dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational details All calculations were performed using the framework of DFT with the periodic plane-wave basis set implemented in the Vienna Ab initio Simulation Package (VASP)  35, 36. Energy analysis for the reported minimized structures was obtained with projectoraugmented-wave potentials  37, 38. Electron exchange and correlation terms were treated within the general gradient approximation (GGA) of the Perdew-Burke-Ernzehof (PBE) functional  39, 40. For all calculations, the plane-wave energy cutoff was 450 eV and a Methfessel-Paxton smearing  41  with smearing value of 0. 1 eV was used. Atomic positions were relaxed with the conjugate gradient (CG) algorithm until forces on each moving atom were less than 0. 02 eV Å -1. The van der Waals contributions were considered in the Grimme D2 empirical dispersion correction  42  , giving a good compromise for the lattice parameters of bulk copper metal with equilibrium values of 3.
ACKNOWLEDGEMENTS This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( ERC Advanced  Grant No. 741123 ,  Corrosion Initiation Mechanisms at the Nanometric and Atomic Scales: CIMNAS). We thank  GENCI  for high performance calculations in the national (CINES) center under the  A0040802217.
Acknowledgements T. -P. L. thank the financial support from  Distinguished University Professor grant  ( Nanyang Technological University ), AcRF Tier 1 grants from the  Ministry of Education of Singapore  ( RT14/20 ), and the  Agency for Science, Technology and Research (A*STAR)  under its  MTC Individual Research Grant  ( M21K2c0114 ). Z. L. would like to express gratitude to the financial support from the  Agency of Science, Technology and Research (A*STAR) ,  Science and Engineering Research Council (SERC) Central Research Fund (Use-inspired Basic Research)  for this work. X. F, Z. L. and T. -P. L. would also like to thank the A* STAR Research Grant  support under RIE2025 Manufacturing, Trade and  Connectivity (MTC)  Programmatic Funding (M22K9b0049).
The Gruneisen parameter is: Problem setup for the two-dimensional SPH simulation of the diamond loading with an intense laser pulse. The diamond sample is a rectangle with sizes 630 µm×240 µm placed in periodic boundary conditions along x-axis. The boundary at y = 0 moves with the velocity υP (t, I) which is the interpolation of several MULTI profiles given in Fig. 9. Γ(ρ, e) = γ i + γ c (ρ) -γ i 1 + x -2/3 [e -e c (ρ)]/e a , (25) where γ c (ρ) is for the low temperatures, while γ i is used for the high temperature plasmas. The energy of anharmonicity e a corresponds to the transition energy between asymptotic expressions for high temperatures. The reference data on diamond may be found in the shock wave database  [26] , where the equation of state for used in our simulations diamond corresponds KEOS5.
V. ESTIMATION OF DENSITY GRADIENT IN SHOCK WAVES As part of the study of shock wave morphology, we estimate a density gradient (shock wave width) using a method based on the analysis of monochromatic X-ray images with high spatial resolution in phase contrast and modelling of shock propagation by the Code WavePropagator. Images corresponding to times t = 3 ns (one SW is observed) and t = 8 ns (two SWs are clearly visible) were selected for analysis. On the 2D phase contrast image of LiF, the region where the shock wave front can be considered approximately flat was chosen to minimise the blurring effect throughout the diamond volume (red region in Fig. 14a ). A number of independent parameters were introduced to simulate the propagation of the probe beam through the shocked diamond sample.
Resonance analysis of the capture yields The three capture yields obtained in EAR1 and EAR2 with the two samples have been analysed to obtain the Resonance Parameters (RP) of the 244,246,248 Cm and 240 Pu isotopes in the Resolved Resonance Region (RRR). The resonances have been fitted with SAMMY  [16] , a code widely used in the nuclear data community, applying the R-matrix formalism and the Reich-Moore approximation  [17]. The different experimental effects consider in the SAMMY analysis are: • The Doppler broadening is caused by the thermal motion of the target nuclei. In this experiment, the free gas model has been used. • The multiple scattering effects, which take into account that the neutrons can be captured after one or more elastic scatterings. SAMMY calculates this effect by performing dedicated Monte Carlo simulations.
