dataesr/scientific-paragraphs-categorization huggingface dataset:
The selected SLE data have been smoothed with the NRTL model given by eqs. (  1 )-(  5 ) with the use of the ThermoData Engine software  [8]. The parameters of the resulting NRTL fit for benzoic acid (1) + water (2) are presented in Table  10. The table also contains the melting parameters for both mixture components used in the modeling. For water, these parameters were as described in the urea + water section. For benzoic acid, the melting temperature and enthalpy of fusion were taken from the recommendations in  [50] , which are based on a precise experimental work  [54] ; the Δ fus C p,m value was taken as the average of two values obtained by adiabatic calorimetry in the vicinity of the melting point  [55, 56]. Since T tp , Δ fus H m , and Δ fus C p,m were assigned fixed values during the NRTL fitting, any inaccuracy in them was absorbed by the fitting parameters α and A i through D i , so their uncertainties were not needed for the purposes of the present project and thus were not evaluated.
Component i Parameters of eq. (  2 ) Melting parameters for eq. (  3 ) A i B i C i D i α T tp /K Δ fus H m / (kJ mol -1 ) Δ fus C p,m / (J K -1 mol -1 ) Component  (benzoic acid)  Another well-studied system featuring low solubilities is naphthalene + water. The solubilities for this system have been compiled and critically evaluated in SDS-81-9  [57]. The available literature sources associated with the SLE data for this system are listed in Supplement 2P. Fourteen sources in addition to those cited in SDS-81-9 have been included in the list. Because of these additions, it was decided to update the solubility equation. Most of the experimental SLE data are in fair agreement with each other, except for seven values from Refs. [58] [59] [60] [61] [62] [63] , which look obviously erroneous (Figure  7 ) and thus were rejected.
Additional notes: excluded system and reserve systems We considered including the anthracene + water system as a reserve system due to its extremely low solubility and the large number of fairly consistent measurements available. The solubilities for this system have been compiled and critically evaluated in SDS-81-11  [64]. The available literature sources associated with the SLE data for this system are collected in Supplement 2Q and are shown in Figure  S17. Unfortunately, two distinct clusters of the SLE data are apparent. A possible explanation is the presence of phenanthrene as an impurity in commercial anthracene  [65]. Phenanthrene is about 20 times more soluble in water than in anthracene  [64] , and it forms a solid solution with anthracene  [66] , which potentially explains the higher solubilities reported in some works.
1A , while Fig. 1B  shows the particle size distribution obtained from the analysis of more than 700 particles. The average value (1. 4 ± 0. 4 nm) is in good agreement with the dispersion determined by H 2 /O 2 titration, and was previously confirmed also by EXAFS data analysis. 49 Temperature programmed reduction (TPR) was performed to evaluate the reducibility of the Pt phase. The main reduction peak (Fig. 1C ), which is due to the Pt 2+ → Pt 0 reduction, starts at a temperature as low as -30 °C, reaches its maximum around 55 °C and ends around 130 °C. The integrated area below the peak delivers the overall hydrogen consumed in the process: it accounts for a Pt/H ratio of 3. 6, indicating that the majority of the Pt phase was in the PtO 2 form in the pristine catalyst.
Characterization Ex situ X-ray powder diffraction (XRD) data were collected using a PANalytical Empyrean X-ray diffractometer equipped with a Bragg-Brentano HD mirror and operated at 45 kV and 40 mA using CuK α radiation (λ = 1. 5418 Å). The materials were examined within the 2θ range of 5-90°using a step size of 0. 0167°. The scan time per step was 1 s. Thermogravimetric analysis (TGA) experiments were performed in a Mettler Toledo TGA/DSC 3 instrument. Typically, 750 μL of a colloidal solution of d-MoO 3 was placed in a sapphire crucible (900 μL) that was heated to 80 °C (5 °C min -1 ) and kept for 1 h. Scanning electron microscopy (SEM) was performed on a Zeiss LEO Gemini 1530 microscope. All electron microscopy images were taken at an acceleration voltage of 5 kV. Prior to imaging the materials were coated with a ca.
