dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the United States Government. C E B, M P P, and T D H (Curtin University) acknowledge financial support from the Australian Research Council for Grants DP150101708, LE0989180 and LE0775551. T S (Shibaura Institute of Technology) acknowledges support from JST SICORP (JPMJSC 1802). S O (Tohoku University) acknowledges support from MEXT/JSPS KAKENHI 'Hydrogenomics' (JP18H05513). J-Y Suh and Y W Cho (KIST) acknowledge funding from National Research Foundation of Korea (NRF-2020M1A2A2080881). S L (University of Nottingham) acknowledges support from EPSRC (EP/V042556/1) and the use of ARCHER2 supercomputer through membership of the UK's HPC Materials Chemistry Consortium funded by EPSRC (EP/R029431/1).
Acknowledgments This paper was realised within the framework of the  Hydrogen Technology Collaboration Programme (TCP)  of the  International Energy Agency (IEA) in Task 40 Energy Storage and Conversion Based on Hydrogen. Some results reported in this publication were obtained thanks to funding from the  Fuel Cells and Hydrogen 2 Joint Undertaking (JU)  under Grant Agreement No. 826352 ,  HyCARE  project. The JU receives support from the  European Union 's  Horizon 2020 research ,  Hydrogen Europe ,  Hydrogen Europe Research, innovation programme and Italy, France, Germany, Norway , which are all thankfully acknowledged. V A Y,  R V D, M V L ,  S S ,  J B V C  and M D greatly acknowledge support from  EU Horizon 2020  programme in the frame of the  H2020-MSCA-RISE-2017 action ,  HYDRIDE4MOBILITY  project, with Grant Agreement 778307.
A schematic engine layout is provided in Figure  1. The engine was installed and fully instrumented in a dynamic test bench, which was managed with the AVL PUMA software. The engine speed and torque were regulated by means of an AVL AFA 200/4-8EU dynamometric brake. The AVL PUMA software was also used to record the main pressure and temperature values, air and fuel mass flows, turbocharger speed, and engine raw emissions, all with a 20 Hz acquisition frequency. The location of the pressure (Kistler 4260A piezoresistive type transmitters, Kistler Group, Milan, Italy) and temperature (3 mm K-type thermocouples) sensors can be observed in Figure  1. The turbocharger speed and the air and fuel mass flows were acquired with the devices MICRO-EPSILON DZ140, AVL FLOWSONIX, and AVL 733S, respectively; the HORIBA MEXA-ONE gas analyzer was utilized to register the measurements of NOx, HC, CO, CO 2 , and O 2  [30].
Moreover, three Kistler 601CAA piezoelectric type sensors (Kistler Group, Milan, Italy) were installed to measure the instantaneous pressure traces at the compressor outlet and both manifolds, while four AVL Z133 spark plugs with integrated pressure sensors were utilized to register the in-cylinder pressure traces. All these signals were recorded with a sampling of 0. 2 crank-angle degrees (CAD) by means of the PXI 6123 and PXI 6251 acquisition modules, both programmed with Labview by National Instruments TM (Austin, TX, USA)  [31, 32]. The original engine control unit (ECU), designed for the base engine without EGR, was partially bypassed with the ETAS ES910 prototyping and interface module to enable any variations on the VVT system, throttle position, spark timing, and injected fuel. The EGR valve, intake flap, and VGT position were directly controlled in open-loop configuration, independently of the ECU, through the PXI 7813R and NI 9759 control modules  [31, 32].
The binding values are obtained by dividing the absorbance at 405 nm of each testing well B (the absorbance measured when OTA-HRP and unlabeled OTA are in competition with SNLHPK peptide) by the absorbance of the positive control well B0 (maximum absorbance obtained with OTA-HRP). Decreasing exponential functions (y = y 0 + A. e -R0x ) were performed on the standard curve B/B0 = f (log [OTA]) by using Origin Lab software. The limit of detection (LOD) was obtained from the equation for y = A, the maximum with y = y 0 and IC 50 with y = 50.