Summary and conclusions This work presents a series of measurements of the capture cross section of 244,246,246 Cm from 1 to 400 eV using two complementary experimental areas of the n_TOF facility and different samples to improve the present evaluations. The experiment was the first capture measurement at n_TOF performed in the two experimental areas, also for the experiments, two different setups were used (TAC and C 6 D 6 detectors). The efficiencies and the different corrections needed were obtained considering the cascades ob- tained with NuDEX, these accurate work leads to small uncertainties in the absolute yields. The results obtained in the two areas with different detectors are compatible. The work that is already ongoing is to combine the information of the various measurements to obtain the final RP with their corresponding uncertainties, these results will be published in dedicated papers and sent to the EXFOR database.
Instruments A Teflon-lined stainless-steel autoclave was used for the solvothermal synthesis of the strontium and calcium aluminate precursor nanoparticles (NPs). The annealing process was performed in a Carbolite tube furnace. The X-ray powder diffraction (XRD) data were collected on a PANalytical X'pert Pro MPD (Cu Kα = 1. 5406 Å). A Horiba Jobin Yvon Fluorolog 3-22, equipped with a photomultiplier tube (PMT) detector was used to obtain the photoluminescence spectra and photoluminescent quantum yields (PLQY). A closed-cycle Janis-Sumitomo SHI-4. 5 cryostat equipped with a programmable temperature controller (Lakeshore Model 331) was used for the luminescence measurements at 5 K. Both excitation and emission slits were at 1 nm for obtaining the excitation and emission spectra, unless stated otherwise.
Visual Characterization Macroscopic views of glasses are accessible through optical or electron microscopy. Both scanning transmission electron microscopy (STEM) and scanning electron microscopy (SEM) allows to visit the microstructure of MOF glass (Figure  2C ), mechanical deformation, and the formation of grain-boundary-free monoliths. Microzone composition can also be analyzed using energy dispersive X-ray spectroscopy (EDS). Deformation of the glass surface or the formation of a shear band under mechanical stress is precisely quantified by atomic force microscopy (AFM). Microstructures can then be theoretically simulated. Molecular dynamics (MD), density functional theory (DFT), PDF and RDF are able to provide theoretical basis and experimental data fitting. Therefore, reverse Monte Carlo (RMC) are able to obtain a visualization of glass structure model (Figure  2C ).
Irradiation testing Irradiation testing includes extensive neutronics calculations to ensure the appropriate experiment conditions are obtained and verified. At SCK CEN, the MCNP software, which utilizes Monte Carlo methods to estimate the particle interactions during an irradiation cycle is used for pre-and post-irradiation calculations. The pre-irradiation neutronics is based on an assumed BR2 reactor core and the post-neutronics on the actual BR2 reactor core and BR2 power and control rod histories. The neutronic calculations are conducted to evaluate essential exposure statistics, usually determined at beginning of cycle (BOC), 3 days into the irradiation (3D), and at the end of cycle (EOC). The main parameters estimated and verified are: • peak and average heat flux (W/cm 2 ), "target" values with the pre-irradiation neutronics, and 'acquired' results with the post-irradiation neutronic calculations over the plate "meat" section.
Significant investments in equipment by TUM at CERCA will enable the fabrication to mostly be performed at CERCA, utilizing commercialscale equipment and processes. Only the Zr coating of the foils for FM1 is expected to be coated on a commercialscale system at TUM. This experiment is planned to begin in 2023 and non-destructive examinations will follow in 2024. In addition to the fabrication studies leading up to these experiments, the EU-QUALIFY project also includes innovative efforts to establish a fuel performance database and to further enhance the capability of the fuel performance modeling. These activities are also important for a generic fuel qualification and to enable the reactorspecific fuel qualifications. Table  3  summarizes the innovations expected from the irradiation tests conducted or examined within the project; namely: ME-DEUS/ ME-CHARTREUSE, E-FUTURE-III, and FUTURE-MONO-1.
To include the solvent effects on the excited-state properties, all the calculations were carried out adopting the linear-response PCM approach (LR-PCM) for solvation in both aqueous solution and pure DMSO. All the DFT/TD-DFT calculations were performed using the Gaussian16 package,  77  while the MOM computations were performed with the Q-Chem software package. 86  Diabatizations were carried out with the code Overdia. 54  All the MD simulations have been performed using the GROMACS package  [45] [46] [47] [48]  (version 5. 1. 4), initiated adopting the two well-separated (>20 Å) Zn PcF12-SH units in pure DMSO and aqueous solution, for which the details of the model systems are reported in Table  S2  of Supporting Information. Periodic boundary conditions were applied in all three directions.
