dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments-We thank  Véronique de Berardinis ,  Marcel Salanoubat , and the late  Annett Kreimeyer  for all the fruitful discussions during the conceptualization of this project. We are grateful to  Christine Pellé  and  Peggy Sirvain  for excellent technical assistance in protein purification. This work was supported by  Commissariat à l'Energie Atomique et aux Energies Alternatives , the  CNRS , and the  Université Evry-Val-d'Essonne/Université Paris-Saclay.
, 2018; Hadrup et al. , 2020; Sutunkova et al. , 2020) , but the impact of EN inhalation on the intestine has never been explored. In this study, we focused on the most abundant EN found in air. Despite the general lack of qualitative and quantitative data on human exposure to EN,  Hansen et al. (2016)  have established "The Nanodatabase" (http://www. nanodb. dk), which is an online inventory of products claimed by manufacturers to contain nanomaterials or include nanotechnology. This database indicates that for inhalation, silver (Ag) is the most prevalent nanomaterial followed by titanium (Ti), titanium dioxide (TiO 2 ), and silicon dioxide (SiO 2 )  (Hansen et al. , 2016). Ag and Ti nanoparticles are known for their biological and biomedical properties  (Pereira et al. , 2018). Both are often present in consumer products such as cosmetics, food additives, kitchen utensils, and toys.
Analysis of sequencing data Bioinformatic analyses were performed using the QIIME 2 pipeline (v. 2020. 2)  (Bolyen et al. , 2019). The Divisive Amplicon Denoising Algorithm 2 (DADA2) plug-in in QIIME2 was used to filter, dereplicate, identify chimeric sequences, and merge paired-end reads to obtain the set of amplicon sequence variants (ASVs) for each sample  (Callahan et al. , 2016). Then the representative sequences were picked for each ASV. The classify-sklearn plug-in in QIIME2, with the SILVA database (v. 132) was applied to assign a taxonomic annotation to each representative ASV sequence. In the next step, ASVs identified as eukaryotic contamination (no contamination) and external contamination, identified with the decontam package (15 ASV; 1599 reads), were filtered out  (Davis et al.
Effects of ENM exposure on gut microbiota The bacterial diversity in cecal content was determined by pyrosequencing of the bacterial 16S rRNA genes. Exposure to ENM did not significantly affect α-diversity (data not shown). In males, unweighted uniFrac index showed a significant increase of β-diversity in ENM- exposed mice compared to control mice (p = 0. 004; Fig. 4A ). The relative abundance of ASVs at phylum, class, order, and family taxonomic levels was compared. The major phyla were Firmicutes and Bacteroidetes and the Tenericutes were less abundant in ENM-exposed mice (Fig. 4B ). ENM exposure also induced in male mice a decrease of Coriobacteriia and Mollicutes classes, and of Coriobacteriales and Mollicutes RF39 orders (Fig. 4C-D ). The only family which showed significantly different abundance in exposed male mice was Eggerthellaceae (Fig.
Materials The three alloys investigated in this work are two new advanced ODS alloys (CrAZY and OFRAC) and one ''classical" ODS material (14YWT). The compositions of each are provided in Table  1. Alloy 14YWT (Fe-14Cr-3 W-0. 4Ti + 0. 3Y 2 O 3 in wt. %) was specifically designed to provide a combination of ultra-fine grains and high creep strength by maximizing the number density of nanoscale (Y,Ti,O)-rich precipitates throughout the microstructure. Initially developed at ORNL in 2000, 14YWT has an extensive mechanical properties database collected over the past 20 years  [12]. More recently in 2018, the nanostructured ODS FeCr alloy OFRAC (Oak Ridge Fast Reactor Advanced Fuel Cladding) was designed with an impurity sequestration approach while utilizing best practices in the development efforts of alloys such as 14YWT, 12YWT, and MA957  [13].
Scanning electron microscopy and backscatter diffraction As-received tubes were sectioned and metallographically polished with a final colloidal silica (0. 05 mm) finish. Scanning electron microscope (SEM) images and electron backscatter diffraction (EBSD) data were collected using a TESCAN MIRA3 SEM equipped with an Oxford Symmetry EBSD detector. Fractography was performed using the same instrument at an accelerating voltage of 20 kV in secondary electron imaging mode. All EBSD data were collected using a 30 kV accelerating voltage with a 4 nA beam current. To prevent anisotropic grain morphologies from affecting grain size statistics, imaging was conducted along the longitudinal orientation (i. e. , the tube axis was oriented vertically in the EBSD scans). To optimize scan times and resolution, multiple different scan areas were analyzed for all three tubes.