The results obtained using a 2D configuration with Infrared microscopy were in a good agreement with literature data that were obtained under experimental conditions where the polymer could swell in 3D configuration. Finally, some of the results obtained in this study After sample positioning, the cell was heated up to the chosen temperature (40 °C or 130 °C). Once the temperature was reached, a first spectrum was recorded without CO 2 (red location shown in Figure  1 ). Then, CO 2 was injected at the desired pressure (5 -10 -20 -30 MPa). During the pressure stabilization a spectrum was recorded every 5 min to follow the CO 2 sorption in the material over time. In this study, the equilibrium was generally reached after 30 to 90 min. At the end of the experiment, samples were taken out of the cell as transparent pieces (unfoamed) or slightly white pieces (close to the beginning of foaming when the cell was quickly depressurized).
Characterization Studies The synthesized CNPs and DCNPs were stored at 4 • C. Morphological features of the nanoparticles were studied in a High-Resolution Transmission Electron Microscope (HR-TEM), which provides an initial confirmation that the synthesized nanoparticles are in the nano range  [23]. The average size, ζ-potential and dispersity index of the nanoparticles were analyzed by Malvern Zetasizer Nanoseries compact spectrophotometer (Malvern Instruments Ltd. , Malvern, UK). The analysis was performed in triplicate to obtain statistically significant data  [24]. Finally, DOX, CNPs and DCNPs were analyzed by Fourier Transform Infrared spectroscopy (FTIR) to validate the available functional groups. A Spectrum RX1 instrument provided with diffuse reflectance mode at 4 cm -1 resolution of KBr pellets was used.
Evaluation of ECG Alterations Twenty-five days after commencement of the experiment, the animals were anesthetized by intraperitoneal injection of ketamine hydrochloride and xylazine cocktail (ketamine hydrochloride 50 mg/kg; xylazine 5 mg/kg) and kept in supine position. The electrocardiogram (MP 100, Biosystems, Inc. , Cumming, GA, USA) was used to record the classic limb leads (right arm, left arm and left leg, respectively, for aVR, aVL and aVF) at 50 mm/s and 20 mV/mm. The amplitude of the traces between the peaks (QRS value) and intervals of ECGs, such as PR-, ST-and QT, and heartbeat rate, were recorded using data acquisition software (Acknowledge 3. 9. 0, Goleta, CA, USA).
Real-time PCR was performed in a total volume of 20 µL, with 10 µL of 2× SYBR Premix Ex Taq, Tli RNaseH Plus (Takara Bio Inc. , Cat. #RR420A), 1. 0 µL of each primer (3 pmoles each), 2. 0 µL of cDNA and 6. 0 µL sterile H 2 O, and was conducted in triplicate for each sample in a Roche light cycler 96 L600 Real-Time PCR system. Primers were designed manually using the sequence within the coding domain, adopting the online software oligo-analyzer, reverse complement and similarity searches in BLAST. The primers were purchased from Shrimpex Biotech Services Pvt. Ltd. (Chennai, India). 18S rRNA and β-actin were used as the endogenous references to normalize gene expression, and 2 -∆∆CT method was used to determine the differences in mRNA content between the samples  [26]. Details of the primers and the size of their amplification product are as follows: Gene Sequence Desired size PPARγ F 5 -GCC AAG GCG AGG GCG ATC-3 180 bp PPARγ R 5 -CAC GGA TCG AAA CTG GCA CC-3.
Acknowledgments: B. G. and P. P. thank the  Imaging Probe Development Platform (IPDP)  and are grateful for the support from the  Cognitive Neuro Imaging Centre (CONIC)  at  NTU. S. A. acknowledges the grants received from RUSA 2. 0, ICMR,  UGC Research Award, UGC Innovative Programme ,  UGC-SAP, DST-FIST, DST PURSE  and  UGC  non-SAP were of great support in the execution of this research. English editing was done by  Sayantan Ghosh ,  Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, India.
ACKNOWLEDGMENTS This work was partly supported by the  Research Council of Norway  through its  Center of Excellence fund-ing scheme , project number  262644. Further support, also from the  Research Council of Norway , was provided through its  INTPART program , project number  309139. This work was also supported by " Investissements d'Avenir du LabEx "  PALM  ( ANR-10-LABX-0039-PALM ).
