dataesr/scientific-paragraphs-categorization huggingface dataset:
| Guidelines summary and data points The guidelines presented in this article lead to a database containing 1800 molecules that have accurate correlations for P sat and ρ sat liq. Among them, 1536 fluids possess an accurate correlation for Δ vap H and 890 for c sat P,liq , resulting in a database containing 301,300 high-quality pseudo-experimental points. Moreover, critical properties (T c , P c , and v c ) are available for the 1800 molecules, which results in 5400 additional data points. In total, the proposed database contains 306,700 pure-component data points for for the assessment of thermodynamic models' performances. The name, the AC, and the critical properties (T c , P c , and v c ) of each molecule along with the temperature range and the identification (ID) number assigned by the DIPPR to each property correlation are reported in Appendix A3.
| BENCHMARK OF THE SRK, PR, tc-RK, AND tc-PR EoSs In this section, the performances of four CEoS are evaluated by assessing their accuracy over the 306,700 pseudo-experimental data points available in the reference database for pure compounds developed in this article. Type III (deviation calculated with the gen-tc-PR 2020 EoS by Equation  2 ) TH test 2 X ¼ MAPE X þ SD X a - a MAPE X and SD X are obtained from Table  1.
in 1991  17  : α T r ð Þ¼T N MÀ1 ð Þ r exp L 1 À T MN r À Á Â Ã ð9Þ For the 1800 pure components available in the proposed database, the three L, M, and N parameters were determined for both the tc-RK and tc-PR EoSs following the fitting procedure that Piña-Martinez et al. 10  advised to implement. In short, the L, M, and N parameters were determined in order to reproduce as well as possible P sat (at least), and/or Δ vap H, and/or c sat P,liq (if available) experimental data and to guarantee that the α-function is consistent. The resulting parameters are reported in Appendix A4.
It is possible to observe that the four EoSs have a ratio of well-modeled molecules greater than 65% and often close to 80% for alkanes, alkenes, alkynes, aromatics, esters, ethers, ketones, aldehydes, epoxides, peroxides, halogen compounds, amines, amides, sulfur and silicon compounds. Alcohols have 61% of well-modeled molecules except with the tc-PR model, which yields a ratio of 53%. Nitriles are modeled the worst, and this is related mostly to high deviations on v sat liq. Such large deviations on v sat liq can, however, not be ascribed to the strong AC of these compounds. A detailed analysis of the chemical structure of the 31 nitrile compounds available in the DIPPR database reveals that most of these molecules are F I G U R E 1 Ratio of well-modeled and badly modeled molecules by the SRK EoS classified into non-self-associating (NSA) and self-associating (SA) fluids.
• There is a spectacular improvement in the reproduction of liquid molar volumes when a substance-dependent volume-translation parameter is used. • The accuracy of the tc-PR EoS proved to be impressive. Such a modern CEoS achieved an average overall deviation of less than 2% over the 306,700 available experimental data points. Excellent results are obtained for all the properties, except the molar critical volumes. • The striking fact is that not all the NSA molecules are well modeled by the studied CEoSs and that almost two-thirds of the SA compounds of the database are represented with high accuracy by CEoSs. At this step, one might wonder whether the addition of an association term is the right solution to improve the accuracy of EoSs. The answer to this extremely complex question is not yet clear to us but we plan to publish our conclusion soon, in a next paper.
At this step, it could be argued that EoSs are mostly developed and used in academia and industry to calculate phase equilibria and thermodynamic properties of fluid mixtures. This is obviously true, but we believe that this study over thousands of pure components deserves attention because, as stated by Agarwal and recalled in this article, "regardless of the sophistication of your thermodynamic model and the number of parameters in the mixing rule, you are in trouble if the vapor pressures are inaccurate. No matter how many parameters your mixture model may have, the end points of isothermal (P,x,y) phase diagrams will always be calculated incorrectly. " Finally, we hope that the proposed guidelines and database will be widely adopted by the scientific community to identify the strengths and weaknesses of a given thermodynamic model.