For major axis grain size determinations, EBSD scans were collected for OFRAC and CrAZY over areas of 50 mm Â 200 mm (width Â height) with a step size of 0. 15 mm. For 14YWT, the scan area was 400 mm Â 1000 mm and the step size was 1 mm. For minor axis grain size and texture determination, identical scans of areas 150 mm Â 50 mm and step size of 0. 075 mm were collected for all alloys. The sample tilt angle was 70°, while the working distance was 18 mm. EBSD data was analyzed using EDAX OIM Analysis version 8. 1. A grain tolerance angle of 5°was used for grain identification and quantification. Grains comprising an area less than 10 pixels were omitted from datasets to prevent erroneously identified grains from affecting grain size statistics. For visualization purposes, one iteration of grain confidence index correlation data cleanup was applied to each dataset.
Orientation calculations were performed using the generalized spherical harmonic series expansion approach with orthotropic sample symmetry. For the calculation, the series rank was 16 and the Gaussian halfwidth was 5°. Taylor and Schmid factors were exported for each point and each grain based on the orientation of the applied load (either parallel or perpendicular to the pilgering direction). Finally, the area-weighted Taylor factor was computed for each EBSD dataset and loading combination. The effective diameter was calculated for each grain under the assumption that (1) grain morphology can be approximated using a spheroidal grain shape  [15] , and (2) that the effective grain diameter can be approximated by the average distance a dislocation travels along the most favorable slip systems.
In the resulting jump ratio map, regions with less Fe enrichment (i. e. , oxygen-rich clusters in these alloys) appeared with dark contrast, which were then identified and quantified using the ImageJ software package  [18]. The areal density of precipitates was converted to a volumetric density by computing the thickness of each lamella. The thickness was calculated by relating the attenuation of electrons through the lamella (the ratio of the unfiltered image to the zero loss image) to the inelastic scattering mean free path ($140 nm, depending on the collection angle of the TEM)  [19] : t k ¼ ln unfiltered image zero À loss image : ð1Þ The same microscope was operated in scanning transmission electron microscope (STEM) mode for dislocation characterization. Dislocations were imaged on h1 1 1i zone using the bright-field (BF) STEM method  [20].
The statistical analysis of the dislocation lines was performed using segmentation methods with Python and Ima-geJ. Individual TEM micrographs of dislocations were segmented by a 5 Â 5 grid and filtered by a self-written background filtering Python code. Each remaining dislocation line was identified in Ima-geJ and measured to provide the length of each dislocation line (l i $ 5-25 nm). At least 7 images with at least 300 dislocation lines were counted at different locations in each specimen for improved statistical confidence and accuracy. The dislocation density (q D [m À2 ]) was then calculated by the total length of dislocation lines ( P N l i ) divided by the measured volume (A Á t) using the equation: q D ¼ P N l i A Á t : ð2Þ 2. 3. 3. Atom probe tomography APT was performed on each tube to further quantify nanoscale precipitation within each alloy and to directly compare with EFTEM methods.
APT specimens were prepared using either an FEI Versa 3D dual-beam FIB-SEM or an FEI Helios 600 with final tip sharpening performed at 5 kV. Three of the APT datasets used in this work, specifically for the OFRAC alloy, were analyzed on a CAMECA LEAP 5000 XR instrument at the Max-Planck-Institut für Eisenforschung (MPIE) in Düsseldorf, Germany. These data were presented in detail in an earlier publication discussing precipitate/dislocation interactions  [21]. The remaining three datasets for OFRAC, and the six datasets each for the CrAZY and 14YWT alloys, were analyzed on a CAMECA LEAP 4000X HR instrument at the Center for Nanophase Materials Sciences facility at ORNL. The sum of these APT runs provides a robust set of data to compare the dispersions measured using the EFTEM technique. On both instruments, APT data were collected in laser pulsing mode using a laser energy of 32 pJ and a pulse frequency of 200 kHz.