(51) Let us search for {θ t+∆t i+1 }, such that R({θ t+∆t i+1 }) = 0. From equation (51), it reads {θ t+∆t i+1 } = {θ t+∆t i } - ∂R({θ t+∆t i }) ∂{θ} -1 R({θ t+∆t i }) = {θ t+∆t i } -K({θ t+∆t i }) -1 R({θ t+∆t i }), (52) where [K] represents the stiffness matrix. Equation (  52 ) is solved for explicitly, since [K(θ t+∆t The shell finite element code uses Shelddon libraries, designed by Inria 2. The latter were modified to solve the solid problem (i) with the displacement as an input and (ii) for any material law for membrane effects. The global structure of the numerical scheme remains unchanged but the following changes are made: • The subroutine to solve Eq. (  49a ) is modified to determine {q s }, the classical unknown being the load {u s }. • The coupled terms and the terms corresponding to the curvature tensor κ αβ are set to zero.
Excess-Gibbs energy models and electrolyte equations of state As expected, the choice of the modelling approach to use will depend on the application of interest (VLE, LLE, SLE, other properties) but also on the availability of model parameters for the applications of interest and especially the familiarity with the specific approach and availability in commercial software. The (non-)availability of model parameters is of some relevance for commercial models, although there is the option in many of these commercial models to fit the parameters if experimental data are available. For all these reasons, activity coefficients (G E -approaches) are by far the ones most often used in practice as they are the only ones available in most software tools. In general terms, among those G E models, the model which has been mentioned by many in the discussion was electrolyte NRTL (eNRTL), possibly due to its availability in several simulators and software packages (Aspen Plus, AVEVA, Simulis Thermodynamics).
They require fewer parameters than electrolyte Gibbs excess models, but today they are not used as often as the activity coefficient models in practice. Interestingly, there has been very little discussion about properties which can only be predicted by e-EoS e. g. densities at high pressures. When searching for a predictive approach, it has been mentioned that at least in one software tool (Simulis Thermodynamics) a predictive Gibbs excess model based on modified UNIFAC is available (the ULDPS model). This model has specific groups for ions, that take into account short-range interactions, while a Pitzer-Debye-Hückel term is used for long-range interactions, and which also takes into account species solvation. Interestingly no/very few publications of this approach exist and there are no comparative validations against the other popular G E models mentioned above.
Again, there is no consensus, and please notice that eNRTL is a salt-specific model. A full section is devoted to data and databases, see below. 4. Conditions. The modelling of aqueous solutions at ambient and non-extreme conditions is quite well-understood, yet the modelling of mixed solvents and/or extreme conditions such as high concentrations and high temperatures are far more challenging. For such conditions, temperature-dependent model parameters may be required as the behaviour of electrolytes with temperature is non-linear. Using models that are not validated to higher temperature and pressure is not recommended and validation through temperature (and pressure-) dependent LLE, SLE or VLE is required. Predictive approaches may not always be successful but they may be useful at extreme conditions where little or no data are present and at high concentrations / high temperature where ion pairing occurs and dielectric constant is decreased.
Databases and model parameter estimation The databases and the parameter estimation of electrolyte models were topics heavily debated in the workshop. On one hand, there is satisfaction about the availability of many databases that several of the participants have access to, e. g. Detherm, LLNL, Thermoddem, NIST, ThermoXL, Thermochimie, PhreeScale, FactSage (for solid compounds), ProPhyPlus (for pressure application requiring an EOS for the vapour phase), and others, including the REAK-TORO or JESS frameworks for chemically reactive systems. The literature on databases is indeed extensive. There were, however, some complaints concerning the lack of open and easily accessible data sources. In response, other companies expressed the view that maybe the available commercial databases are actually sufficient but more structured databases are needed, with the data also available in digital form.
The reason why databases and experimental data were such a "hot topic" is that there is a broad consensus amongst participants that for all typical electrolyte models, a large number of adjustable parameters are required. There was a general feeling that electrolyte models seem to require a lot of parameters which are often difficult to properly regress. These must be fitted and validated using various types of experimental data, including vapour-liquid equilibria, osmotic and activity coefficients in completely dissociated aqueous systems, solubility of solid salts in water, organic solvents and mixed solvents, densities, heats of mixing and dilution, heat capacities and various speciation-related data (pH, acid dissociation constant as a function of solvent composition) as well as Gibbs energy of transfer of electrolytes.
g. a clear methodology to fit parameters is required. In addition, many companies emphasize that e-NRTL parameters may be missing in the database for specific systems of interest to some applications e. g. radioactive and toxic compounds. Despite the lack of much discussion on alternatives, the companies in the survey stated that improved and increasingly predictive models are required for electrolyte solutions, with limited need for adjustable parameters and data regressions. The predictive capabilities should include high concentrations, and speciation behaviour; both areas where significant improvements are needed. Many companies expressed concerns about most existing models requiring large datasets or having no predictive power outside the experimental range (in particular the difficulty to predict multicomponent systems from lower order systems, like ternaries from binaries).
