dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterisation Techniques Transition temperatures and enthalpies were recorded with a Q1000 apparatus from TA Instruments, Paris, France. The instrument was calibrated with indium and otherwise stated three heating/cooling cycles were performed on each sample, with a heating and cooling rate of 10 • C/min. SAXS/WAXS patterns were obtained with a transmission Guinier-like geometry. A linear focalized monochromatic Cu Kα1 beam (λ = 1. 5405 Å) was obtained using a sealed-tube generator (600 W) equipped with a bent quartz monochroma-tor. In all cases, samples were filled in home-made sealed cells of 1 mm path. The patterns were recorded with a curved Inel CPS120 counter gas-filled detector, Paris, France, linked to a data acquisition computer (periodicities up to 90 Å) and on image plates scanned by Amersham Typhoon IP, Cytiva, France with 25 µm resolution (periodicities up to 120 Å).
SAXS/WAXS patterns were obtained with a transmission Guinier-like geometry. A linear focalized monochromatic Cu Kα1 beam (λ = 1. 5405 Å) was obtained using a sealed-tube generator (600 W) equipped with a bent quartz monochromator. In all cases, samples were filled in home-made sealed cells of 1 mm path. The patterns were recorded with a curved Inel CPS120 counter gas-filled detector, Paris, France, linked to a data acquisition computer (periodicities up to 90 Å) and on image plates scanned by Amersham Typhoon IP, Cytiva, France with 25 μm resolution (periodicities up to 120 Å).
Acknowledgments: We acknowledge the  Romanian Academy , Program 4. S. N. and F. M acknowledge  Politehnica University of Timisoara. B. D. and M. R. thank  CNRS  and  University of Strasbourg  for support and  B. Heinrich  for recording the SAXS/SWAXS patterns of the paste electrodes at room temperature. This work was supported by a grant of the  Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI , project number  PN-III-P4-ID-PCE-2020-1958 , within  PNCDI III.
Funding: This research was funded by  "Ministère de l'Enseignement Supérieur et de la Recherche Scientifique Tunisien -Université de Carthage " (fellowships to J. BHS. ), the  Ministery of Higher Education and Research in France , the " Region Basse Normandie ", the  LABEX SynOrg  ( ANR11-LABX-0029 ), the  European FEDER.
Acknowledgments. We thank the  TEM platform from ICS  for technical support. Support from  ANRT  through  Cifre PhD  grant  2018/0723  is acknowledged (MB). Guillaume Rogez  ( IPCMS, Strasbourg ) is acknowledged for giving access to the  FTIR spectroscopy laboratory. Mélanie Legros  is acknowledged for performing the GPC analysis of the PLLA materials. We thank also.
Acknowledgements This work has benefited from support provided by the  University of Strasbourg Institute of Advanced Study (USIAS) , within the  French national programme  " Investment for the future" (IdEx-Unistra ). We also thank the  Université de Strasbourg , the C. N. R. S. , and the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation  for financial support.
It has been proposed that hub formation is a two step process, with the formation of individual filaments first and then the collision of filament pairs in a second step  [8]. One key aspect of such scenario is the existence of two distinct formation path for individual and hub filaments. In the study presented here, we use the very sensitive 1. 15mm continuum data of the GASTON large programme on the IRAM 30m to determine the time evolution of compact millimetre sources as a function of the local filament network convergence.
Acknowledgements NP and AJR would like to thank the  STFC  for financial support under the consolidated grant numbers  ST/N000706/1  and  ST/S00033X/1 , and the  Royal Society  for providing computing resources under Research Grant number  RG150741. We would like to thank the  IRAM  staff for their support during the campaigns. The NIKA2 dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean Paul Leggeri ,  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble  and the  LabEx FOCUS   ANR-11-LABX-0013. This work is supported by the  French National Research Agency  under the contracts "MKIDS", " NIKA " and  ANR-15-CE31-0017  and in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
■ ACKNOWLEDGMENTS J. A. M. and C. E. A. K. acknowledge the  Flemish Government  for long-term Methusalem structural funding. K. A. , N. P. , E. B. , and C. E. A. K. acknowledge joined funding by the  Flemish Science Foundation (FWO)  ( G083318N ). This work has received funding from the  European Research Council (ERC)  under grant agreement no. 834134  (WATUSO) and from the  Austrian Science Fund (FWF)  (project ZeoDirect  I3680-N34 ). E. B. acknowledges  FWO  for a "Krediet aan navorsers"  1. 5. 061. 18N. NMRCoRe is supported by the  Hercules Foundation  ( AKUL/13/21 ), by the  Flemish Government  as an international research infrastructure (I001321N), and by department  EWI via the Hermes Fund  ( AH. 2016. 134 ). C. E. A. K. and E. B. coordinated the project. All authors contributed to discussions of the results, development of the nucleation model, and aided in writing the paper during all stages.