■ ACKNOWLEDGMENTS I. R. and S. K. M. thank the  Labex iMUST  (grant  ANR-10-LABX-0064 ) of the  Université de Lyon  for financial support. I. R. acknowledges the use of the " Pole Scientifique de Modélisation Numérique " (PSMN) at the  E ́cole Normale Supérieure de Lyon, France. F. S. thanks the support of  ICSC�Centro Nazionale di Ricerca in High Performance Computing , Big Data and  Quantum Computing , funded by  European Union�NextGenerationEU�PNRR ,  Missione 4 Componente   2 Investimento 1. 4. M. A. acknowledges the support of the  EC Research Innovation Action  under the  H2020 Programme , the Project  HPC-EUROPA3  ( INFRAIA-2016-1-730897 ), and the computer resources and technical support provided by  CINECA supercomputer center  in Italy. D. A. acknowledges  Fundación Ramón Areces  and  Generalitat Valenciana/European Social Fund  ( APOSTD/2021/025 ) for fundings and  ICCOM-CNR (Pisa) / ICMol-MolMatTC   (Valencia)  for hospitality.
This was done with the COLEGRAM software  [19] , a user-friendly interface to process x-ray spectra. To analyse several similar spectra with COLEGRAM, the user must create a model on one spectrum and a batch fitting is available to process large number of spectra with the same peaks (number, positions, constraints on parameters), fitting on the remaining free parameters (amplitudes, …). Several peak shapes are available for the user. We chose to use gaussians with condition upon standard deviation: it is an option to account for the variation of the instrumental width with energy. The standard deviation of the peaks becomes 𝜎 2 = 𝜎 0 2 + 𝑘. 𝐸 where k and σ are the new fitting parameters common to all peaks. A Levenberg-Marquardt algorithm performs the peak fitting. The spectrum decomposition is a critical step as some line interferences are possible owing to the limited spectral resolution (130-150 eV) of the SDD in this photon energy range.
Conclusion X-ray planar waveguides made of a few thin films can be used to probe the buried interfaces between solids at the nanoscale. In this framework, a series of Ta/Cr/Pt waveguides was studied by a combination of non-destructive and complementary methods: x-ray reflectivity and grazing incidence x-ray fluorescence. The measurements were performed in the hard x-ray range (photon energy higher than 5 keV) for incident, reflected and emitted radiations. The combined fit of the XRR and GIXRF data tightened constraints on the many parameters used to model the stacks and also showed that the thicknesses of the Ta and Cr layers are smaller than expected. The constraints on the parameters were further tightened by comparing the number of atoms as calculated from the thicknesses and densities of the layers to that measured by Rutherford backscattering spectrometry.
Introduction Nanomaterials, which are defined as materials having at least one dimension below 100 nm  [1] , have become ubiquitous in our daily lives. Their presence as nanoparticles is well-known in common consumer products, such as cosmetics, various pharmaceutical products, textiles and food products  [2] [3] [4] [5]. Nanomaterials remain a source of concern because they can exhibit very different properties from those of their bulk form. The high specific surface area of nanomaterials often heightens their reactivity and their small dimensions can lead to cell internalization in the event of their absorption through the gastrointestinal tract  [6]. Over the last decade, several European national registers have begun data collection on nanomaterials used in food products  [7]. In particular, food additives should be examined, as they are likely to occur in multiple types of food products.
X-ray Diffraction (XRD) X-ray diffraction (XRD) was performed using a D8 Bruker diffractometer in the Bragg-Brentano geometry, equipped with a front germanium (111) monochromator, a copper anode (Cu Kα1 radiation; λ = 1. 540598 Å), and a LynxEye PSD detector. The scanning consisted of a 2θ angular range from 8 • to 90 • and a step size of 0. 018 •. The obtained diffraction pattern was analyzed using the Diffrac. eva V5. 2 software for phase assignment according to the Powder Diffraction File (PDF) database. A Rietveld refinement was made using the Jana2006 software to determine the proportion of phases in the E341(iii) A sample.