All measurements were taken using Cu-K a radiation (k = 1. 540598 Å). The raw XRD data were instrument-corrected using the standardized LaB6 powder data and analyzed using TOPAS V6 software by Bruker. Additionally, a peak deconvolution without a Rachinger correction using Origin Pro software was performed. A pseudo Voigt peak fit (PsdVoigt1) was used to distinguish the K a1 and K a2 peaks, assuming a ratio of 2:1. A peak deconvolution for each single peak was conducted, sharing the FWHM and the profile shape factor for both resulting peaks. The center of each peak, which was determined using TOPAS software, was set and not allowed to change, whereas area, offset, FWHM, and the shape factor were variables that could be changed to reach a fit tolerance of 1Â10 -15. The value q was calculated using a linear regression model in a python script for each material.
Furthermore, imaging along h111i-zone axis has been shown to greatly reduce the number of invisible dislocations using STEM methods  [42]. This is partially because of the limiting of dislocation types fulfilling the g Ã b invisibility criterion to the a/2h111i edge and screw dislocation as well as the averaging of signal intensities over a wider range of incident illumination angles. Although errors in thickness measurements using the EFTEM approach can be appreciable (±20%) based on uncertainties in the calculation of k  [43] , the larger error usually comes from the final issue of field-of-view limitations. Spatially dependent dislocation densities within alloys requires multiple averaged datasets for reliable statistics. Fig. 6  shows typical dislocation networks seen in the tubes as imaged along the h111i-zone axis.
For the calibration and the validation of the model, a series of experimental data have been collected from different literature studies, initially corresponding only to the case of pure styrene homopolymerization. These data were then enriched with additional experimental data produced in the framework of this study, including DSC measurements  (Florez et al. , 2020)  and bench-scale solution polymerization runs  (Florez Parra, 2019) , both in the presence and in the absence of GTR. The mathematical modeling of the radical polymerization of styrene has been extensively studied and several modeling frameworks have been proposed. The reported works of the group of Hamielec  (Hamielec et al. , 1967; Duerksen et al. , 1967; Hui and Hamielec, 1972; Husain and Hamielec, 1978) , as well as the work of Mayo  (Mayo, 1968) , were certainly pioneering in the domain and have set the grounds for later modeling developments.
e. , mol:l À1 :min À1 ), while the molar concentrations of the different species (i. e. , in mol:l À1 ) are denoted with the use of brackets ½. Also, dðn À 1Þ is the Kroenecker' s delta, given by: dðn À iÞ ¼ 1 for n ¼ i 0 for n-i & ð42Þ Note that, in the present work, the complete mathematical model was developed in Matlab to allow for a maximum control over the derivation of the model equations (i. e. , in contrast to commonly employed polymerization-specific software).
In this respect, the resulting system of ordinary differential equations (ODEs) will be of the form: dn S dt ¼ r S Á V ð62Þ where S denotes the different species of interest and V is the volume of the reacting mixture, which also varies according to: dV dt ¼ r M Á 1 d M À 1 d P Á M 0 Á V ð63Þ where M 0 is the molecular weight of styrene and d M and d P are the respective densities of styrene and polystyrene in g:mol À1 , calculated by the following expressions  (Kotoulas et al. , 2003) : d m ¼ 9:236 Á 10 À1 À 0:887 Á Tð CÞ; d p ¼ 1:085 À 0:605 Á Tð CÞ ð64Þ The above system of ODEs is completed by the algebraic equations for the calculation of the concentrations of AH, PR, GPR and PRl (Eqs. (  58 )-(  61 )). This system of ODEs was numerically solved using a variable step and variable order numerical integration method, as implemented in the function ''ode15s" of the toolbox of Matlab, which is specifically adapted for stiff systems.
2. These experiments were realized within the framework of a study that was focused on the final average molecular weight of the produced polystyrene  (Florez Parra, 2019). As such, not enough intermediate data were recorded to allow the tracking of the complete temporal evolution of the course of the polymerization. Nonetheless, the comparison with the model predictions, depicted in Fig. 3 , provides evidence that the model remains coherent in this solution polymerization case as well, under two different polymerization temperatures. The last set of data of pure styrene homopolymerization, on which the model has been tested, is the DSC data set that has been produced and reported in the framework of our previous study  (Florez et al. , 2020). This comparison is shown in Fig. 4 , where the conversion-time histories are plotted for three different reaction temperatures, namely at 85 °C, 90 °C and 120 °C.