7c  because it returns a negative number as the second order polynomial trendline in Fig. 7b  is just starting to trend upwards at the rightmost S/C ratio data point. Nonetheless, the data clearly shows that marginal CO 2 avoidance costs beyond 95 % capture become excessive when the S/C ratio is used to increase CO 2 avoidance in a situation with 80 % PSA off-gas recycling. On the other hand, Fig. 7c  shows a constant marginal CO 2 avoidance cost around 150 €/ton for increasing the CO 2 capture ratio via PSA offgas recycling at a constant S/C ratio. In other words, a CO 2 price above 150 €/ton would flip the optimal configuration from one with no recycle and less than 80 % CO 2 capture to one with the maximum practical recycle ratio capturing close to 100 % of the produced CO 2. It is noted that impurities (mainly N 2 ) in the natural gas will constrain this strategy below 100 % CO 2 capture.
Fan-like textures were also observed for O 2 -Fla-S 3 at 44 °C (Figure  3e ) and O 3 -Fla-S 3 at 146 °C (Figure  3f ). According to the POM investigations, the fully thioether-substituted flavylium salts also belonged to category (b), showing pseudo-focal conic textures, e. g. for S 2 -Fla-S 2 at 72 °C (Figure  3h ) and S 3 -Fla-S 2 at 22 °C (Figure  3j ) with or without line defects and homeotropic regions upon cooling. The violet appearance of the textures is a result of the strong intrinsic color of the flavylium salts. Fan-shaped textures are common for smectic and columnar liquid crystals and further investigation via XRD was necessary to understand the mesomorphic behavior. To fully assign the mesophase geometries, XRD studies were performed. All XRD data are summarized in Table  1. First, members of category (a) were examined.
Acknowledgements Generous financial support by the  DFG  ( LA 907/17-2 ,  LA 907/20-1  and shared instrumentation grant  INST 41/897-1  FUGG for 700 MHz NMR), the  Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg , the  Bundesministerium für Bildung und Forschung  (shared instrumentation grant) and the  Carl-Schneider Stiftung Aalen (instrumentation grant ) is gratefully acknowledged. Open Access funding enabled and organized by Projekt DEAL.
In both epoxy networks (ILM1 or ILM2-based ones) containing POSS ® Ph -triol, the formation of an interface is observed around some POSS ® Ph aggregates as shown in Figure  1a, d. This interface has an average thickness included between 10 and 30 nm measured directly from the TEM images by using Image J Software (ImageJ bundled with 64-bit Java 1. 8. 0_172, 2020). This interface represented about 2% of the total volume of the POSS aggregate. This interface can be also explained by the low amount of POSS ® Ph -triol nanoclusters which reacted with the epoxy-amine matrix. The interconnected nanophase-separated morphology with uniform domain size is characteristic of self-assembled structures generated as result of a balance of different intermolecular and intercluster interactions  [52, 53].
This interface has an average thickness included between 10 and 30 nm measured directly from the TEM images by using Image J Software (ImageJ bundled with 64-bit Java 1. 8. 0_172, 2020). This interface represented about 2% of the total volume of the POSS aggregate. This interface can be also explained by the low amount of POSS ®Ph -triol nanoclusters which reacted with the epoxy-amine matrix. The interconnected nanophase-separated morphology with uniform domain size is characteristic of self-assembled structures generated as result of a balance of different intermolecular and intercluster interactions  [52, 53]. It should be pointed out that the dispersion of POSS ®Ph -triol clusters in the ILM1 or ILM2-based matrix is better than the one obtained with DGEBA/IPD network, where the diameter distribution is ranging from 0.
The number of the mixed pixels N Mixed Pixels at any instant t was counted when the value of red channel of a single pixel became larger than the local threshold β. The degree of mixing M% (N MixedPixels /N TotalPixels ) was plotted against time t to obtain the mixing curve. The mixing time t m was determined when the degree of mixing was 95%. For all the experiments, only the images when the reactor came back to the undeformed positions were analyzed (see Fig. 3a ). It should be noted that each experiment was repeated three times and average values were calculated for analysis. Although the mixing curves based on data taken from different viewing angles were slightly different during the initial stage of mixing, the mixing curves for the final stage of mixing overlap. Viewing angles did not affect mixing time as well (see Fig.
