dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Carole Causserand ,  Ludovic Menjot , and  Bruno Payré  for their kind assistance with ICP-OES, AAS, XRD and SEM measurements. Financial support of this study by the  Deutsche Forschungsgemeinschaft DFG  ( JO301/6-1 ) and the  Deutsche Akademische Austauschdienst DAAD  ( Projekt-ID :  57560933 ) is gratefully acknowledged. Furthermore, the authors are grateful for two anonymous reviews and the editorial handling by  Mariette Wolthers.
ACKNOWLEDGEMENTS This work is supported by the  National Key Research & Development (R&D) Program of China  (grant number  2021YFB3203200 ), the  Shaanxi 2023 Natural Science Basic Research Plan  (grant number  2023-JC-QN-0489 ), the  National Natural Science Foundation of China  (grant number  52175548 ). R. B. thanks the  ANR  for nancial support of the project " SIREVIVAL " of the  LEAP-RE programme  that received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement  963530. Min LI thanks the nancial support from  State Key Laboratory for Manufacturing Systems Engineering.
Three sample geometries (Dumbbells, AE2 and AE42) and four loading paths (tension, torsion, compression, combined tension/torsion) have been investigated. The model involves a weak thermo-mechanical coupling and is based upon the assumption that all the dissipated energy is converted into heat. A good agreement between experimental data and simulations is found whatever the specimen geometry and whatever the loading path. The computation takes only a few minutes, making this approach very attractive when designing rubber components. The paper is organized as follows: In section 2, the experimental protocol including material, samples and the measurements method is detailed. Experimental results are given and widely discussed in section 3. In section 4, the time-dependent mechanical behavior of the material is described using a visco-hyperelastic model.
softwares. The mathematical representation of the constitutive model is based upon the additive partition of the overall resistance into a purely hyperelastic network (Resistance A) and a time-dependent network (Resistance B). Consequently, both deformation gradient F A and F B equal the total deformation gradient F and the total stress σ is the sum of the stress on each network σ A and σ B :    α dumbbell (W. mm -3. K -1 ) 0. 0873 αAE2(W. mm -3. K -1 ) 0. 0497 αAE42(W. mm -3. K -1 ) 0. 1807 h0(W. m -2. K -1 ) 60 cp(J. kg -1. K -1 ) 2950 κ (W. m -1. K -1 ) 0. 27 ρ (kg. m -3 ) √ is the stretch endured by the network B. In MSC Marc software, the flow rate is written as follows for a reason    of dimension consistency: γ = c 1 ( λ v B - 1 ) c2 ( τ ′ B )c 3 (12) In which τ B = τ ′ B τ , τ is a constant user-defined normalization value and c 1 takes the following form: c 1 = γ0 τc3 (13) On the other hand, the hyperelastic springs in networks A and B are described by the 8-chain model  [21]  which takes the following form: W(I 1 ) = nkT 3 ̅̅̅ ̅ N √ ( λ ch β ch + ̅̅̅ ̅ N √ ln ( β ch sinh(β ch ) )) (14) In which, λ ch = ̅̅̅ I1 3 √ and β ch = L -1 ( λ ch ̅̅ ̅ N √ ) , L being the Langevin function.
In this so-called 8-chain model, only 2 material parameters are involved: the chain density n and the mean number of monomers in a chain N. Such a model can capture the drastic hardening occurring when approaching the extensibility limit. Moreover, the parameters are linked to the microstructure of the network. Ultimately, in the Bergstöm-Boyce model, 7 parameters are to be identified: -3 parameters to describe the time-dependent behavior: c 1 , c 2 and c 3 -4 parameters to describe the hyperelastic behavior: n A , N A , n B and N B The identification of the parameters was first achieved using MCalibration software on the tensile tests on dumbbell specimens (Fig. 7a ). They were further adjusted on the other geometries and loading paths by means of trial-and-error method. The optimal values are reported in Table  2.
