dataesr/scientific-paragraphs-categorization huggingface dataset:
Microstructural Characterization of the TiNbTaMoZr-Based HEA. X-ray diffraction (XRD) patterns of the as-synthesized pTNTMZxh powders and the as-sintered sTNTMZ_y • C specimens were recorded using a PANalytical X'Pert Pro instrument (Malvern Panalytical Ltd, Malvern, UK) equipped with a Bragg-Brentano θ/θ geometry, a Cu Kα radiation source (45 kV, 40 mA), a secondary Kβ filter and an X'Celerator detector. The XRD patterns were obtained by scanning between 20 and 120 • of the 2theta degree with 0. 03 • steps and a counting time of 800 s•step -1. The free-access Crystallography Open Database (COD) was employed to elucidate the phases developed with the corresponding structures and space-group symmetries (SGSs). A lanthanum hexaboride, LaB6 (Standard Reference Material 660b, NIST) pattern was used to calibrate the positions of the diffraction lines.
The high-resolution electron microscopy (HREM) analysis of the samples was performed under the same magnification and the same defocus to better understand the microstructures of the amorphous state. Figure  2  shows the obtained micrographs studied for all compounds (i. e. , S1 (a), S2 (b), S3 (c), and S4 (d)). We observed that two compounds (i. e. , S1 and S2) were very stable under the beam, while the other two (i. e. , S3 and S4) were slightly unstable after ~1 min of TEM. To avoid any artefact in the HRTEM images due to beam damage, data from each sample were collected under the same electron dose condition, far below the critical dose of the material. The images corresponding to samples S1 and S2 showed an amorphous microstructure, while those of S3 and S4 showed small changes compared to S1 and S2, since the presence of Si-Si ordering in a SRO with different arrangements of the Si-Si domains was observed (see enlarged images inserted in Figure  2c, d ).
g. turbulent transport modelling  [22] , and to make empirical correlations from large experimental databases, e. g. disruption prediction  [23]. A neural network with two fully connected layers of 30 sigmoid neurons has been recently developed in the Tore Supra geometry to achieve fast SXR inversions, as depicted in Fig. 1 , and trained with a large database of synthetic profiles  [24]. The input layer is composed of 69 neurons, corresponding to the number of SXR channels used for the inversion and of an output layer of 2500 neurons to produce 50 9 50 pixels tomograms. The training loss that assesses the network performances is defined by the mean square error cost function: C w; b ð Þ ¼ 1 2n X x y x ð Þ À a x; w; b ð Þ ½ 2 ð4Þ where w; b ð Þ denotes the weights and biases of each neuron, x; y x ð Þ ð Þ represent a training sample, i.
However, the emissivity error can locally peak from 15% to up to 40% as shown in Fig. 3g-i ), which could be detrimental for the SXR analysis of impurities. The reconstruction error could be further reduced by optimizing the network parameters (number of hidden layers and neurons per layer) and the training process, e. g. using the dropout method. The relatively high level of error observed on Fig. 3  is the drawback of the simplicity of fully connected layers as compared with deeper de-convolutional networks, for which the structure is specifically adapted for image recognition and tomography tasks  [19, 27]. The main advantage of the neural network approach is that, once properly train with a synthetic or experimental database, the neural network can perform inversions in a time \ 0. 1 ms, two orders of magnitude faster than Tikhonov regularization and compatible with real-time control.
Two options can be considered while applying a neural network to experimental data. The first option is to directly train the neural network on a database of experimental tomograms already obtained from another method, typically using Tikhonov regularization, like in  [19]. This option allows performing reconstructions much faster with respect to the traditional regularization, at the cost of an additional error induced by the neural network, but requires   4 ), after # epochs of the training process the prior existence of a reliable and complete experimental database. The second option is to train the neural network on a synthetic database in a first step, and in a second step to apply it on experimental data. The advantage of such an approach is to be able to perform the tomographic task ab initio, for instance from the beginning of tokamak operations.