ACKNOWLEDGMENTS Financial support from the  National Natural Science Foundation of China  (Grant No. 21805166 ), the  111 Project of China  (Grant No. D20015 ), The outstanding young and middle-aged science and technology innovation teams,  Ministry of Education, Hubei province, China  ( T2020004 ),  Foundation of Science and Technology Bureau of Yichang City  ( A21-3-012 ), and the  University of Bordeaux  and the  Centre National de la Recherche Scientifique (CNRS) ,  is F I G U R E 11 Plots of produced O 2 volume versus time  for O 2 evolution upon the decomposition of H 2 O 2 : (A) various concentrations of H 2 O 2 , (B) different amounts of catalyst, and (C) various reaction temperatures; (D) comparison of O 2 generation catalyzed by Pd/CNS-800 and Pd/C. CNS, carbon nanosphere.
Acknowledgments: The Tshwane University of Technology is acknowledged for providing the facilities for the synthesis of the membranes. The European Institute of Membranes, University of Montpellier, is acknowledged for providing the facilities for the characterization of the membranes. CSIR Durban is acknowledged for the supply of cellulose nanocrystals. This research has been sponsored by funding from the Science and Technology Department (DST), South Africa. The financial support is highly appreciated.
Computational framework The search for the ground state structures and energies as well as energy dependent quantities within DFT was carried out using plane-wave Vienna Ab initio Simulation Package (VASP) code  [13, 14]  with the projector augmented wave (PAW) method  [14, 15]  for the atomic potentials. The exchange-correlation XC effects within DFT were considered using the generalized gradient approximation (GGA)  [16]. For the methodology details, the reader is referred to our previous papers  [17, 18]. Regarding the choice of the XC functional, the relevant studies focusing on black phosphorous  [19]  and phosphorene  [20] , both presenting layered stacking, showed the impact of using more sophisticated XC functional such as the hybrid GGA-HSE06  [21]. However, calculations using such functional didn't change the outcome of the present investigation.
By fitting a model to this pattern, the refractive index depth profile of the sample can be determined. Neutrons interact with the nuclei of the target atoms, which makes them sensitive to isotope substitution. In soft matter studies, parts of the sample are commonly labeled by using deuterated chemicals as a means of varying the contrast within the sample. 46 Furthermore, the polymer volume fraction depth profiles can be determined by measuring the same sample using different mixtures of deuterated and hydrogenated solvent and simultaneously fitting the recorded reflectograms to the same model. Neutron reflectometry data are generally ill-conditioned for fitting due to the loss of phase information during the detection of the reflected neutron wave. 47 To increase the confidence of the model fit in the case of hydrated samples, multiple reflectograms with different mixtures of D 2 O and H 2 O are measured and fit simultaneously beyond the necessary two compositions that are required to determine the solvent content of the layers.
Although a method based on Lagrange multipliers was used in previous studies,  56  we use numerical calculations implemented in a Python script within the GenX program  52  to constrain the fit parameters; details of these procedures, including the code, are included in the Supporting Information. To assess the swelling of the layers, we defined the hydrated layer thickness using the expression h z z z z z 2 ( ) d ( ) d wet 0 0 = (5) The formulas for calculating the first moment of the volume fraction profiles are shown in the Supporting Information (Table  S3 ). The swelling of the layer can then be calculated as Σ = h wet /h dry. The monomer ratios in the films were determined using the following equation: ). Because the fits are only an approximation of the real profiles, the layers in the fit do not necessarily represent an actual stratified film; thus, the remaining parameters are not constrained to a priori specified physically realistic values.
Because the polymerization is progressing largely in bulk with attachment of chains to the surface, as a "grafting-to" process,  32  the difference in the UV intensity also explains the different grafting speeds for the two surfaces. Studies performed on poly(HEMA-co-PEG 10 MA) brushes grown by surface-initiated atom transfer radical polymerization on gold substrates  72  suggest a correlation between the crowding of the layers, swelling, and antifouling performance. Comparing the swelling and grafting in Figure  S10 , we can see a clear negative correlation between these in our samples as well, although data are limited. Neutron reflectometry data (dots) measured on double-grafted layers for samples Si1 (a), Si2 (b), and Au2 (c) with a deuterated MAA layer grafted after preparation of the hydrogenated films and the resulting fits from the modeling (lines).