XAS spectra were collected at the Mo K-edge using a doublecrystal Si (111) monochromator with continuous scanning in transmission mode. The in situ carburization followed by dry reforming of methane (DRM) experiment was performed in a quartz capillary reactor. 11, 17, 25  was handled in a N 2 -filled glove box to prepare specimen for XAS analysis in air-tight sealed Al bag. XAS data were processed using the Athena software (Demeter 0. 9. 25 software package). 26 The extracted extended X-ray absorption fine structure (EXAFS) data were k 3 -weighted and the Fourier transform performed in the k-range 3-10 Å -1 for data collected during thermal treatment and 3-12 Å -1 for that collected at room temperature. The in situ time resolved normalized X-ray absorption near edge structure (XANES) data were analyzed using a MCR-ALS method.
Acknowledgements This publication was created as part of  NCCR Catalysis  (Grant Number  180544 ), a  National Centre of Competence in Research  funded by the  Swiss National Science Foundation. We acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 819573 ), and from  ETH Zürich  ( ETH-40 19-2 ). The Swiss Norwegian beamlines (SNBL at  ESRF) facility  is acknowledged for provision of beamtime. The authors thank  ScopeM  for the use of their electron microscopy facilities. Dr. Elena Willinger  is thanked for her assistance with the TEM data analysis of the spent Mo 2 C-nb catalyst.
The ATR accessory was a horizontal ZnSe crystal coated with diamond (A = 2. 54 mm 2 ) with single reflection and an angle of incidence of 45°(Smart Miracle from PIKE). The spectra were recorded with resolution 2 cm À1 and were averaged from 64 scans in order to minimize duration of the contact of samples with air and corrected by corresponding refractive index. Although some absorption of moisture from the air is possible during the measurement, we assume that its effect can be neglected if compared with the initial water content which was several wt. % even for the less concentrated in water samples (see Table  1 ). A droplet of the sample was placed on the diamond ATR crystal and the measurement started immediately, five spectra were recorded for each concentration. OMNIC software was used for the data collection and treatment.
MD modeling methodology Gauss-View 6 was used for visualization  [62]  together with Gaussian 16  [63]  for calculations in junction with the Antechamber module  [64, 65]  to assign the Restrained Electrostatic Potential (RESP) charges for each atom. As the protocol requires, Gaussian structure optimizations were carried out at Hartree Fock level of theory with the standard 6-31* basis, as required by the RESP protocol  [66]. We performed different simulations of one hundred pairs of IL cations and acetate anions, and an appropriate number of water molecules were added to reproduce desirable water concentrations. The molar water fractions v(H 2 O)were 0. 500, 0. 667, 0. 918 for ILA and 0. 538, 0. 700,0. 875, 0. 903, 0. 954, and 0. 963 for ILE. More detailed description of each mixture can be seen in Supplementary information, Table  S1.
Digital image correlation The digital image correlation technique has been widely used in the field of mechanics for assessing the experimental strain fields  (Lecompte et al. , 2006). This method has also been used in the field of compacted powders and particularly in the pharmaceutical field  (Mazel et al. , 2016a; Stirling et al. , 2013)  and was used in this study to assess the deformation of the tablet surface during the diametral compression test. Before the diametral compression test, the front surface of the tablets was colored with black ink to create a speckle. This speckle allows for the use of image correlation technique  (Mazel et al. , 2016b). The images from the high-speed camera were processed using the software Vic-2D (v. 6, Correlated Solutions). The chosen output was the normal strain in the direction perpendicular to the loading diameter and parallel to the tablet surface.
Numerical modeling Finite Elements Modeling Abaqus® software (Dassault Systèmes, Vélizy-Villacoublay, France) was used to undergo numerical simulations of the die compression and the diametral compression test of presscoated tablets. In the same way as different previous studies  (Picart et al. , 2021; Diarra et al. , 2013) , die compression was simulated using the Drucker-Prager-Cap model, with the parameters of microcrystalline cellulose. For the diametral compression test simulation, a linear elastic model was used to analyze the stress and strain distributions in the tested tablet before failure. The elastic properties of the model were defined according to the die compaction simulations, as presented in Table  2. The geometry of the tested tablet was simplified in three sections: the core, the layers, and the band (as presented in Fig.
