Publisher Correction: Coherent diffractive imaging of single helium nanodroplets with a high harmonic generation source

In the original version of this Article, the affiliation for Luca Poletto was incorrectly given as ‘European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Hamburg, Germany’, instead of the correct ‘CNR, Istituto di Fotonica e Nanotecnologie Padova, Via Trasea 7, 35131 Padova, Italy’. This has now been corrected in both the PDF and HTML versions of the Article

Three-Dimensional Shapes of Spinning Helium Nanodroplets

A significant fraction of superfluid helium nanodroplets produced in a free-jet expansion have been observed to gain high angular momentum resulting in large centrifugal deformation. We measured single-shot diffraction patterns of individual rotating helium nanodroplets up to large scattering angles using intense extreme ultraviolet light pulses from the FERMI free-electron laser. Distinct asymmetric features in the wide-angle diffraction patterns enable the unique and systematic identification of the three-dimensional droplet shapes. The analysis of a large dataset allows us to follow the evolution from axisymmetric oblate to triaxial prolate and two-lobed droplets. We find that the shapes of spinning superfluid helium droplets exhibit the same stages as classical rotating droplets while the previously reported metastable, oblate shapes of quantum droplets are not observed. Our three-dimensional analysis represents a valuable landmark for clarifying the interrelation between morphology and superfluidity on the nanometer scale

Deep neural networks for classifying complex features in diffraction images

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns represent a severe problem for data analysis, due to the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but facing different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published in Langbehn et al. (Phys. Rev. Lett. 121, 255301 (2018)) the first application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications and the training process of the deep neural network for diffraction image classification and its systematic benchmarking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during post-processing of large amounts of experimental coherent diffraction imaging data.Comment: Published Version. Github code available at: https://github.com/julian-carpenter/airyne

XUV double-pulses with femtosecond to 650 ps separation from a multilayer-mirror-based split-and-delay unit at FLASH

Extreme ultraviolet (XUV) and X-ray free-electron lasers enable new scientific opportunities. Their ultra-intense coherent femtosecond pulses give unprecedented access to the structure of undepositable nanoscale objects and to transient states of highly excited matter. In order to probe the ultrafast complex light-induced dynamics on the relevant time scales, the multi-purpose end-station CAMP at the free-electron laser FLASH has been complemented by the novel multilayer-mirror-based split-and-delay unit DESC (DElay Stage for CAMP) for time-resolved experiments. XUV double-pulses with delays adjustable from zero femtoseconds up to 650 picoseconds are generated by reflecting under near-normal incidence, exceeding the time range accessible with existing XUV split-and-delay units. Procedures to establish temporal and spatial overlap of the two pulses in CAMP are presented, with emphasis on the optimization of the spatial overlap at long time-delays via time-dependent features, for example in ion spectra of atomic clusters

Melting, bubble-like expansion and explosion of superheated plasmonic nanoparticles

We report on time-resolved coherent diffraction imaging of gas-phase silver nanoparticles, strongly heated via their plasmon resonance. The x-ray diffraction images reveal a broad range of phenomena for different excitation strengths, from simple melting over strong cavitation to explosive disintegration. Molecular dynamics simulations fully reproduce this behavior and show that the heating induces rather similar trajectories through the phase diagram in all cases, with the very different outcomes being due only to whether and where the stability limit of the metastable superheated liquid is crossed.Comment: 17 pages, 8 figures (including supplemental material

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Abbildung der Formen und Dynamik suprafluider Helium-Nanotröpfchen