Acknowledgments The authors are grateful for the  National Key Research and Development Program of China  ( International S&T Cooperation Program ,  ISTCP ,  2016YFE0101200 ), the financial support from the  National Natural Science Foundation of China  ( 21676172 ,  21978184 ), the  Natural Science Foundation of Jiangsu Province  (Grants No. BK20170062 ), and the" Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions ". The author XDC came up the idea of SER based on his in vitro digestion studies. He thanks  Soochow University  for providing the start-up grant for carrying out projects in bio-inspired chemical engineering. Jie Xiao  also acknowledges the "Jiangsu.
Acknowledgements The authors thank  Ruslan R. A. Diduk  and  Arben Merkoçi  from  Institut Català de Nanociència i Nanotecnologia (ICN )  for fruitful discussions regarding paper fluidics, the  VTT Technical Research Centre of Finland Ltd  for furnishing paper membranes, and  Marco Giannetto  ( Università di Parma ) for fruitful discussions on the use of ISMs. The authors also acknowledge financial support from the  EC  through the  ERC  project  SUPRA DMAT  (GA-), the  Marie Sklodowska-Curie  project  ITN  project  BORGES  ( GA-) , and the  Graphene Flagship Core project  ( GA- ) as well as the  Labex  project  CSC  ( ANR-LABX-CSC ) within the  Investissement d'Avenir program  ANR--IDEX-the  International Center for Frontier Research in Chemistry  and the  Institut Universitaire de France (IUF).
Emissions cycle, methodology and data collection The new WLTP procedure is based on the new WLTC (Worldwide Harmonized Light-duty Vehicles Test Cycles) driving cycles, which are divided into 3 classes based on the weight-to-power ratio. The most important class in the WLTC cycle is class 3b because it includes cars. WLTP is divided into 4 different subparts, each with a different maximum speed [4] (see Fig. 1 ). The evolution of the regulations concerning the ambient temperature and altitude ranges for different test procedures became more severe as they are presented in Fig. 2. This impose to engine manufacturers more stringent limitations to be consider starting from the engine research and development stage. Over these 4 engine operating conditions, experimental tests were performed on an engine bench with the automatic acquisition of all the parameters necessary to perform the theoretical model calibration.
Model calibration, test and validation This section presents the results of the model calibration created using the AVL Boost program. The calibration over the 4 selected operating conditions was performed according to the experimental data recorded on the engine test bench for gasoline engine operation. In Fig. 6  a comparison between in-cylinder pressure traces of the experimental data and simulation results was made for both engine speeds (1500 rpm and 2000 rpm) for each considered loads (BMEP) of each engine speed when gasoline fuelling. It can be observed that the calibration of the model based on the experimentally obtained data is accurate and the accuracy is within the limit of 5% for the BMEP, the peak fire pressure and the angle of the peak fire pressure for the selected operating conditions (Table  5 ).
Figs. 7, 8, 9 and 10 show the calibration results together with the relative deviations of engine effective power and regulated emissions of HC, CO, and NOx. The power calibration was performed with relative differences up to 2% for both 1500 rpm and 2000 rpm engine speeds. The calibration for regulated emissions of HC, CO and NOx was made with a good accuracy for all 4 operating conditions with the relative deviations up to 1%. The good agreement existing between experimental data and simulation results ensure that the model could be consider reliable and predictive tool so it can be used for the following simulations.
Acknowledgements This work was financially supported by  TotalEnergies  and  CEA  (contract of collaboration no. FR00009899 ). This work was also selected and sponsored by the Program Magellan of the CEA. Part of the experiments were performed at  Grand Accelerateur National d'Ions Lourds (GANIL) Caen, France , and supported by the  French Network EMIR&A. The authors want to warmly thank the EMIR&A committee to have accepted their irradiation proposal and allow GANIL experiments. Notably, the strong implication of  Dr. Y. Ngono-Ravache  during swift heavy ion irradiations is herein acknowledged. The authors wants also to warmly thank  Sylvain Losa  from  Thermo Electron SAS  for his strong involvement in setting up the  Ion Chromatography experiment at LSI. Pierre Bulit  from  IDO  is also acknowledged for his contribution in designing membrane-electrode waterproof clip.