Diffractograms were obtained over 10 min and 5 min in the case of isothermal conditions and temperature ramp conditions, respectively. The raw scattering data is available in the ILL data repository  [48]. The diffraction pattern of the empty sample cell was subtracted from all measurements, yielding a pattern consisting of NaAlD 4 /CB and the incoherent background signal caused by the applied D 2 pressure (p D2 ) (Fig. S1a ). As the equilibrated temperatures were not equivalent between the applied routine and the empty sample cell measurements, the signal subtraction was not perfect and four 2q regions were excluded from further analysis, between 44 and 45. 5 , between 63 and 66. 5 , between 98 and 100 , and between 115 and 118 (Fig. S1a ). Data reduction and initial analysis were performed with OriginPro 2021b (OriginLab Corporation, Northampton, MA, USA) software package.
4a  and Fig. S4c ) and the consistency of the crystalline phase during the 4th cycle, even though vacuum was applied during the 4th cycle (Fig. 3  and Fig. S4c ). Molar ratios of the crystalline phases determined by refinement of NPD data are presented in Fig. 4c. Based on the NPD data refinement only the first two decomposition steps are evoked at the applied T and p conditions. Thus, the total crystalline phase stoichiometry can be summed up based on Eq. (1) and Eq. (  2 ): 3NaAlD 4 4 Na 3 AlD 6 þ 2Al þ 3D 2 4 3NaD þ 3Al þ 4. 5D 2 (4) The presence of Al, n Al /n sum of around 0. 1 mol Al mol À1 , in the pristine NaAlD 4 /CB, is most likely caused by the preliminary decomposition of NaAlH 4 and NaAlD 4 during the ball milling and deuteration routines applied, respectively, during material preparation.
Acknowledgements The authors wish to express their gratitude for the beamtime as well as the great support offered by the staff of  Institut Laue-Langevin (ILL). This work was supported by the  Estonian Research Council Grants  ( PUTJD957 ), by the  EU  through the  European Regional Development Fund (Centers of Excellence ,  TK141  " Advanced materials and high-technology devices for energy recuperation systems "), as well as by the  Swedish Research Council  through a  neutron project grant  ( Dnr. 2016-06955  and Dnr. 2021e06157). E. N. is fully funded by the  Swedish Foundation for Strategic Research (SSF)  within the  Swedish national graduate school in neutron scattering (SwedNess ).
Optical Attenuation Measurement with LDV Optical measurements of attenuation might be insightful as they allow acoustic mechanisms as diffraction and propagation losses to be distinguished from other possible non-acoustic sources of loss, such as modifications of the electromechanical coupling, transduction coefficient, or other second-order effects. To obtain data on the energy distribution in the acoustic beam, complete cross-sectional amplitude profiles were acquired in the propagation region between the emitting and receiving transducers. The location of the scan profiles is shown by red diamonds in Figure  1. Each was formed by 245 scanning points spaced by ∆y = 15. 2 µm. In total, there were 7 profiles separated by ∆x = 0. 5 mm distance. Thus, 123% of the 3 mm IDT aperture was covered by the 3.
The κ p value for the non-covered sensor has been obtained by numerical simulation using FEMSDA (free software)  [48, 49]. This value has been used as an initial guess in the fitting procedure for the resting-covered sensors. Indeed, for a SAW delay line manufactured on a weak piezoelectric material, such as quartz, the ripples in the admittance curves are almost directly influenced by κ p  [31, 50]. These ripples in the conductance curve are referred to as ∆G in Figure  5b. Achieving the same amplitude of ∆G for measured and simulated curves allows a reliable extraction of the reflection coefficient value. The transduction coefficient for the non-covered sensor, α p has been derived from value of the effective electromechanical coupling coefficient K 2 numerically obtained with FEMSDA  [31, 50] : α p = η K 2 ω 0 C n W/λ 0 , (4) where η is the IDT element factor, which is equal to 0.
78 cm 2 and comparable electrochemically active areas (curves 1 and 3 in Fig. S1 ). The platinum mesh and silver wire were used as counter and pseudo reference electrodes, respectively. The polymerization progress was controlled by the charge integration function implemented in EC-Lab software version 11. 31 from BioLogic. Both electrodepositions were accomplished by passing ~30 mC through a polymerization cell. The polymerization charges constituted the sum of the integrals under subsequent current-time polymerization curves. After electrodeposition, electrodes were rinsed with the abundant propylene carbonate solvent before further use. The electrochemical responses were recorded, in a three-electrode system, in a monomer-free solution of 0. 1 M (TBA)PF6 in propylene carbonate.