The survey included very few comments on equations of state for electrolytes e. g. those based on SAFT, since they are not available in (most) commercial simulators. A single company noticed that there have been some positive developments, such as the inclusion of Coulombic interaction terms in SAFT, while another company mentioned that the development of models (GC PPC-SAFT) that could be used in Aspen Plus would be a major improvement. Not everybody is fully satisfied with e-NRTL and some mention that testing electrolyte SAFT models should be given more consideration. The authors of the survey manuscript concluded that electrolyte thermodynamics is an area where development of new models require advancement of fundamentals e. g. the role and importance of the Born term; the description of ion-solvent interactions; the role of individual ion activity coefficients (even if they are not entirely accepted by the community) and more.
Collaboration and recommendations to the community The collaboration opportunities have been extensively discussed within the area of electrolyte thermodynamics and there is a general consensus that it is important to create common ground for exchange of information between industrial and academic experts as well as software providers. There is quite some diversification in the field with many research teams having own tools and databases, but this should not prohibit discussion on how some consolidation can be achieved. Joint Industrial Projects (JIP) is one way to have such a way of working and the Elether JIP [12] is mentioned by many as an exemplary and successful initiative. The creation of consortia like those already existing for pure components databases (DIPPR from AIChE) or group contribution predictive models (UNIFAC Consortium) is also an interesting track that creates a synergy on the long term, with clearly established development axes by the different members of these consortia.
A careful preparation prior to the creation of these JIP is however emphasized. Well-defined targets will facilitate to bring the case to decision-makers. In addition, the findings should be implemented in some commercial simulation tools (which modelling approach is the best, best practices in parameter regression etc. ). The role of software vendors is, thus, quite relevant in this context. Some examples of interesting targets for such collaboration have been mentioned: 1. To develop or agree upon a small number of benchmark cases/challenges which could allow discussions without confidentiality complications. 2. To develop a "standard" database for model development and/or model validation, as recently proposed for nonelectrolytes by Jaubert and co-workers  [13]. A consolidationcombination of available electrolyte databases would be a useful, albeit difficult task.
Of course, such a "standard" database will still be simply used as guidance as the range of relevant systems for electrolyte applications is very extensive. 3. To stress and prove the importance of electrolytes to current technology, including a wider range of applications (Power-to-X, batteries, etc. ) and climate challenges in a way that is easy to demonstrate to policy makers and management levels. 4. To create a network of laboratories with special expertise in experimental methods -measurements relevant to electrolytes. 5. To develop a common tool for optimisation, calculation with various models, with the same format for all experimental data (that could be shared) would probably enhance further collaborations on these topics and the investigation of the use of openaccess platforms is recommended.
Acknowledgements The authors wish to acknowledge all participants that have provided input in the discussion, and very specifically the panellists who have provided sometimes very detailed replies on the questions. Georgios M. Kontogeorgis  wishes to thank the  European Research Council (ERC)  for funding under the European Union's  Horizon 2020 research and innovation program  (grant agreement no  832460 ),  ERC   Advanced Grant  project " New Paradigm in Electrolyte Thermodynamics ".
Characterizations The morphology of the catalysts was observed by a FEI Apreo S LoVac scanning electron microscope (SEM) with an accelerating voltage of 10. 0 kV. The X-ray diffraction (XRD) patterns were analyzed in the range of 10°to 90°at a scan rate of 20°min -1 using a Rigaku Smartlab 9KW diffraction system with a Cu Kα source (λ = 1. 54056 Å). The X-ray photoelectron spectra (XPS) measurements were performed on a Thermo Fisher ESCALAB-250Xi spectrometer using a monochromatic Al Kα x-ray beam (1486. 60 eV). All the peaks were calibrated by the binding energy of 284. 8 eV of the C 1 s spectrum. The X-ray absorption spectroscopy (XAS) of the Fe K-edge was undertaken under an ultrahigh vacuum at the 1W1B beamline of the Beijing Synchrotron Radiation Facility (BSRF). The XAS spectra were analyzed with the ATHENA software package  48  The NMR spectra were recorded on JEOL JNM-ECZ400S/L1 instrument at 400 MHz ( 1 H NMR) and 101 MHz ( 13 C NMR) with DMSO-d 6 , CDCl 3 , or CD 3 CN as the solvents.