Experimental The single-chain dynamics of a polymer chain can be measured with neutron spin echo by selectively Hlabelling a small part of the long chains immerse in a matrix of deuterated polymers. Nearly monodisperse long and short linear polyisoprene were synthesized by anionic polymerization. The molecular weight of the polymers were : 𝑀 # = 83. 5 kDa and PDI=1. 01 (long hPI); 𝑀 # = 90. 2 kDa and PDI=1. 03 (long dPI); 𝑀 # = 12. 4 kDa and PDI=1. 04 (short dPI). The entanglement mass of PI is 𝑀 % ≈5 kg/mol. Long-linear PI had 𝑍 = 𝑀/𝑀 % ≈ 16 entanglements per chain while the shortlinear PI had 𝑍 =2. 4. The samples for neutron scattering experiments contained 10% of long protonated PI chains in the otherwise deuterated material. The NSE data were obtained in IN15 (ILL, Grenoble, France)  [20].
Regarding the software employed, Julia programming language,  Bezanson et al. (2017)  has been used, in particular the package FerriteFem. jl, see  Carlsson and Ekre (2021) , for the FEM simulations and Amores (  2022 ) for the P-Splines functionality. A typical assumption made on the phenomenological approach for isotropic incompressible solids is the Valanis Landel decomposition, W (λ d 1 , λ d 2 , λ d 3 ) = ω (λ d 1 ) + ω (λ d 2 ) + ω (λ d 3 ). With that assumption, the expressions for the principal PK2 stresses can be obtained in terms of an unknown function ω ′ (x): S j = J -2/3 1 λ d j (ω ′ (λ d j ) - λ d 3 d j ω ′ (λ d 3 )) for j = 1, 2 (20) Once ω ′ (x) is obtained, the principal stresses are determined for any deformation state under the plane stress hypothesis or the contribution to the principal stresses coming from the deviatoric energy for any general case (not in plane stress), see (Eq.
Numerical resolution An algebra-differential system (1) could be express in a matrix form (4), as follows: ⎡ ⎢ ⎢ ⎢ ⎢ ⎣ ℎ -𝐻 ℎ 𝑇 ℎ 𝑥 0 0 0 0 𝑥 -𝑦 0 𝑈 𝑙 0 0 0 0 0 ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ U𝑙 Ṫ ẋ ẏ ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ = ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ 𝑃 𝑤 ∑ (𝑦 -𝑥) 0 𝑦 -𝐾(𝑥, 𝑇 , 𝑃 ) ⋅ 𝑥 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ (4) and simpler as: 𝑀(𝑍) ⋅ 𝑍 = 𝐹 (𝑍, 𝑡) (5) where () is the mass matrix (with the differential terms);  (, ) is the residual vector; and Z is the vector containing the variables of the problem (  ,  , , ). This problem is solved using a Gear method  [27] , consisting in prediction/correction method. The code is programmed with Matlab ® coupled with the toolbox Simulis ® Thermodynamics and the precision is set at 10 -8.
The emissions of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) from indoor building and vehicle cabin materials can adversely affect human health. Many mechanistic models to predict the VOC/SVOC emission characteristics have been proposed. Nowadays, the main obstacle to accurate model prediction is the availability and reliability of the physical parameters used in the model, such as the initial emittable concentration, the diffusion coefficient, the partition coefficient, and the gas-phase SVOC concentration adjacent to the material surface. The purpose of this work is to review the existing methods for measuring the key parameters of VOCs/SVOCs from materials in both indoor and vehicular environments. The pros and cons of these methods are analyzed, and the available datasets found in the literature are summarized.
Sensitivity analysis based on physical models for ventilated conditions indicates that  (Yang et al, 2001; Wang et al. , 2021a) : (1) the gas-phase concentration associates approximately linearly with C 0 for VOCs or y 0 for SVOCs; (2) D m significantly affects both the peak concentration and the decay rate (for VOCs); (3) the influence of K is twofold (for VOCs): on the one hand, increasing K decreases the emission rate during the early stages, and results in a slow depletion rate; on the other hand, the concentration under ventilated condition after some period is almost identical for different K. This implies that the key parameters have a very significant impact on VOC/SVOC emission behaviors. The lack of adequate parameter data (or database) forms a bottleneck for emission prediction, given that the physical model is well developed.