How many molecules should (and could) be included in the reference database of pure components? 4. How to define the temperature ranges in which deviations should be calculated? Existing databases of pure fluids Concerning experimental data on pure fluids, three databases exist as the most used by researchers working in the field of chemical engineering thermodynamics. The first is the DIPPR database, which contains recommended values and accuracy estimates for 34 constant properties and 15 temperature-dependent properties spanning 2230 compounds. Another database is the Dortmund Data Bank (http:// www. ddbst. com/ddb. html), which stores the largest amount of data worldwide for the thermophysical properties of pure components and their mixtures. The third one is the ThermoData Engine database of the National Institute of Standards and Technology, which provides experimental data for nearly 24,000 pure fluids and includes several methods for evaluation of the uncertainty and thermodynamic consistency of such data.
Since trustworthy data are contained in the aforementioned databases, any of them may be extremely useful for the definition of model comparison guidelines. Nevertheless, it is worth noting that an important feature of the DIPPR is that, apart from the experimental data points of the 15 temperature-dependent (T-dependent) properties, it proposes a fitting equation used for correlating T-dependent property data, reports the corresponding coefficients, and provides an evaluation of the mean error between predictions from the fitting equation and raw data. This enables the generation of pseudoexperimental data that are evenly distributed over wide ranges of temperature, which is desirable for the calculation of property deviations. Considering these features, the 12. 3. 0 version (May 2020) of the DIPPR database is used in this article as the starting point to build a reference database aimed at assessing the accuracy of EoSs for pure fluids.
Ratio of well-modeled and badly modeled molecules by the PR EoS classified into non-self-associating (NSA) and self-associating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules F I G U R E 3 Ratio of well-modeled and badly modeled molecules by the tc-RK EoS classified into non-self-associating (NSA) and selfassociating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules F I G U R E 4 Ratio of well-modeled and badly modeled molecules by the tc-PR EoS classified into non-self-associating (NSA) and selfassociating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules 4 | CONCLUSION In this article, we presented guidelines for the constitution of a database of pure-compound properties stemming from the DIPPR for the assessment of the performance of thermodynamic model for such compounds.
These guidelines provide the number of molecules, the property data to be incorporated, and the temperature ranges between which pseudo-experimental points should be generated. Application of such guidelines led to a database containing 1800 molecules for which, in addition to the critical coordinates (T c , P c , and v c ), accurate correlations for P sat and ρ sat liq are available. Among them, 1536 fluids also possess an accurate correlation for Δ vap H and 890 for c sat P,liq , resulting in a database containing 306,700 high-quality pseudoexperimental data In addition, the accuracy of the SRK, PR, tc-RK, and tc-PR EoSs has been investigated by calculating the deviations between experimental data points embedded in the developed reference database and calculated values returned by the CEoSs.
allows us conclude that the tc-PR EoS with an average overall deviation of less than 2% over the 306,700 available T A B L E 3 Overview of the data available in the proposed database Total number of pure components: 1800 (1252 non-self-associating + 548 self-associating). Total number of pseudo experimental data points: 3,06,700 Number of components for which the property is available Expressions of the SRK, PR, tc-RK, and tc-PR EoS.
3. 2. 1 | 321 How many molecules should (and could) be included in the reference database of pure components? 4. How to define the temperature ranges in which deviations should be calculated? Existing databases of pure fluids Concerning experimental data on pure fluids, three databases exist as the most used by researchers working in the field of chemical engineering thermodynamics. The first is the DIPPR database, which contains recommended values and accuracy estimates for 34 constant properties and 15 temperature-dependent properties spanning 2230 compounds. Another database is the Dortmund Data Bank (http:// www. ddbst. com/ddb. html), which stores the largest amount of data worldwide for the thermophysical properties of pure components and their mixtures. The third one is the ThermoData Engine database of the National Institute of Standards and Technology, which provides experimental data for nearly 24,000 pure fluids and includes several methods for evaluation of the uncertainty and thermodynamic consistency of such data.