Electrochemical in situ XAS measurements The in situ electrochemical XAS at the Fe K-edge was recorded at a 1W1B beamline of the BSRF. The electrolytic cell was homemade by Teflon with a Pt plate as the counter-electrode and a Hg/HgO electrode as the reference electrode. The α-Fe 2 O 3 /CP NRs were pre-reduced at -1. 9 V for 35 minutes, and then the potential was changed to -1. 2 V for the XAS measurements for reducing the influence of H 2 bubbles for the XAS tests. The XAS spectra were analyzed with the Athena software package.
Acknowledgements The authors are grateful to the  National Natural Science Foundation of China  (Nos. 21871206  ( B. Z. ) and  22001192  (C. L. )). We also acknowledge  Dr. Yanmei Shi  for her kind help with electrochemical in situ Raman and XRD measurements. We appreciate  Dr. Shibo Xi  at the  XAFCA beamline of the Singapore Synchrotron Light Source  and  Dr. Lirong Zheng  at the 1W1B beamline of the  Beijing Synchrotron Radiation Facility  for the XAS discussion and support. Article https://doi. org/10. 1038/s41467 -022-33779-8 Nature  Communications | (2022) 13: 5951.
Bending experiments The experiments were performed with a home-made movable stage. The PET-supported sample strip was mounted between two electrodes (one movable) in a distance of 25 mm and bent in positive (upwards) direction (Figure  S20 ). To provide a better electrical contact both ends of the carbon strip were connected with silver paint. The curvature of the bending was determined by translating the shapes from photographs into x-y data using the freeware Engauge Digitizer. The curvature is defined as the inverse radius at the maximum point of bending.
Transmission electron microscopy For bright-field transmission electron microscopy (bright-field TEM, BFTEM), selected-area electron diffraction (SAED, selected area diameter 150 nm), and high-resolution TEM Experimental 4D-STEM datasets were acquired using the TEAM I instrument (NCEM/LBNL), a double aberration corrected Thermo Fisher Titan fitted with a Gatan Continuum energy filter and K3 direct electron detector (operated in electron counting mode, 4x binning). 55 The energyfiltered diffraction patterns (15 eV slit width, central beam covered by 2. 5 mrad beam stop) were acquired at an accelerating voltage of 300 kV in microprobe mode with a beam convergence semi-angle of 0. 7 mrad (beam diameter approx. 2 nm, beam current 70 pA) and a covered spectrometer semi-angle of maximum 21 mrad.
A shadow image of the sample (i. e. , a strongly defocused image of the STEM probe) was recorded to align the rotation between scan field and recorded diffraction patterns. The datasets were evaluated employing the py4DSTEM software package. 56 Details are provided in the supporting information. Fourier-transform infrared spectroscopy FT-IR spectroscopy was performed using a Nicolet iS 5 FT-IR-spectrometer in conjunction with an iD7 ATR unit from ThermoFisher Scientific. The LP-NC powder was scratched off the PET substrate and directly analyzed.
EELS mappings were acquired from representative areas of the microtomic sensor crosssections, which exhibit excellent and homogeneous relative thicknesses of t/λ ≈ 0. 35 -0. 5 depending on the local sensor density (around 0. 25 for the adjacent embedding epoxy due to the lower density). Dual EELS mappings (step size ~20 nm) with core loss spectra in the energy-loss range of 200 -800 eV (dispersion 0. 3 eV/channel, ratio convergence/acceptance angle around 2) were recorded to cover the essential ionization of C, N, and O at around 285 eV, 400 eV, and 532 eV, respectively. Energy calibration was employed utilizing the respective low-loss datasets. EELS element distributions for C, N, and O were derived by quantification with theoretical cross-sections as implemented in the Gatan software. For background correction, a power law function for C, O but a 1 st -order log-polynomial function for N were employed (pre-edge fit range for all elements: 50 eV, post-edge fit ranges: 50 eV (C) and 25 eV (N, O)).