reconfiguration of network connections. The nanofluids are prepared by directly loading Ag NPs from the gas aggregation cluster source into liquid PEG. The NPs exhibit a negative zeta potential in PEG; if the potential difference is applied between two electrodes submerged in this nanofluid, the NPs migrate toward the anode, accumulate in its vicinity, and form a conductive path. Spikes of electric current passing through the path are detected, accompanied by resistive switching phenomena, similar to the random switching dynamics in solid NPs networks. The unique behavior of Ag/PEG nanofluids makes them promising for the realization of spatio‐temporal reconfigurations in network topologies with the potential to transition to 3D.