Powder diffraction In order to check the unit cell of α-hemihydrate for the s3DXRD indexing process, a high-resolution powder diffraction pattern was collected at ID22, the High Resolution Powder Diffraction beamline of the European Synchrotron Radiation Facility (ESRF), using a monochromatic X-ray beam at an incidence energy of E = 35 keV ( = 0. 35389 Å). Data was recorded at room temperature in the -5 to 30° 2θ range using the combination of multi-analyzer crystals and a 2D HPC-detector (Dectris Eiger2 X CdTe 2M-W), and merged together using the in-house software id22sum  (Dejoie et al. , 2018). Le Bail fits implemented in TOPAS5  (Coelho, 2018)  were then performed to confirm the unit cell of the hemihydrate. Powder diffraction patterns, were as well collected at ID11, the Materials Science beamline of the ESRF.
Two data collections were performed, one of the starting dry hemihydrate and one after the hydration process, to assess whether the hydration reaction had fully or partially taken place. The experimental setup was the same used for the s3DXRD (Figure  2 ) with the exception that a beam size of 250  250 µm instead of 5  5 µm was used in order to give a better powder average. The patterns were collected in the 1-10° 2θ range at an energy of 43. 575 keV ( = 0. 2843 Å). Preliminary instrumental calibration at ID11 was performed using a CeO 2 powder standard using the pyFAI-calib tool and integrated with pyFAI-integrate  (Ashiotis et al. 2015).
S3DXRD In-situ s3DXRD experiments were conducted at the 3DXRD station of ID11  (Poulsen, 2004). The analysed specimen contained calcium sulfate hemihydrate grains in a glass capillary with a diameter of 0. 3 mm. The first data collection was performed with the sample in dry conditions, and a second dataset was taken 36 h after starting the hydration process in the capillary. A monochromatic beam at an energy of 43. 575 keV (Nd edge) was focused to a size of 55 μm using aluminium compound refractive lenses. A pencil beam approach was used, having the beam size significantly smaller than the average size of the grains. The sample was scanned horizontally along the y direction (across the beam direction) with a Δy step equal to the beam width of 5 μm. The datasets were collected with an angular step of Δω=1° over rotations between 0° and 180°, and exposure time of 0.
08 s. The detector used was a FreLoN2k camera, with 2048  2048 pixels of 47. 2  47. 2 μm, placed at 154 mm from the sample. The capillary was mounted with its longitudinal direction parallel to the z direction as represented in the scheme in Figure  2a. The diffraction spots coming from the rotation of the grains were collected as 2D images from the detector. A schematic representation of the s3DXRD data acquisition is shown in Figure  2a. The s3DXRD data analysis consists of the indexing and refinement of the grains. The indexing and refinement of the polycrystalline diffraction spots were done with Jupyter notebooks based on the ImageD11 software that is part of the FABLE complete suite for 3DXRD data treatment  (Wright 2005 )  (Sørensen et al. , 2012)   (Hayashi et al. , 2015). ImageD11 permits to segment the diffraction spots from the detector images and to determine their center of mass positions.
The scattering vectors are calculated from the calibrated instrument geometry. A schematic representation of the segmentation sequence used for the two datasets is shown in Figure  2b. After locating spots for all the images, these were transformed into a 2θ-η diagram to identify the crystallographic phases and check for possible texture. For the hemihydrate sample (initial state), the majority of the grains could be indexed using the known unit cell. For the hydrated sample, only the largest gypsum crystals were indexed. This selection was done using a higher threshold of intensity for the segmentation step that allowed also to avoid problems with spot overlaps. By segmenting and indexing only the diffraction spots coming from the larger crystals of gypsum, the smaller grains were discarded.
For the indexing process of both datasets, only selected rings for the appropriate phases were used to generate trial orientation matrices (UBI matrices that are the inverse of the conventional (UB)  (Busing & Levy, 1967) ). Grains were only retained if they indexed a certain number of peaks (1000 for hemihydrate grains and 500 for gypsum grains). Then the orientation matrices and the center of mass of the indexed grains were refined and the position and shape of the crystals in the space were reconstructed within a grain map  (Hektor et al. , 2019). The iradon transformation of the sinograms, made by the indexed peaks, of each grain gave the position and shape of the grains in the grain map.