In this section, we show a set of three simulations of the acquired SXR spectrum by the GEM cameras in the case of a standard WEST Physics basis with heating power of 12 MW, I p = 0. 6 MA including W impurities  [37]. The SXR emissivity is modelled as follows: e SXR ¼ n 2 e L H T e ð Þ þ c W L W T e ; D; V ð Þ ½ ð 5Þ with n e , T e the electron density and temperature and where L H and L W denote the Hydrogen and W cooling factors, respectively. The W spectral properties, as well as recombination and ionization coefficients of W ions are obtained from the open ADAS database  [38]. The scenario includes the possibility to choose the diffusion and convective W transport coefficients (D,V) to modify the W ionization equilibrium. The Fig. 5a  reports the synthetic GEM spectrum obtained for the three scenarios considered: (1) pure plasma hydrogen plasma, i.
Conclusion In this paper, some recent studies related to the development of X-ray synthetic diagnostic tools have been presented, in the prospect of impurity transport monitoring. First, the potential of neural networks to perform tomographic inversions with real-time capabilities was demonstrated based on a synthetic database in the geometry of the Tore Supra SXR diagnostic. A good capability of the network to learn and reproduced the emissivity patterns was found with 2% of Gaussian noise in the measurements, although the local reconstruction error can peak from 15% to up to 40%. Secondly, a GEM synthetic diagnostic was applied in a WEST scenario to illustrate the influence of plasma parameters, in particular the tungsten concentration level and transport effects, on the acquired spectrum and the possibility to disentangle the different contributions to the SXR spectrum.
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Acknowledgements We would like to thank  Ludovic Josien ,  Laure Michelin  and  Gautier Schrodj  for their assistance with the scanning electron microscopy, the X-ray diffraction and thermogravimetric analysis, respectively. Funding from the  Centre National d'Etudes Spatiales (CNES) , the company  Zéphir Alsace  and  Institut Universitaire de France (IUF)  is gratefully acknowledged. We would also like to thank  IMCD  for supplying the commercial silicone resin SILRES ® MP 50 E.
Kinetic Analysis Kinetic analysis and modeling of the degradation of the samples were made using a Kinetics Neo software package developed by Netzsch Company. The principle has been discussed by Opfermann in  [6]  and here we only briefly remind the reader of the basic concepts of the method. For kinetic analysis, it is assumed that the material decomposes according to Equation (1): A solid → B solid or liquid + C gas (1) The rate expression de/dt, where e is the concentration of educt (reactant), is assumed to be defined by Equation (2): de dt = k(T) × f(e, p) (2) where k is the kinetic constant, p is the concentration of the product, k = Aexp(-E/RT) according to the Arrhenius law, A is the frequency factor, E is the activation energy and f(e,p) is the so-called "reaction equation" or in the case of TGA, the "reaction model".
There are several reaction types that can be attributed to a decomposition step. The typical homogenous reactions and classic solid reactions are listed in Table  1. F1 f = (1 -α) Reaction of 1st order F2 f = (1 -α) 2 Reaction of 2nd order Fn f = (1 -α) n Reaction of nth order R2 f = 2(1 -α) 1 2 Two-dimensional phase boundary R3 f = 3(1 -α) 2 3 Three-dimensional phase boundary D1 f = 1 2 • 1 α One-dimensional diffusion D2 f = - 1 ln((1-α)) Two-dimensional diffusion D3 f = 3 2 • (1-α) 2 3 1-(1-α) 1 3 Three-dimensional diffusion Jander's type D4 f = 3 2 • 1 ((1-α) -1 3 -1) Three-dimensional diffusion Ginstling-Brounstein type B1 f = (1 -α)• α Prout-Tompkins equation Bna f = (1 -α) n •α Kcat Expanded Prout-Tompkins equation C1 f = (1 -α)•(1 + K cat •α) Reaction of 1st order with autocatalysis by product Cn f = (1 -α) n •(1 + K cat •α) Reaction of nth order with autocatalysis by product Cnm f = (1 -α)ˆn•(1 + K cat •α m ) Reaction of nth order with m-Power autocatalysis by product A2 f = 2(1 -α)•[-ln(1 -α)] 1 2 Two-dimensional nucleation according to Avrami A3 f = 3(1 -α)•[-ln(e)] 2 3 Three-dimensional nucleation according to Avrami An f = n•(1 -α)•[-ln((1 -α))] n-1 n n-dimensional nucleation according to Avrami-Erofeev The models were optimized using the KineticsNeo software (Netzsch).