The different size distributions have been fitted with a Gaussian model to help the visualization of the data (Figure  9 ). Counting is performed manually across multiple images, with the main counting c being particle visibility. The maximal and minimal Feret diameter (Dmax and Dm spectively) were measured. The aspect ratio (Dmax/Dmin) was determined for eac vidual particle and resulted in an aspect ratio distribution allowing a better appre of the general particle shape. The different size distributions have been fitted with a ian model to help the visualization of the data (Figure  9 ). The general statistical data of the distributions are reported in Table  3. E341(iii) C are most similar in terms of Dmin and Dmax, with a slightly broader distribut E341(iii) C. The micrometric DCPA plates present in E341(iii) A were disregarded count (a proportion of 18% from the XRD analysis).
Acknowledgments: The authors are grateful to  Stéphane Grolleau  from the  IMN laboratory  for performing the adsorption/desorption N 2 isotherms and pycnometry measurements and  Michaël Paris  from the  IMN laboratory  for performing NMR measurements. Appreciated technical support was provided by  Eric Gautron  from the  IMN laboratory  for the use of the transmission electron microscope. Funded by the  French Contract Plan État-Région  and the  European Regional Development Fund of Pays de la Loire , the  CIMEN Electron Microscopy Center in Nantes  is greatly acknowledged. The authors would also like to thank  Carole La  from the  Laboratoire de Planétologie et Géosciences  ( UMR CNRS 6112 ) for her technical support in the ICP-AES measurements.
Source of Funding M. De Waard  thank the  Agence Nationale de la Recherche (ANR)  for financial support to the  laboratory of excellence "Ion Channels, Science and Therapeutics"  (grant N°  ANR-11-LABX-0015 ) and for grants entitled  OptChemCom and Cardiag  (grant N°  ANR-18-CE19-0024-02  &  ANR-21-CE17-0010-01 ). This work was supported by the  Fondation Leducq  in the frame of its program of ERPT equipment support (purchase of an automated patch-clamp system), and  Transatlantic Network  of Excellence  19CV03  FANTASY (to M. E. M), by a grant " New Team " of the  Région Pays de la Loire  to  M. De Waard , the  National Institutes of Health  ( R01NS091352  to F. B. ), and by a  European FEDER  grant in support of the automated patch-clamp system of Nanion.
Data were monitored in the direct beam position. The heating kinetics applied to the sample was 1 • C/min from +10 • C to +120 • C. Every 5 • C, a 10-min plateau was introduced to allow acquisition of the diffraction spectrum. The obtained spectra were normalized between 3 and 28 • in 2θ. The same analysis was carried out in excess of water on different doughs using a 1. 5 mm diameter capillary, sealed with wax to prevent water evaporation.
Differential Scanning Calorimetry DSC experiments were carried out using a multi-cell microcalorimeter MC DSC (TA Instruments, New Castle, DE, USA), and the data were analyzed using TA Universal Analysis 2000 software (version 4. 5). The three cells were filled with homogeneous samples (either dough or model system). An empty pan was used as a reference. Cells were hermetically sealed with heat-resistant seals and heated from +10 • C to +120 • C at a heating rate of 1 • C. min -1 , and cooled from +120 • C to +20 • C at a cooling rate of 2 • C. min -1. Concerning the doughs, the analysis was carried out on three different samples. The same experiment was carried out in the presence of excess water with three additional doughs, specifically at 70% water content, instead of 80%, to improve endotherm features for the enthalpy analysis.
Statistical Analysis Each dough formulation and each model system were produced in triplicate. For each measurement, three samples per replicate were collected. Results are presented as mean value of the nine samples ± standard deviation. Statistical significances of the differences were computed with a one-way ANOVA combined to a Duncan test (α = 5%) using R software (version 3. 6. 2) and agricolae R package (version 1. 3. 3). Specific mixing cells were used to characterize the water uptake priorities between the flour and the different sweeteners (Figure  1 ). For this, a flour-sweetener mixture with a ratio of 1:1 was introduced into the bottom compartment (1) of the "batch mixing" cell (SETARAM, Caluire, France). Water was injected into the upper compartment (2) using a syringe. The sample and the reference cells were placed in a µDSC 7 evo microcalorimeter (SETARAM, Caluire, France) in isotherm mode and left at equilibrium at 20 °C for 30 min; then, the water was released into the lower compartment by a rotating rod mechanism.
Statistical Analysis Each dough formulation and each model system were produced in triplicate. For each measurement, three samples per replicate were collected. Results are presented as mean value of the nine samples ± standard deviation. Statistical significances of the differences were computed with a one-way ANOVA combined to a Duncan test (α = 5%) using R software (version 3. 6. 2) and agricolae R package (version 1. 3. 3).