The VOC concentration in the chamber was monitored until it reached a steady state. The K could be calculated using the data at the steady state as: K = M -C ss V V m C ss (4) where, M is the mass of VOCs injected into the chamber, mg; C ss is the steady state VOC concentration in the chamber, mg/m 3 ; V and V m are the chamber volume and material volume, respectively, m 3. With this method, Tiffonnet et al. measured the K of acetone in acrylic paint.
Then, according to the least square method, q 1 can be obtained by fitting the experimental data with a physical model  (Xiong et al. , 2011b). The three key parameters can then be obtained. Wang et al. used  this method to obtain the three key parameters of 7 VOCs (ethylbenzene, butyl acetate, styrene, m-/p-xylene, glycol diacetate, benzaldehyde, and formaldehyde) emitted from panel furniture. The main advantage of this method is that only one parameter needs to be determined during the optimization process, thus it is possible to get a unique solution. Wang et al. (2021b)  and  Xu et al. (2022)  further examined the parameter measurement for furniture, and found that the single-layer emission model was not well adapted to the early experimental data for some slow emission scenarios for furniture.
In addition, a rack for placing test materials was designed, with corresponding holes in the test chamber. The material could be taken by pulling the rack out, avoiding mass loss and disruption of SVOC transmission caused by opening the test chamber. Using this method,  Liu et al. (2014 Liu et al. ( , 2016) )   may be just a rough estimate, since the external convection effect is ignored in the DSS model, and the possibility of obtaining a non-unique solution resulting from estimating two parameters from a single dataset. Liu et al. (2015a)  developed a C-depth method to determine the D m and K of PCBs in building materials (sink materials, as secondary sources). The primary source is the sealant, and K is the partition coefficient between the primary source and the secondary source, not the ratio of the PCB surface concentration to air-phase concentration.
An analytical solution for the SVOC concentration adsorbed by the sink material was derived, and the time-resolved SVOC concentrations in the sink material were measured by Soxhlet extraction. y 0 and K can be obtained by nonlinearly fitting the concentration data with the analytical solution. This method only measures the SVOC concentration in the sink material at an early stage and does not need to reach equilibrium, thus is very efficient and easy to implement. The authors determined the y 0 and K of DEHP, DiBP and DnBP at 25 ℃ and 32 ℃. The test time for DnBP was 24 h. Due to insufficient data in the short period of time, DEHP could not be analyzed. This also shows that it is necessary to obtain enough data to meet the accuracy requirements for curve fitting. Yang et al. (2020)  developed a new method to simultaneously measure the two key parameters (K and D m ) of several phthalates in sink materials, and y 0 in source materials, in a single experiment.
Discussion Table  1  summarizes the testing time, features/strengths, limitations and application conditions of measurement methods for VOC/SVOC key parameters. Tables  S1  and S2  in the Supplementary material provide a preliminary database of the available data of the measured key parameters of VOCs/SVOCs emitted from indoor building and vehicle cabin materials under various test conditions. The time scale and spatial scale for different methods are different. In this section we discuss and analyze the available methods from these two aspects.
The key parameters of VOCs/ SVOCs that we focused on in this review are the physical parameters, which don't change with the loading ratio or air change rate. Moreover, previous studies have established some correlations between these key parameters and temperature/humidity. These achievements provide the basis for IVAQ pre-assessment during the design stages of buildings or vehicles. IVAQ pre-assessment can be performed according to the following steps: (1) select the material groups for the buildings or vehicles; (2) determine the VOC/SVOC key parameters of the building or vehicle cabin materials at different temperatures and humidity (or select these key parameters from an available database); (3) substitute the key parameters into an appropriate physical model to predict VOC/SVOC concentrations or evaluate the human health risk (e.
Small-angle scattering Small-Angle Neutron Scattering (SANS) was performed at the EQ-SANS of the Oak Ridge National Laboratory (ORNL) and D11 of the Institut Laue-Langevin (ILL, doi:10. 5291/ILL-DATA. CRG-2632) in Grenoble. All measurements were performed at room temperature covering a general scattering vector range between 0. 002 and 0. 3 Å -1. All SANS data were corrected for container scattering and calibrated to absolute scales with beamline specific calibrants. All data were systematically corrected for incoherent background in order to exploit the coherent signal only. Additional Small Angle X-ray Scattering (SAXS) measurements were obtained from the Bruker Nanostar U at FZ Jülich covering a comparable range of scattering vectors from 0. 005 to 0. 3 Å -1. The absolute calibration was only approximate due to the undefined thickness of the samples and the high absorbance of silica.