PCT In-situ PCT measurements were performed during the same experiment at ID11. The energy was not changed from the s3DXRD measurements but the setup presented few differences. Here, no focusing optics were used and the projections were recorded with an imaging detector (FreLoN4M camera with region of interest (ROI) of 500  550 pixels) placed at 300 mm downstream from the sample to enhance the phase contrast. The relatively large distance was chosen in order to give good phase contrast between the different components of the sample while retaining good spatial resolution. A 10x magnification lens was used, providing a voxel size of 1. 56 μm. Since PCT and s3DXRD data collections where acquired sequentially by changing the beam width and detector, the measurements were performed on the same sample.
A first measurement was performed before the hydration of the sample in order to characterize the dry hemihydrate grains. Following measurements were taken every 30 minutes after the injection of the solution in the capillary. The last measurement was acquired after 36 hours of hydration to match the final s3DXRD data. The projections of the sample were collected over rotations in ω = 0°<ω<360° with a total of 500 images per rotation. The datasets were reconstructed using ESRF inhouse software. Tomwer and Nabu were used to apply the dark and flat field corrections and subsequently reconstruct transversal slices of the sample with the filtered back projection algorithm  (Shepp & Logan, 1974). In order to segment grains from the surrounding solution and air bubbles, the Paganin phase retrieval algorithm  (Paganin et al.
, 2004)  was applied during the reconstruction. The most suitable delta/beta value was / = 30, giving the best compromise between sharpness and contrast to segment phases in the images. The reconstructions were segmented to locate grains of hemihydrate and gypsum and to follow the evolution of the microstructure of the plaster during the hydration. A few selected grains of both hemihydrate and gypsum, which were also indexed in the s3DXRD data, were segmented manually (with the ROI painter tool of the Dragonfly software). In this case, to allow a clearer enhancement of the edges of the grains, no Paganin filter was applied. Preliminary visualization of the images was performed using both ImageJ/Fiji  (Abramoff M. D. et al. , 2004)  and Dragonfly (version 2021. 1, Object Research System (ORS) Inc.
Grain maps Following the segmentation of the detector images of the dry hemihydrate dataset, and the extraction of the diffraction spots, we indexed 107 hemihydrate grains using the monoclinic cell I2 refined by  Ballirano et al. 2001 (Ballirano et al. , 2001). The indexing process works better for larger crystals because they produce stronger diffraction spots that are easier to locate, providing also more pixels for crystal shape reconstruction. For this study, only the crystals that were large enough to offer meaningful shape information were added to the grain map for correlation with the PCT data. While building the sinogram of each indexed grain, all of the peaks which are close to ideal scattering vectors are potentially assigned via the indexing process. Once the iradon transformation was performed on the sinogram for each indexed grain, the reconstructions were treated to remove the background noise (i.
From the indexing process of the gypsum dataset, 102 grains were indexed using the C 2/c lattice from  (Boeyens & Ichharam, 2002). In this case, a higher intensity threshold, compared to the hemihydrate dataset, was used for the segmentation process. This permitted to index the largest gypsum grains. The grain map obtained from the indexing of gypsum grains is shown in Figure  S5.
In Figure  10  two plots are shown relative to the evolution of the volume (Figure  10a ) and normalised surface area (Figure  10b ) for one grain of α-hemihydrate and one grain of gypsum during the hydration process. The two particles were segmented manually, with the Dragonfly software, from the full volumes during the hydration in order to show some quantitative measurements coupled with the morphological evolution. The red particle on the left of Figure  10  is the hemihydrate grain and the blue particle on the right is the gypsum grain that were used to calculate the volume and surface area variation during dissolution and growth. Since the two particles were segmented by hand, a segmentation range was considered. The upper limit of the segmentation range corresponded to the values of volume and surface area calculated by dilating the segmented particles.
Acknowledgements ISTerre is part of Labex OSUG@2020. Use of the Geochemistry-Mineralogy platform at ISTerre is acknowledged. The authors wish to aknowledge the  ESRF  for provision of beamtime (Experiment number MA4498). We are also thankful to  Dr. Pierre-Olivier Autran  for useful insight on tomographic reconstructions and  Dr. Marta Majkut  for discussions on grains orientation calculations. The authors wish also to thank  Dr. Irina Snigireva  and  Dr. Nathaniel Findling  for SEM characterization of the samples and  Dr. Catherine Dejoie  for complementary high-resolution powder diffraction data from the ID22 beamline of the  ESRF.