Because the Peng-Robinson (PR) thermodynamic model is robust and presents minimum deviation from the base models  [25] , all simulations in the biomethane upgrading unit were performed using the Aspen Plus V10. 0 software by employing the PR equation of state. In addition, for hydrogen production using TP, Sarafraz et al. [26]  used the UNIQUAC properties package. Thus, it was employed to simulate the TP reactor unit in this work.
Acknowledgements This project has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  810182 ). This work was also supported by the " Priority Research Centers Program " of the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2014R1A6A1031189 ) and " Human Resources Program in Energy Technology " of the  Korea Institute of Energy Technology Evaluation and Planning (KETEP)  funded by the  Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20204010600100 ). r e f e r e n c e s.
In this context, the knowledge of the 3D structure of the complexes is fundamental to explore the recognition processes at atomic level and to properly characterize their interactions. Our current understanding of protein-protein interfaces has grown along with the amount of structural information deposited in the Protein Data Bank (PDB)  (Berman et al. , 2000). Protein-protein interfaces span a large range of size and shape, display shape complementarity, and are usually described in terms of core and peripheral regions, with a few hotspot residues being the major contributors to the binding affinity  (Clackson and Wells, 1995; Jones and Thornton, 1996; Bogan and Thorn, 1998; Chakrabarti and Janin, 2002; Saha et al. , 2006; Levy, 2010). However, the anatomic description of proteinprotein interfaces and their physico-chemical properties generally relies on a static point of view.
Terminal residues present in the free proteins but absent from the complex were chopped, and residues present in the complex but absent in the free proteins were modeled based on the complex. We computed a pKa calculation on the complex structure using the software PDB2PQR  (Dolinsky et al. , 2004)  and the algorithm PROPKA  (Olsson et al. , 2011; Sondergaard et al. , 2011)  to define the protonation state of each residue. To deal with the same chemical species, we applied the same protonation state of the complex on the relative free proteins.
All-atom molecular dynamics simulations All-atom Molecular dynamics (MD) simulations were performed with the GROMACS five package  (Berendsen et al. , 1995; Lindahl et al. , 2001; Van Der Spoel et al. , 2005; Hess et al. , 2008; Abraham et al. , 2015)  using the Amber 99SB-ILDN force field for proteins  (Wang et al. , 2000; Hornak et al. , 2006; Lindorff-Larsen et al. , 2010). The proteins we studied were each placed in a truncated octahedral box and solvated with TIP3P water molecules  (Jorgensen et al. , 1983)  to a depth of at least 11 Å. The solute was neutralized with potassium cations and then K + Cl -ion pairs  (Dang, 1995)  were added to reach a physiological salt concentration of 0. 15 M. Long-range electrostatic interactions were treated using the particle mesh Ewald method  (Darden et al.
Interface definition Interface contacts are defined at the residue level using a 5 Å distance cutoff between heavy atoms. The solvent accessible surface area (ASA) is computed with the NACCESS software using the default radius of 1. 4 Å to take into account the water molecule  (Hubbard and Thornton, 1992). We define distinct regions at the interface according to the criteria introduced by Levy  (Levy, 2010). Residues with a change of ASA between the isolated chain and the complex are classified as: support if the relative ASA in the isolated chain extracted from the complex is lower than 25%, rim if relative ASA in the complex is greater than 25%, core if relative ASA in the isolated chain is greater than 25% and lower than 25% in the complex.
Interface properties The area of accessible surface buried by the interface is defined by: ΔASA ASA A + ASA B -ASA AB (1) where ASA A and ASA B denote the accessible surface areas of separate chains and ASA AB the accessible surface area of the complex. The gap index is defined as gap index 2 gap volume ΔASA (2) where the gap volume is computed with the SURFNET software  (Laskowski, 1995). The number of hydrogen bonds at the interface is detected using the gmx hbond routine implemented in GROMACS  (Abraham et al. , 2015).