Outcomes

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Publications view all
our research
Optics Letters V. 42, 21, 4327 (2017)
Tunable kinoform x-ray beam splitter
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We demonstrate an x-ray beam splitter with high performances for multi-kilo-electron-volt photons. The device is based on diffraction on kinoform structures, which overcome the limitations of binary diffraction gratings. This beam splitter achieves a dynamical splitting ratio in the range 0–99.1% by tilting the optics and is tunable, here shown in a photon energy range of 7.2–19 keV. High diffraction efficiency of 62.6%, together with an extinction ratio of 0.6%, is demonstrated at 12.4 keV, with angular separation for the split beam of 0.5 mrad. This device can find applications in beam monitoring at synchrotrons, at x-ray free electron lasers for online diagnostics and beamline multiplexing and, possibly, as key elements for delay lines or ultrashort x-ray pulses manipulation.
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our research
Scientific Reports 7, 13282 (2017)
Neural Network for Nanoscience Scanning Electron Microscope Image Recognition
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In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.
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our research
Phys. Chem. Chem. Phys., (2017)
Imaging on-surface hierarchical assembly of chiral supramolecular networks
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The bottom-up assembly of chiral structures usually relies on a cascade of molecular recognition interactions. A thorough description of these complex stereochemical mechanisms requires the capability of imaging multilevel coordination in real-time. Here we report the first direct observation of hierarchical expression of supramolecular chirality at work, for 10,10′-dibromo-9,9′-bianthryl (DBBA) on Cu(111). Molecular recognition first steers the growth of chiral organometallic chains and then leads to the formation of enantiopure islands. The structure of the networks was determined by noncontact atomic force microscopy (nc-AFM), while high-speed scanning tunnelling microscopy (STM) revealed details of the assembly mechanisms at the ms time-scale. The direct observation of the chirality transfer pathways allowed us to evaluate the enantioselectivity of the interchain coupling.
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Deliverables view all
WP1 - Management
D1.3 - Setup and implementation of the TA and evaluation procedures
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NFFA-Europe offers to European and Third Country1 scientists from both academia and industry the possibility to carry out comprehensive projects for multidisciplinary research at the nanoscale. Activities are performed in six different types of Installations: - Lithography and nano-patterning (Litho) - Growth and synthesis (Growth) - Theory and Simulation (Theory) - Structural and Morphological nano-characterisation (SM Charact.) - Electronic and Chemical nano-characterisation (EC Charact.) - Magnetic, Optical and Electric nano-characterisation (ME Charact.) Each Installation includes laboratories located in different NFFA-EU sites; furthermore, when needed, limited2 access to co-located Large-Scale Facilities for Fine Analysis is offered as part of the access to Litho, or SM, EC or ME nano-characterisation. NFFA-Europe proposals necessarily include access to more than one type of Installation (e.g. Litho and Growth, Growth and Theory, SM Charact. and EC Charact., etc.) and cannot be limited to Fine Analysis only. Whenever possible access will be granted in a single NFFA-Europe site for all research steps. Access to more than one site for a given proposal will be considered only when technically or scientifically justified.
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WP1 - Management
D1.1 - Internal test of NFFA-Europe Website
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This deliverable describes the results achieved within task 1.5 “Communication”, and is also connected to the dissemination purposes of the project (WP11). The work done aimed at setting up the main information and functionalities of the website as a Single Entry Point (SEP) to find out about the project and access the offer of tools made available through NFFA-Europe research infrastructure.
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WP11 - Networking activities for NFFA user community impact and growth
D11.2 - Draft metadata standard for nanoscience data
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This document contains the NFFA Deliverable D11.2 “Draft metadata standard for nanoscience data” due in M6. It describes the approach, the relevant information management practices, standards and recommendations taken into account, as well as empirical research done by NFFA JRA3 for the purpose of metadata design, and then suggests a draft recommendation for NFFA metadata model. Having a common and well-defined metadata model is essential for human-to-human, human-to-machine and machine-to-machine interoperability in NFFA. Such a model will support the development of Information and Data management Repository Platform (IDRP) and will contribute to structured business analysis across the project. In return, the model will get further inputs from the continuing IT architecture design and business analysis. In addition to the NA activities, the deliverable has been discussed and validated through a number of conference calls and electronic communication in JRA3, as well as in the course of a dedicated face-to-face meeting in Abingdon, UK, in February 2016. The metadata model here proposed will be further validated, updated and detailed through the NFFA project activities within and beyond JRA3. It will be then finalised in D11.14 “Final metadata standard for nanoscience data” in M30. Approach
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