Calculational: Ab initio calculations were performed by using density functional theory (DFT) within the Generalized Gradient Approximation (GGA) including van der Waals corrections as implemented in the Vienna ab-initio Simulation Package (VASP)  [8, 9]. The Perdew-Burke-Ernzerhof (PBE)  [10]  with van der Waals-D2 functional  [11]  was used to describe the exchange-correlation interaction. The core electrons were described by the projected augmented wave (PAW) method  [12]. The plane-wave basis set was restricted to a cut-off energy of 400 eV. The HOPG surface was modeled by a four-layer slab with 56 carbon atoms per layer in an ABAB stacking of graphene within periodic boundary conditions, keeping the bottom two layers fixed. The vacuum region is as thick as 2. 0 nm. The entire system; silicene on top of graphite, was fully relaxed by a conjugate gradient method until the forces acting on each atom were less than 0.
ACKNOWLEDGMENTS We are most thankful to  Dr. Andrew Mayne  who shared his in-depth understanding of quantum interferences with us in several exciting and encouraging discussions. H. V. , F. J. , and C. L. gratefully acknowledge the  HPC centers of IDRIS  (Grant  A004-090642 ) and CERMM for computational resources. F. J. acknowledges the  Hariri Foundation for Sustainable Human Development  for the scholarship that was awarded to her during her PhD studies. We would like to express our most sincere thanks to  Prof. Chih-Piao Chuu  for having kindly provided us with the structure files of his most stable silicene configurations. M. D. D. , M. S. and P. C. acknowledge the  European Community  for the HORIZON 2020 MSC- RISE  Project  DiSeTCom  ( GA 823728 ). Many thanks are also due to  Dr. James Creel  for the careful reading of the present manuscript.
Characterizations The X-ray diffraction (XRD) patterns were performed on a (RU-200B) (Rigaku, Tokyo, Japan) X-ray diffractometer using Cu Kα radiation (wavelength = 0. 15405 nm) with a Ni filter. The tube current was 100 mA with a tube voltage of 40 kV. The 2θ angular region between 10 and 80 • was explored at a scan rate of 5 • /min. For all the XRD analysis, the resolution in the 2θ scans was kept at 0. 02 •. Initial transmission electron microscopy (TEM) measurements to confirm morphology were performed using JEOL JEM-1400 (Jeol Inc. , Peabody, MA, USA) equipped with Lanthanum-hexaboride (LaB 6 ) filament and operated at an acceleration voltage 120 kV and data were collected on the Gatan Ultrascan 1000 CCD camera (Thermo Fisher Scientific, Hillsboro, OR, USA). Subsequent measurements were performed using FEI Tecnai F30 Twin microscope (Thermo Fisher Scientific, Hillsboro, OR, USA) equipped with analytical spectrometers (Bruker Quantax 400 Silicon Drift Detector or Quantum 963 Gatan Image filter (GIF)) to get high magnified TEM images.
After treating the above product with hydrogen peroxide and acetone in a sequential process, the AFM shows a tubular structure with a pattern of bundles as in Figure  2c, d. It is important to mention that H 2 O 2 solely failed to decompose the oil materials. Nevertheless, the synthesized CNTs after sequential treatment with H 2 O 2 followed by acetone has shown a descent evidence of having CNTs structure with a length of 0. 8-2. 1 µm with a tubular structure. Materials 2020, 13, x FOR PEER REVIEW 4 of 9 using JEOL JEM-1400 (Jeol Inc, Peabody, MA, USA) equipped with Lanthanum-hexaboride (LaB6) filament and operated at an acceleration voltage 120 kV and data were collected on the Gatan Ultrascan 1000 CCD camera (Thermo Fisher Scientific, Hillsboro, OR, USA). Subsequent measurements were performed using FEI Tecnai F30 Twin microscope (Thermo Fisher Scientific, Hillsboro, OR, USA)equipped with analytical spectrometers (Bruker Quantax 400 Silicon Drift Detector or Quantum 963 Gatan Image filter (GIF)) to get high magnified TEM images.
Consumer and patient protection measures are at the heart of this new regulatory system. The main benefits for all consumers and patients are to give better protection in terms of public health and patient safety. For example, for high-risk devices, stricter pre-market control is required. A new EU database on medical devices (EUDAMED) is also created and will propose a continuing view of the lifecycle of all products being available on the EU market. An identification system based on a unique device identifier (UDI) will optimize traceability of medical devices. Finally, in the case of defective products, a new financial mechanism should allow patients to be effectively compensated. Overall, this EU update aims to increase the credibility and reputation of the oversight system after the occurrence of a few incidents (e.