F I G U R E 2 • 2 Ratio of well-modeled and badly modeled molecules by the PR EoS classified into non-self-associating (NSA) and self-associating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules F I G U R E 3 Ratio of well-modeled and badly modeled molecules by the tc-RK EoS classified into non-self-associating (NSA) and selfassociating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules F I G U R E 4 Ratio of well-modeled and badly modeled molecules by the tc-PR EoS classified into non-self-associating (NSA) and selfassociating (SA) fluids. Green: well-modeled molecules. Red: badly modeled molecules 4 | CONCLUSION In this article, we presented guidelines for the constitution of a database of pure-compound properties stemming from the DIPPR for the assessment of the performance of thermodynamic model for such compounds.
T A B L E 2 Acceptance thresholds for the different property correlations and types of error Acceptance thresholds for each property correlation Error type P sat v sat liq Δ vap H c sat P,liq c P,IG Type I (error reported in the DIPPR database) 5% 5% 5% 5% 5% Type II (average exp. error calculated by Equation 1) 5% 5% 5% 5% 5%.
5 MAPE on P sat , v sat liq , Δ vap H, c sat P,liq , v c , T c , and P c as predicted by the SRK, PR, tc-RK, and tc-PR EoS Such MAPEs are exactly zero since the four EoSs are parameterized in order to exactly reproduce T c,exp and P c,exp. T A B L E 6 MAPE on P sat , v sat liq , Δ vap H, c sat P,liq , and v c depending on the associating character as predicted by the SRK, PR, tc-RK, and tc-PR EoS were initially intended to describe NSA components only. By only considering NSA compounds instead of all pure components of the database, a more severe test is proposed to include a molecule to the "well-modeled" category. Quantitatively speaking, a pure component j will be considered as well modeled by a given EoS provided 8 > > > > > < MAPE EoS P sat j ≤ MAPE EoS,NSA P sat þ SD EoS,NSA P sat > > > > > : and MAPE EoS v sat liq j ≤ MAPE EoS,NSA v sat liq þ SD EoS,NSA liq v sat ð10Þ where SD EoS,NSA X and MAPE EoS,NSA X denote the mean SD and MAPE for property X calculated with a given EoS over the 1252 NSA components of the database.
If one or both conditions of Equation (10) are not satisfied, the pure component is considered as "badly modeled. " Results are presented in Figures 1-4 and deserve discussion. A key point is that, regardless of the considered CEoS, about 75% of pure components are well represented, according to the criterion defined in Equation (10) (see the left panel in each figure). In addition, the database contains 70% (1252/1800) of NSA fluids and 30% of SA components. At first sight, it could be believed that the 75% well- modeled molecules match the NSA fluids plus some SA components. This is, however, not the case at all. Another striking fact is that almost 67% of the SA molecules are well modeled (see the right panel in each figure), while almost 20% of the NSA compounds are badly modeled (refer to the middle panel in each figure).
Positions of all atoms are stored at every time step for analysis. As we aim to study the reactivity and stability of the different solvated molecules in the molten carbonate, we opted for first-principles molecular dynamics where energies and forces are calculated at each time step at a DFT level. We used the software CP2K that employs a hybrid Gaussian plane-wave method (GPW). [25] [26] [27]  To reduce the number of explicit electrons, core electrons were replaced by Goedecker-Teter-Hutter (GTH) norm-conserving pseudopotentials. [28] [29] [30]  The remaining valence electrons were projected into a doublezeta valence plus polarisation (DZVP) basis set optimized for molecules for each atom type (C, O, Li and K). [31]  A cutoff of 700 Ry was used for the electronic density. We chose the BLYP functional to determine the exchange-correlation interactions  [32, 33]  and the DFT-D3 scheme with a cut-off of 40 Å for the dispersive interactions corrections.
The authors are grateful to  Michel Cassir  for discussion on the interesting properties of molten carbonate. We acknowledge funding by  PSL Research University  (project  COOCAR , grant  ANR-10-IDEX-0001-02 ) and from the  French Agence Nationale de la Recherche (ANR)  under grant  ANR-17-CE05-0025  (MCEC). This work was performed using HPC resources from  GENCI  (grants  A0030802309  and  A0050802309 ).