By a search in the Cambridge Crystallographic Database (WebCSD, November 2022; Groom et al. , 2016) , various structures of dihalide transition-metal complexes with the same ligand motif as 1 were found. To compare the most similar structures, only dihalide transition metal complexes with the bis[(methylsulfanyl)benzene] ligand and its oxidized derivative are focused on now. The already compared structure trans-dichloro-bis[methyl(phenyl)sulfanyl]platinum (LEQSUW; Vigo et al. , 2006)  has been published, as well as its palladium derivative with (SARWEP; Oilunkaniemi et al. , 2006)  and without (LEQSOQ; Vigo et al. , 2006)  inserted benzene. In addition, cis-dichlorobis(methylphenylsulfoxide)palladium has been published independently by two different research groups [JISWUD (Antolini et al.
The excess of the droplet was aspirated with a filter to leave a thin liquid film onto the TEM grid. The grids were then left to dry for 10-15 min before the analysis. NP size distribution and diameter was obtained by measuring the diameters of ∼300 NPs of each batch with ImageJ software (https://imagej. nih. gov/ij/). Size-histograms were fitted to a normal distribution law via Origin software. The morphology proportion of spherical (monocore) NPs and flower NPs (multicore) was determined by counting the number of each type of NP on the TEM images. For each batch, a total number of NPs N>300 was considered to determine the morphological statistics. The constituting core diameter of the multicore NPs was determined by measuring the average size of the cores on both high magnification (> 60000×) and HR-TEM images using ImageJ software.
Static magnetization curves versus field Magnetization experiments under DC field were conducted on a Vibrating Sample Magnetometer (VSM) Microsense EZ-7. For this measurement, the samples were diluted at 2 g•L -1 ɣ-Fe2O3 in HNO3 10 mM (pH∼2) and an aliquot of 20 µL was added in Ag sample holders. The magnetization was then recorded under a range of applied magnetic field intensities H from 0 to 1430 kA•m -1 (Bmax=1. 8 T) with regularly spaced data points and at least 40 measurements in the low-field region (H<475 kA•m -1 or B<0. 6 T).
[1] The ω function is defined using the densities of the isolated monomers as the Hirshfeld reference density, namely if one wished to constrain the charge on monomer A: w A r ð Þ ¼ 1 A r ð Þ 1 A r ð Þ þ 1 B r ð Þ (3) This formalism was implemented in our local versions of the deMon2k software,  [23, 24]  which we used to carry out all the cDFT calculations presented in this work. Fine adaptive grid  [25]  and density fitting  [26]  with large auxiliary basis  [27]  GEN-A3* was used to accelerate the calculations without compromising their accuracy. It has to be noted that achieving the SCF convergence in the presence of multiple constraints is more difficult than in the case of a single constraint, especially if the constrained fragments are in a direct contact, and this also limited the choice of the systems used in this work.
Model Systems In this work, we study 23 systems selected from the 3B69 data set of trimers extracted from a variety of molecular crystals. [18] It features diverse interaction motifs with different modes of interaction. Having three different trimer geometries for each compound is useful for comparison. From the 69 systems there, we have selected those to which we can apply both of the decomposition schemes described above, i. e. the trimers where two of the three monomers are not in direct contact (e. g. forming a hydrogen bond). A few more systems have been further discarded because the calculations failed to converge when two constraints were applied simultaneously in the computation of DE c 3b. The final set of systems is listed below in Table  1. We keep the numbering of the systems introduced in the original paper for their unique identification.
We also use the benchmark three-body energies calculated at the CCSD(T)/CBS level and additional DFT results from the 3B69 paper. In addition to the linear trimers, we also study the cyclic water trimer as a demonstration of the applicability of the method to more tightly coupled trimers. Again, we use the geometry from the 3B69 data set (entry 1c) which corresponds to the most stable water trimer arrangement.