They have to assess the dangers and risks that the substance may present. This information is sent to ECHA through a registration dossier. Registration is based on the principle "one substance, one registration". Registration therefore applies to substances and related such as substances contained in mixtures and to certain cases of substances listed in articles. In order to complement REACH, regulation (EC) No 1272/2008 on Classification, Labelling and Packaging (CLP) was issued and entered in force on 20 January 2009  [80]. This regulation harmonized the criteria for the classification of substances and mixtures, and the rules on labelling and packaging for hazardous substances and mixtures. It also aims at establishing a classification and labeling inventory of substances. Over the years, other chemicals are also listed in the ECHA database such as "Intermediate Use Only".
At the last update (13 November 2020), the database contained 23,032 unique substances and contains information from 101,006 dossiers. Metals Co (231-158-0) and Cr (231-157-5) were registered on 17 September 2010 and 8 September 2010, respectively  [81]. This CLP legislation defined new CMR categories (1A, 1B and 2). Only substances classified as CMR of category 1A/1B are restricted by REACH. Since 2009 and the publication of the first Adaptation to Technical Progress (ATP), many updates and corrections have been made. As previously mentioned, the RAC proposed a reclassification of metal Co as a CMR 1B substance  [73]. The 14th ATP to the CLP Regulation was published in the EU official journal on 18 February 2020, including the harmonized classification for cobalt metal as CMR (Annex VI to CLP, in force from 9 September 2021)  [82].
Co-Existence and Grace Period 2017-2025 As with the introduction of the REACH regulation, the implementation of the MDR (EU) 2017/745 is being carried out through a transition period of four years (26 May 2017-26 May 2021). During this period, medical devices can be evaluated under either MDD or MDR legislation. After this point in time, all medical devices must only be approved according to the MDR process. Starting in March 2021, the the first module of EUDAMED for the registration of the economic operators (manufacturers, authorized representatives and importers) will be available. It will allow these operators to obtain the famous Single Registration Number (SRN) which will give access to the other functionalities of the database. This SRN will also be requested by the NB. In 2022, the massive database EUDAMED should be fully effective.
Drug Internalization Studies 0. 5 × 10 6 1301 cells were cultivated in total volume 100 µL in flat-bottomed 48-well cell culture plates (TPP, Switzerland) for 4 h. Then, water solutions of amphiphilic dendron (5 µM), doxorubicin (3 µM) or doxorubicin-loaded dendrimersomes (doxorubicin content 3 µM) were added. The equal volume of PBS was added to control cells. After cultivation, cells were collected, washed once with PBS, then 50 µL of acidic glycine solution were added, gently pipetted for 30 s and washed again with PBS. Flow cytometric analysis was performed on a FACSCanto II cytometer (BD Biosciences, San Jose, CA). The analysis was made in FITC and PE channels. 15,000 to 50,000 events per sample were acquired and analysed using FACSDiva 6. 1. 2 software. 1301 cells were incubated with blank dendrimersomes, doxorubicin and doxorubicin-loaded dendrimersomes (6.
Drug Internalization Studies 0. 5 × 10 6 1301 cells were cultivated in total volume 100 µL in flat-bottomed 48-well cell culture plates (TPP, Switzerland) for 4 h. Then, water solutions of amphiphilic dendron (5 µM), doxorubicin (3 µM) or doxorubicin-loaded dendrimersomes (doxorubicin content 3 µM) were added. The equal volume of PBS was added to control cells. After cultivation, cells were collected, washed once with PBS, then 50 µL of acidic glycine solution were added, gently pipetted for 30 s and washed again with PBS. Flow cytometric analysis was performed on a FACSCanto II cytometer (BD Biosciences, San Jose, CA, USA). The analysis was made in FITC and PE channels. 15,000 to 50,000 events per sample were acquired and analysed using FACSDiva 6. 1. 2 software. 1301 cells were incubated with blank dendrimersomes, doxorubicin and doxorubicin-loaded dendrimersomes (6.