Chemical shift data are given in δ ppm calibrated with residual protic solvent (e. g. , CDCl 3 : 7. 26 ppm -1 H/77. 16 ppm -13 C). A 13 C UDEFT sequence was used with a spectral width of 33,300 Hz and 4096 scans. In addition, 2D homonuclear 1 H-1 H g-COSY (1 scan, 512 real (t1) × 2048 (t2) complex data points) and 2D heteronuclear spectra of 13 C-1 H g-edited HSQC and 13 C-1 H g-HMBC were acquired to assign the compound (8 scans, 512 real (t1) × 2048 (t2) complex data points). Homonuclear 2D spectra 1 H-1 H NOESY were recorded using data matrices of 256 real (t1) × 2048 (t2) complex data points; 16 scans per t1 increment with a 3s recovery delay and a spectral width of 7200 Hz in both dimensions were used. Spectra were processed and visualized with Topspin 3. 6. 2 (Bruker Biospin) on a Linux station.
Fourier transform infrared spectra were collected with Thermo Scientific (Waltham, MA, USA) Nicolet 210 FT-IR spectrometer featuring a Specac Golden Gate attenuated total reflection (ATR) heating cell. High-resolution mass spectrometry (HRMS) data were acquired using an LC-TOF mass spectrometer (micrOTOF-Q) with electrospray ionization (ESI) in either positive or negative mode. GCMS analyses were conducted using electrospray ionization (ESI) on a Chimadzu QP2010SE mass spectrometer. Thermogravimetric analyses (TGA) of the cured polyurethanes were carried out on a Netzsch (Burlington, MA, USA) STA 449 F1 TGA. Nitrogen gas at a flow rate of 20 mL•min -1 was used as the protective gas. About 10 mg of the sample was placed in an alumina crucible and heated from room temperature to 800 • C with a heating ramp of 10 • C•min -1.
Since our nuanced classification is substantially different from the binary identification by human as only being battery or pseudocapacitor among various electrochemical signals, we propose a new definition called capacitive tendency. This tendency is not only able to classify a large majority of relevant materials, but also to depict possible behaviors of the material in question. Hence, this artificial intelligence (AI) power will be the only important tool to help transforming the information from images to accurate values based on big database available in the electrochemical energy storage community. In addition to this, we provide an online tool kit which uses supervised machine-learning to easily classify materials. Today, the large amount of literature sometimes leads to a misuse of the proposed definitions, to reduce this definitional mishap, our work will reduce these errors.
Dataset Construction In the present paper, all datasets are in the form of images extracted using PyMuPDF library in Python language from more than 3,300 scientific papers. The first dataset, or Output 1, was obtained by figures extracting using OpenCV which provides (2,979) GCD, (5,598) CV and other images such as crystal structure image (which will not be used in the further classification steps). The GCDs and CVs were then labeled as belonging to one of two classes, namely batteries or pseudocapacitors without ambiguity, to be used for model training (80 % of total data), as well as for validation (20 % of total data) in Process 2 and Process 3 for GCD and CV classification, respectively. From Process 3, Output 3 was obtained and categorized into three types of training sets: 100 % battery, 50 % battery/pseudocapacitor, and 100 % pseudocapacitor.
On the contrary, a curve whose confidence value is close to zero means that the curve has a different contribution. Basically, the capacitive tendency reflects the analysis of the signal shape. It is information based on a geometric shape. Of course, alternatives could be used. However, the use of the classical formalism, as indicated in the "ideal CVs" area in Figure  4a , is impossible when the shape of the electrochemical signal deviates from this ideal. In the purely mathematical domain, the possibility of adding a rectangle to a closed geometric shape (a CV is a closed geometric shape) is a complex mathematical situation. It is the concept of Inscribed rectangular problem in mathematics. Thus, our data science-driven by supervised deep learning approach is a suitable alternative.
Results and Discussion This section explains how the models for CV and GCD classification were established for this specific dataset through the validation of different CNN architectures. The selection was based on well-known parameters including Accuracy, Sensitivity, Specificity, Accuracy, and F-Score. Moreover, the most accurate model was developed for use as the descriptor in order to determine the capacitive tendency of the various electrochemical behaviors, by applying the experimental data of various electrode materials. Ultimately, the selected model is destined for use by electrochemists as a tool for determining the nature of their materials.