This thesis addresses the three-dimensional (3D) structure determination of individual nanoparticles or, more precisely, helium nanodroplets, via scattering of extreme-ultraviolet light pulses. In particular, the recent development of free-electron lasers (FELs) delivering intense short-wavelength light pulses of femtosecond duration is a promising prospect for the study of unsupported particles such as proteins, nucleic acids, viruses, and also droplets on the nanometer scale. Motivated by this potential application in imaging of biomolecules, the thesis investigates superfluid helium nanodroplets in an experiment at the FERMI FEL that is focused on the interaction of intense light pulses with matter by analyzing wide-angle scattering patterns. The experiment can be divided in two parts: In the first part, the complete 3D droplet shapes are retrieved from the diffraction patterns, enabling a comparison with theory. Despite the absence of friction in a superfluid, it is shown that the shapes of spinning superfluid helium nanodroplets resemble those of rotating normal liquid drops. Also the evolution of the droplet shapes from spherical to oblate, prolate, and even two-lobed configurations is observed. In the second part of the experiment, scattering images of xenon doped helium nanodroplets are recorded after irradiating the droplets with intense near-infrared laser pulses to study the light induced dynamics. The diffraction patterns indicate density fluctuations in the droplets that occur as the energy of the laser pulse is deposited at the locations of the dopant atoms. The density fluctuations are further explored for two selected cases: (i) A random distribution of the fluctuations when the dopants are also randomly distributed in the droplet, and (ii) a structured distribution of the fluctuations when the dopants accumulate at specific sites, which is probably connected to the occurrence of quantized vortices in the spinning superfluid droplet.Diese Arbeit befasst sich mit der dreidimensionalen Strukturbestimmung einzelner Nanoteilchen, genauer gesagt Helium-Nanotröpfchen, mittels Streuung extrem ultravioletter Lichtpulse. Hier bieten insbesondere die erst seit kurzem verfügbaren Freie-Elektronen-Laser (FEL), mit denen sich intensive Femtosekundenpulse im kurzwelligen Spektralbereich erzeugen lassen, einen vielversprechenden Ansatz, um einzelne Proteine, Nukleinsäuren, Viren und auch Tröpfchen auf der Nanometer-Skala zu untersuchen. Inspiriert von der Idee einzelne Biomoleküle, vor allem auch jene, die sich nicht kristallisieren lassen, direkt abzubilden, widmet sich diese Arbeit der Strukturaufklärung suprafluider Helium-Nanotröpfchen in einem Experiment am FERMI FEL. Durch die Analyse von Weitwinkel-Streubildern einzelner Tröpfchen können zudem Rückschlüsse auf die Wechselwirkung mit intensiven Lichtpulsen gezogen werden. Das Experiment lässt sich im Wesentlichen in zwei Teile gliedern: Im ersten Teil wird aus den Streubildern die dreidimensionale Form der Tröpfchen gewonnen, was einen Vergleich mit theoretischen Gleichgewichtsformen ermöglicht. Obwohl es in einem Suprafluid keinerlei Reibung gibt, zeigt sich, dass rotierende Tröpfchen im suprafluiden und normalflüssigen Zustand sehr ähnliche Formen annehmen. Hierbei lässt sich der Übergang von sphärischen zu oblaten, prolaten und schließlich stark verformten hantelförmigen Tröpfchen beobachten. Im zweiten Teil des Experiments werden lichtinduzierte Dynamiken in den Tröpfchen untersucht. Dazu werden die Helium-Nanotröpfchen mit Xenon dotiert und Streubilder nach Anregung der Tröpfchen mit intensiven, nah-infraroten Laserpulsen aufgenommen. Die beobachteten Streubilder weisen auf Dichtefluktuationen in den Tröpfchen hin, die dadurch entstehen, dass die Energie des Laserpulses an den Orten der Dotanden in die Tröpfchen eingebracht wird. Diese Dichtefluktuationen werden für zwei Fälle genauer untersucht: (i) Eine zufällige Verteilung der Fluktuationen, wenn die Dotanden im Tröpfchen ebenfalls zufällig verteilt sind, und (ii) eine strukturierte Verteilung der Fluktuationen, falls sich die Dotanden an bestimmten Orten sammeln, deren Position vermutlich mit dem Auftreten quantisierter Wirbel in den sich drehenden suprafluiden Tröpfchen zusammenhängt

Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning

Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years, yielding in-situ measurements with ultra-high spatial and temporal resolution. The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images, which are needed for the structure determination of specimens with greater structural variety and dynamic experiments. The size of the datasets, however, represents a monumental problem for their analysis. Here, we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling. By introducing the concept of projection learning, we extend self-supervised contrastive learning to the context of coherent diffraction imaging and achieve a dimensionality reduction producing semantically meaningful embeddings that align with physical intuition. The method yields substantial improvements compared to previous approaches, paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.ISSN:2057-396

X-Ray and XUV Imaging of Helium Nanodroplets

X-ray and extreme ultraviolet (XUV) coherent diffractive imaging (CDI) have the advantage of producing high resolution images with current spatial resolution of tens of nanometers and temporal resolution of tens of femtoseconds. Modern developments in the production of coherent, ultra-bright, and ultra-short X-ray and XUV pulses have even enabled lensless, single-shot imaging of individual, transient, non-periodic objects. The data collected in this technique are diffraction images, which are intensity distributions of the scattered photons from the object. Superfluid helium droplets are ideal systems to study with CDI, since each droplet is unique on its own. It is also not immediately apparent what shapes the droplets would take or what structures are formed by dopant particles inside the droplet. In this chapter, we review the current state of research on helium droplets using CDI, particularly, the study of droplet shape deformation, the in-situ configurations of dopant nanostructures, and their dynamics after being excited by an intense laser pulse. Since CDI is a rather new technique for helium nanodroplet research, we also give a short introduction on this method and on the different light sources available for X-ray and XUV experiments.ISSN:1437-0859ISSN:0303-421

The Scatman: an approximate method for fast wide-angle scattering simulations

Single-shot Coherent Diffraction Imaging (CDI) is a powerful approach to characterize the structure and dynamics of isolated nanoscale objects such as single viruses, aerosols, nanocrystals or droplets. Using X-ray wavelengths, the diffraction images in CDI experiments usually cover only small scattering angles of few degrees. These small-angle patterns repre sent the magnitude of the Fourier transform of the two-dimensional projec tion of the sample’s electron density, which can be reconstructed efficiently but lacks any depth information. In cases where the diffracted signal can be measured up to scattering angles exceeding ∼ 10 ◦ , i.e. in the wide angle regime, three-dimensional morphological information of the target is contained in a single-shot diffraction pattern. However, the extraction of the 3D structural information is no longer straightforward and defines the key challenge in wide-angle CDI. So far, the most convenient approach relies on iterative forward fitting of the scattering pattern using scatter ing simulations. Here we present the Scatman, an approximate and fast numerical tool for the simulation and iterative fitting of wide-angle scat tering images of isolated samples. Furthermore, we publish and describe in detail our Open Source software implementation of the Scatman algo rithm, PyScatman. The Scatman approach, which was alreadin previous works for forward-fitting-based shape retrieval, adopts the Multi-Slice Fourier Transform method. The effects of optical properties are partially included, yielding quantitative results for weakly scattering samples. PyScatman is capable of computing wide-angle scattering pat terns in few milliseconds even on consumer-level computing hardware. The high computational efficiency of PyScatman enables effective data analysis based on model fitting, thus representing an important step to wards a systematic application of 3D Coherent Diffraction Imaging from single wide-angle diffraction patterns in various scientific communities