Ice-cold 70% ethanol solution was used for the necrosis induction control; and doxorubicin-treated cells were taken as the apoptosis induction control. For cell staining, the solutions of FITC Annexin V (5 µL per probe with 0. 25-1. 0 × 10 7 cells) and 7-AAD (5 µL per probe with 0. 25-1. 0 × 10 7 cells) were added. Cells were gently pipetted and incubated for 15 min at room temperature in the dark. Then, 400 µL of Annexin V Binding Buffer were added to each tube. Flow cytometric analysis was performed on a FACSCanto II cytometer (BD Biosciences, San Jose, CA, USA). 40,000 to 100,000 events per sample were acquired and analyzed using FACSDiva software. All experiments were run in triplicates.
This study was supported by RFBR grant No. 18-33-20109, grant of the President of the Russian Federation No. MK-2278. 2019. 4, by MINECO grant CTQ-2017-85224-P, Consortium NANODENDMED-II-CM (B2017/BMD-3703) and IMMUNOTHERCAN-CM (B2017/BMD3733). The project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 844217. This article is based upon work from COST Action CA 17140 "Cancer Nanomedicine from the Bench to the Bedside" supported by COST (European Cooperation in Science and Technology). CIBER-BBN is an initiative funded by the VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.
Funding: This study was supported by RFBR grant No. 18-33-20109, grant of the President of the Russian Federation No. MK-2278. 2019. 4, by MINECO grant CTQ-2017-85224-P, Consortium NANODENDMED-II-CM (B2017/BMD-3703) and IMMUNOTHERCAN-CM (B2017/BMD3733). The project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 844217. This article is based upon work from COST Action CA 17140 "Cancer Nanomedicine from the Bench to the Bedside" supported by COST (European Cooperation in Science and Technology). CIBER-BBN is an initiative funded by the VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.
The inner collection semi-angle of the HAADF detector was set to 90 mrad, with 22 mrad probe convergence semi-angle. The HAADF image analysis was carried out using StatSTEM add-on for Matlab  [44]. Single width 2D Gaussian functions were fitted to the atomic columns after background subtraction. HAADF image simulation was performed using the muSTEM software using 15 frozen-phonon configurations, transmission functions with 0. 02 Å square pixel size, and supercell size ~20 × 15 Å  [68, 69]. The above experimental STEM probe parameters were used with defocus C1 = 0, C3 = 0. 002 mm, and C5 = 1 mm spherical aberration coefficients, and a fully coherent electron probe. Kirkland multislice code was used to calculate the average of electron probe intensity versus sample depth, averaged over 10 frozen-phonon configurations  [68].
The intensity average is taken across the atomic column in a 1 Å wide window. X-ray energy dispersive spectroscopy was performed using 0. 98 steradian solid-angle windowless silicon drift-detector JEOL JED-2300. The probe current was set to 200 pA for EDX characterization and pixel dwell time 0. 2 msec. The EDX images were 512 × 512 pixels in size, and a total of 5 frames were accumulated for each data set. Wiener filtering was applied to both EDX images for visualization, and sample drift-correction was used on Fig. 5  EDX data. On-axis electron energy-loss spectrum imaging was carried out using a modified monochromated Nion Hermes-200 (ChromaTEM) operated at 100 kV. The probe convergence semi-angle was set to 10 mrad, EELS collection semi-angle 35 mrad, 0. 02 eV EELS energy dispersion, and 0.
Funding This work was supported by the  European Regional Development Fund  (project No  01. 2. 2-LMT-K-718-02-0020 ) under grant agreement with the  Research Council of Lithuania (LMTLT). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 823717-ESTEEM3. This study was also supported by the  Operational Program Research and Innovation  for the project ITMS2014+ code  313010 T598  co-financed by the  European Regional Development Fund. This work has received support from the  French  state managed by the  National Agency for Research  under the programme of future investment EQUIPEX TEMPOS-CHROMATEM with the reference  ANR-10-EQPX-50.
Non-linear magnetohydrodynamic (MHD) simulations play an essential role in active research and understanding of tokamak plasmas for the realization of a fusion power plant. The development of MHD codes such as JOREK is a key aspect of this research effort. In this paper, we present an operational version of the full-MHD model implemented in JOREK, a significant advancement from the reduced-MHD model used for previous studies, where assumptions were made on the perpendicular dynamics and the toroidal magnetic field. The final model is presented in detail, and benchmarks are performed using both linear and non-linear simulations, including comparisons between the new full-MHD model of JOREK and the previously extensively studied reduced-MHD model, as well as results from the linear full-MHD code CASTOR3D.