Outcomes

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Publications view all
from our users
Separation Science and Technology (2017)
One step preparation of ZnFe2O4/Zn5(OH)6(CO3)2 nanocomposite with improved As(V) removal capacity
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Novel adsorbents consisting of ZnFe2O4/Zn5(OH)6(CO3)2 (hydrozincite) nanocomposite materials were studied for efficient As(V) removal from water. Nanocomposites were synthesized by the co-precipitation of Zn and Fe salts in alkaline conditions. Depending on the Zn/Fe molar ratio, a variety of materials was produced with different ZnFe2O4/Zn5(OH)6(CO3)2 contents. The adsorbent’s efficiency for As(V) removal was enhanced proportionally to the percentage of Zn5(OH)6(CO3)2 content. The nanocomposite with 74 ± 7 wt% of Zn5(OH)6(CO3)2 provided a capacity of 18.4 μg As(V)/mg for residual concentration of 10 μg/L (pH 7) which is over twice that of an iron oxy-hydroxide prepared under similar conditions.
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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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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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WP11 - Networking activities for NFFA user community impact and growth
D11.3 - First Annual report on NFFA-EUROPE dissemination activities
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The dissemination activities of the NFFA-Europe project are crucial in the early stage of any Infrastructure EU project, as it has the important task to explain the NFFA-Europe offer to the scientific community. This communication activity consists of many different actions and, with the exception of the dissemination programme, all others have to be considered very succesfull.
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WP11 - Networking activities for NFFA user community impact and growth
D11.5 - First Report on the industry and business development networking meetings
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A strong scientific knowledge base is one of Europe’s traditional key assets, and it has allowed the European Union to become world class in several research fields, such as nanosciences and nanotechnologies. In spite of these merits, the global position of European research is currently being challenged by a rapidly changing research landscape. Simultaneously, European research is faced with the implications of globalisation of markets and industries, digitalisation and new technologies, as well as a need to address societal issues such as an ageing population or climate change.
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