Apparatus All electrochemical experiments, including mesoporous silica deposition under potentiostatic conditions, polymer growth under potentiodynamic conditions, as well as the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements, were performed with a VSP electrochemistry system of BioLogic (SP 150) Science Instruments controlled by EC-Lab software of the same manufacturer. Before the EIS measurements, mesoporous silica and poly(meso-NiSaldme-3dMe) wires composite electrodes were equilibrated by applying selected potential for 30 s. After that time, the current reached equilibrium, and then EIS measurements were performed at the voltage amplitude of 10 mV in the frequency range of 100 kHz to 10 mHz. AFM imaging was carried out in the PeakForce Quantitative Nanomechanical Mapping (QNM) mode, in air, using a Bioscope Resolve (Bruker Nano Surface, Bruker France SAS, Palaiseau, France).
Since DFT calculations require a much longer time than semi-empirical ones, therefore, initial semi-empirical calculations were used to start DFT optimization as close as possible to the stationary point of molecular geometry. During the selection of appropriate functionals for the calculations on the DFT level, dispersion component and long-range correction were taken into account. 50 The high accuracy of calculated parameters and relatively high efficiency of computation process was ensured by utilization of empirical D3 correction  50  available as an option for CAM-B3LYP  51  and LC-ωPBE  52  functionals implemented in Gaussian 16 (G16) software package  53  with 6-31 ++ G(d,p) basis set. We obtained the lowest values of the Gibbs energy, ∆G, for anti-parallel π-stacked dimers from CAM-B3LYP and LC-ωPBE functionals.
Acknowledgments: The authors want to thank all partners of the  THyGA  project consortium, especially the following persons for discussions and/or administrative help during the  THyGA  project:  Kris de Wit ,  Andrea Manini ,  Maurizio Beghi ,  Philipp Fischer ,  Manfred Lange ,  Lisa Blanchard ,  Laurent Briottet ,  Krishnaveni Krishnaramanujam ,  Jörg Endisch ,  Regis Anghilante ,  Robert Judd ,  Alexandra Kostereva  and  Alberto J. García Hombrados.
CALculation of PHase Diagram (CALPHAD)-assessed thermodynamic databases Although considerable research has focused on defining new compositions for metal hydride materials, there is still a need for public databases of thermodynamic and structural data, which are currently dispersed in the literature. Developing these databases will accelerate efforts to determine the design rules that dictate thermodynamic properties across the wide chemical and crystallographic space  [25]. A powerful approach is the CALPHADs method, which can fit and model experimental and ab initio data for metal hydrides across wide compositional, temperature and pressure ranges. Supported by DFT  [26] , CALPHAD assessments provide a thermodynamic description and model of experimental data that can interpolate properties of multicomponent systems and be used to compute phase equilibria and predict reaction hierarchies  [27, 28].
CALPHAD database development and optimization, which integrates various thermodynamic data to create self-consistent models, opens the way for exploring multicomponent materials. It can also compute thousands of thermodynamic data that can be used to develop and validate machine learning (ML) models  [29]. The understanding of thermodynamic properties and structure-chemistry relationships will pave the way for prediction and validation of enthalpy and especially entropy values of hydriding reactions that are still unknown. Tailored CALPHAD approaches are being developed to assess specific H storage materials, including the mixing of different metal hydrides. The goal is to anticipate positive interaction effects on the H storage properties of hydride mixtures or the assessment of ternary compounds.
An example is the thermodynamic description of the NaMgH 3 phase, for which the available literature data were critically assessed to compute the equilibrium pressure-temperature phase diagram  [30]. In another example, the thermodynamics of hydriding-dehydriding in simple hydrides (NaH, AlH 3 and CaH 2 ) has been included in CALPHAD assessments, providing evidence for either the formation or hindering of a stable hydride-fluoride solid solution that enables a reversible hydriding reaction  [31, 32]. Moreover, CALPHAD assessments are able to support further thermo-fluid-dynamic modelling of H absorption and desorption in a LaNi 4. 8 Al 0. 2 material for application in a hydride bed  [33, 34]. The development of a unique thermodynamic database for H storage systems by the CALPHAD approach is fundamental and can be implemented by a collection of literature and experimental data, to be integrated with estimations of the energy of formation of hydrides by ab-initio calculations  [35].