Conclusions We have shown that constrained DFT can be used to isolate the contribution of charge transfer to three-body non-covalent interactions. The method has been applied to a set of 23 trimers selected from the 3B69 data set of model systems extracted from molecular crystals. It has been found that the contribution of charge transfer amounts to half of the total three-body energy (with the rest being mostly electrostatic induction). This may seem to be a larger proportion of the total value than that found for pairwise interactions in similar systems, but it must be kept in mind that the decomposition of the two-body interaction energy includes components that are not present in the three-body term. If we compare only the corresponding terms, namely charge transfer and induction, their balance in two-body and three-body interactions is very similar.
EPW is an open-source software for ab initio calculations of electron-phonon interactions and related materials properties. The code combines density functional perturbation theory and maximally localized Wannier functions to efficiently compute electron-phonon coupling matrix elements, and to perform predictive calculations of temperature-dependent properties and phonon-assisted quantum processes in bulk solids and low-dimensional materials. Here, we report on significant developments in the code since 2016, namely: a transport module for the calculation of charge carrier mobility under electric and magnetic fields using the Boltzmann transport equation; a superconductivity module for calculations of phonon-mediated superconductors using the anisotropic multi-band Eliashberg theory; an optics module for calculations of phonon-assisted indirect transitions; a module for the calculation of small and large polarons without supercells; and a module for calculating band structure renormalization and temperature-dependent optical spectra using the special displacement method.
This method exploits the spatial localization of the electron-phonon matrix elements in the Wannier representation and enables efficient calculations of electron-phonon matrix elements on ultra-dense momentum grids while retaining the accuracy of DFPT. The EPW code builds upon this methodology to compute a number of properties relating to electron-phonon interactions and temperature-dependent materials properties. EPW is the first open-source ab initio software devoted to electron-phonon interactions and has been actively developed for over 16 years. It was publicly released in 2010 under a GNU General Public License (GPL)  11  and is being distributed within the Quantum ESPRESSO materials simulation suite  12  since 2016. In addition to EPW, several software projects have been developed in recent years to address electron-phonon interactions, including: Perturbo  13  , elphbolt 14 , Phoebe  15  , and EPIq  16.
All these packages rely on the same interpolation methodology employed by EPW  10. Two prior manuscripts  17, 18  describe the status of the EPW code until 2016. The present manuscript aims to describe progress on the code that has occurred since 2016 as well as ongoing efforts, leading to the latest EPW release. Added functionalities involve: a magneto-transport module for the calculation of the conductivity in metals as well as the drift and Hall mobility in semiconductors; a module for the calculation of small and large polarons without using supercells; a module for the solution of the full-band Eliashberg equations for superconductors; a module for calculations of phonon-assisted indirect optical processes; and a module for calculations of finite-temperature electronic and optical properties via the special displacement method.
The code has been refactored to keep up with contemporary developments in high-performance computing (HPC) architectures, for example via hybrid two-level message passing interface (MPI  19  ) and Open Multi-Processing (OpenMP  20  ) parallelization, and parallel I/O via parallel Hierarchical Data Format 5 (HDF5  21  ). The manuscript is organized as follows. In the 'Methods' section (located at the end of the manuscript), we describe the notation employed throughout this manuscript, we review the conceptual basis of electron-phonon interpolation using DFPT and MLWFs, and we discuss the extension of this methodology to the case of polar materials with long-range Coulomb interactions. In the 'Results' section, we describe the additional or expanded functionalities available in the latest release of the EPW code.
(  1 ) and (  6 ); (ii) the density of the coarse grids used for the Wannier-Fourier interpolation of the DFT and DFPT data 22 ; (iii) the Gaussian smearing employed to compute the Dirac delta functions appearing in Eq. (  1 ); and (iv) to check for the sensitivity of results to lattice parameters, exchange and correlation functionals, and pseudopotentials. To facilitate the convergence with respect to the Gaussian smearing and the k-point sampling, EPW offers the possibility of using the adaptive smearing method of ref. 44. Application example. To demonstrate the implementation of the transport module in EPW, we investigate the electron and hole mobilities of cubic boron nitride, as well as the associated Hall factors. We use the relativistic Perdew-Burke-Ernzerhof (PBE) parametrization  45  of the generalized gradient approximation to DFT.