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Register a new userSingle-shot electron imaging of dopant-induced nanoplasmas
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We present single-shot electron velocity-map images of nanoplasmas generated from doped helium nanodroplets and neon clusters by intense near-infrared and mid-infrared laser pulses. We report a large variety of signal types, most crucially depending on the cluster size. The common feature is a two-component distribution for each single-cluster event: A bright inner part with nearly circular shape corresponding to electron energies up to a few eV, surrounded by an extended background of more energetic electrons. The total counts and energy of the electrons in the inner part are strongly correlated and follow a simple power-law dependence. Deviations from the circular shape of the inner electrons observed for neon clusters and large helium nanodroplets indicate non-spherical shapes of the neutral clusters. The dependence of the measured electron energies on the extraction voltage of the spectrometer indicates that the evolution of the nanoplasma is significantly affected by the presence of an external electric field. This conjecture is confirmed by molecular dynamics simulations, which reproduce the salient features of the experimental electron spectra.
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Helium nanodroplets irradiated by intense near-infrared laser pulses ignite and form highly ionized nanoplasmas even at laser intensities where helium is not directly ionized by the optical field, provided the droplets contain a few dopant atoms. We present a combined theoretical and experimental study of the He nanoplasma ignition dynamics for various dopant species. We find that the efficiency of dopants to ignite a nanoplasma in helium droplets strongly varies and mostly depends on (i) the pick-up process, (ii) the number of free electrons each dopant donates upon ionization, and remarkably, (iii) by the hitherto unexplored effect of the dopant location in or on the droplet.
Helium (He) nanodroplets irradiated by intense near-infrared laser pulses form a nanoplasma by avalanche-like electron impact ionizations even at lower laser intensities where He is not directly field ionized, provided that the droplets contain a few dopant atoms which provide seed electrons for the electron impact ionization avalanche. In this theoretical paper on calcium and xenon doped He droplets we elucidate the mechanism which induces ionization avalanches, termed ignition. We find that the partial loss of seed electrons from the activated droplets starkly assists ignition, as the Coulomb barrier for ionization of helium is lowered by the electric field of the dopant cations, and this deshielding of the cation charges enhances their electric field. In addition, the dopant ions assist the acceleration of the seed electrons (slingshot effect) by the laser field, supporting electron impact ionizations of He and also causing electron loss by catapulting electrons away. The dopants ability to lower the Coulomb barriers at He as well as the slingshot effect decrease with the spatial expansion of the dopant, causing a dependence of the dopants ignition capability on the dopant mass. Here, we develop criteria (impact count functions) to assess the ignition capability of dopants, based on (i) the spatial overlap of the seed electron cloud with the He atoms and (ii) the overlap of their kinetic energy distribution with the distribution of Coulomb barrier heights at He. The relatively long time delays between the instants of dopant ionization and ignition (incubation times) for calcium doped droplets are determined to a large extent by the time it takes to deshield the dopant ions.
We demonstrate ultrafast resonant energy absorption of rare-gas doped He nanodroplets from intense few-cycle (~10 fs) laser pulses. We find that less than 10 dopant atoms ignite the droplet to generate a non-spherical electronic nanoplasma resulting ultimately in complete ionization and disintegration of all atoms, although the pristine He droplet is transparent for the laser intensities applied. Our calculations at those intensities reveal that the minimal pulse length required for ignition is about 9 fs.
High intensity XUV radiation from a free-electron (FEL) was used to create a nanoplasma inside ammonia clusters with the intent of studying the resulting electron-ion interactions and their interplay with plasma evolution. In a plasma-like state, electrons with kinetic energy lower than the local collective Coulomb potential of the positive ionic core are trapped in the cluster and take part in secondary processes (e.g. electron-impact excitation/ionization and electron-ion recombination) which lead to subsequent excited and neutral molecular fragmentation. Using a time-delayed UV laser, the dynamics of the excited atomic and molecular states are probed from -0.1 ps to 18 ps. We identify three different phases of molecular fragmentation that are clearly distinguished by the effect of the probe laser on the ionic and electronic yield. We propose a simple model to rationalize our data and further identify two separate channels leading to the formation of excited hydrogen.
Single-shot X-ray imaging of short-lived nanostructures such as clusters and nanoparticles near a phase transition or non-crystalizing objects such as large proteins and viruses is currently the most elegant method for characterizing their structure. Using hard X-ray radiation provides scattering images that encode two-dimensional projections, which can be combined to identify the full three-dimensional object structure from multiple identical samples. Wide-angle scattering using XUV or soft X-rays, despite yielding lower resolution, provides three-dimensional structural information in a single shot and has opened routes towards the characterization of non-reproducible objects in the gas phase. The retrieval of the structural information contained in wide-angle scattering images is highly non-trivial, and currently no efficient rigorous algorithm is known. Here we show that deep learning networks, trained with simulated scattering data, allow for fast and accurate reconstruction of shape and orientation of nanoparticles from experimental images. The gain in speed compared to conventional retrieval techniques opens the route for automated structure reconstruction algorithms capable of real-time discrimination and pre-identification of nanostructures in scattering experiments with high repetition rate -- thus representing the enabling technology for fast femtosecond nanocrystallography.
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