Alvares et al demonstrated the use of CALPHAD to assess thermodynamic parameters under these non-equilibrium conditions by considering a pseudo-binary system in which the different metals are considered as one species and by applying parameters of the ordered B2 phase taken from a previously assessed order-disorder model of the TiFe body-centred cubic (BCC) phase. Despite the crystallographic complexities, especially concerning the phase transition between the hydrides, this CALPHAD assessment agrees with most previously reported thermodynamic data and represents the first complete assessment of a system solely utilizing the open-source thermodynamic software OpenCalphad  [38, 43, 44].
The expression for µ M (y) can be obtained employing the CALPHAD framework or other available thermodynamic databases. The calculation of p eq (y) using equation (  5 ) finally yields the PCT diagram, as shown for the hydriding of Mg 3 Cd in figure 4  [60]. Equation (  5 ) can be used for estimating the effects of various factors on the PCT diagram. Let us compare the potentials to increase the plateau pressure in the PCT diagram by manipulating the alloy's thermodynamics, and by elastic stresses in the hydride phase. A good order-of-magnitude estimate of the increase in plateau pressure that can be achieved by manipulating the alloy's thermodynamics can be made by assuming µ M ≈ -RT. This corresponds to assuming that the configurational entropy term is the leading one in defining the Gibbs free energy of the solid solution.
Mechanism and kinetics of H sorption in a Zr/Ti-based Laves type alloy A recent detailed study on the kinetics of H interaction with the Ti/Zr-based Laves phase alloys  [278]  was focused on Ti 0. 15 Zr 0. 85 La 0. 03 Ni 1. 126 Mn 0. 657 V 0. 113 Fe 0. 113 that belongs to a family described above and contains La to improve the activation performance and cycling stability. The composition was selected based on in-house research performed at IFE on AB 2±x C15 Laves-type alloys for their application as H storage and battery electrode materials. Experimental data on H absorption and desorption were collected at isothermal conditions using a single-step charge/discharge while assuring a strict temperature stabilization of the sample. The experimental and modelling study of sorption kinetics was based on the application of the JMAK model modified by introducing pressure-and temperature-dependent rate constants.
The temperature difference from the centre ∆T = T -T c has a parabolic profile along the radial direction ∆T = mr 2. The effective thermal conductivity can be determined as: k eff = Qρ/4m ( 9 ) where Q is the heat rate and ρ is the density of the hydride bed. The analysis of the data in figure  16  yields as-compacted to 7. 5 W (m K) -1 after cycling  [288] ). The experimental setup allows the dependence of k eff on p (H 2 ) and T to be determined  [283, 289] ; finally, the obtained values can be applied to model and design scaled-up H storage tanks. k eff = 9 W (m K) -1 at p (H 2 ) =.
This shows that the inverse relationship between VEC and hydride stability applies also at a local atomic level. Attempting to predict hydriding properties from a single parameter is the simplest approach, while a more comprehensive solution is to model HEA hydride thermodynamic properties via ML/data-driven techniques. Using gradient boosting tree models trained on the historical HydPARK database  [301, 302] , showed that compositional featurization  [303, 304] , i. e. in which material's features do not directly rely on the crystal structure, led to the predictive capabilities for the enthalpy of hydriding and to some extent the entropy of hydriding. Consequently, the equilibrium plateau pressure (and thus the Gibbs free energy of reaction) could be reliably predicted. Furthermore, the use of 'explainable' ML tools revealed simple, first-order design rules that, while not as accurate as the ML model itself, provide intuitive understanding of the model predictions and establish a correlation of the hydride stability with purely physical characteristics  [302].
Future efforts will enable extensions of this model to identify HEA with other desirable properties, such as high H capacity. It is worth noting that the most significant limitation of this approach is the lack of a standardized and centralized database, as HydPARK has many limitations  [302]. This issue has been largely resolved in the other areas of the materials research  [305, 306]. A concerted effort within the H storage community to develop centralized repositories for accurate H storage data in standardized formats will be essential to unlock the full potential of data-driven approaches for the metal hydride discovery.