Acknowledgments The author thank  Dr. Aude Falcimaigne-Cordin  for advises concerning the ABTS assays,  Raphaël Bronchereux ,  Apolline Ledoux  and  Hugo Mathieu  for their help for the antioxidant and antimicrobial assays. The author would like to thank  Dr. Fady Mohareb  for constructive criticism of the manuscript. The authors acknowledge funding from  ESCOM , the  European Regional Development Fund ERDF  and the  Region Hauts-de-France  ( CPER 2007-2020  and  CapsAntiOx  project).
Engine Testbed The numerical study is performed with a turbocharged 2. 0-litre GDI engine, which specifications and testbed are illustrated in Table  2  and Figure  2 , respectively. The required data for model validation was obtained from the engine testbed under CAC mode. The engine's speed and torque can be accurately controlled and measured by a programmable Electronic Control Unit (ECU) and an electrical dynamometer. In addition, spark-plug type pressure sensors (AVL-GH13Z), a charge amplifier (Kistler 5018A) and a combustion analyser (AVL 641) were utilised to measure, analyse and record the transient cylinder pressure signals. The cylinder pressure should be averaged by 200 consecutive cycles to reduce the deviation of cycle-to-cycle variations. According to Holman's root mean square method, the uncertainties of some items are listed in Table  3.
The spectra were processed using the ATHENA program from the Demeter software package. 48 The catalyst powder (sieved fraction size = 100 μm-200 μm) was loaded in a quartz capillary (100 mm length, outer diameter = 1. 5 mm, and wall thickness = 0. 02 mm) and fixed between two quartz wool plugs. Gases were dosed with the mass flow controllers (Bronkhorst) and analyzed with either the mass spectrometer (MS, Pfeiffer Vacuum, OmniStar GSD 320 T) or the online Fourier-transform infrared spectrometer (FTIR, ). The exhaust gas lines were heated to 150 ○ C. The 2 wt. % Pd/Al 2 O 3 three-way catalyst (TWC) was prepared by the incipient wetness impregnation method using palladium acetylacetonate (STREM Chemicals, 99%) as a precursor and commercial γ-Al 2 O 3 (SASOL Germany GmbH) as a support material.
ACKNOWLEDGMENTS This work was supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  through Grant No. GR 3987/9-1  [in kind cooperation with  Xiulian Pan  ( Dalian Institute of Chemical Physics, China )] and through project  CRC1441  (Project No. 426888090 ). Portions of this research were carried out at the light source PETRA III at  DESY , a member of the  Helmholtz Association (HGF). We would like to thank  Edmund Welter  and  Ruidy Nemausat  for assistance in using beamline P65. The authors thank  Nilsu Öceş  ( ITCP/KIT ) for preparing technical drawings,  Michael Zimmermann  ( IKFT/KIT ) for EDX measurements, and the  Institute for Beam Physics and Technology (IBPT/KIT)  for the operation of the storage ring in the  Karlsruhe Research Accelerator (KARA).
Acknowledgements This work was supported by a young researcher grant from the  Agence Nationale de la Recherche  (grant  n°18-CE05-0007 ). Mr. Stéphane Lorcy at Synchrotron SOLEIL  is acknowledged for assistance in recording SEM images. We express our gratitude to  Dr. Jean-Blaise Brubach  of the AILES beamline of SOLEIL for his helping during infrared spectra collection. We are indebted to  Ms. Chanjuan Zhang , and  Dr. Zakaria Halime  at the  Institut de Chimie Moléculaire et des Matériaux d'Orsay, Université Paris-Saclay  for their assistance in performing 1 H NMR measurements.
Acknowledgment The experimental work was supported by the  French research agency  ( Association Nationale de la Recherche et de la Technologie ANRT ),  Renault S. A. , and PPRIME Institute during the Ph. D. thesis of  M. Ben Houidi  (CIFRE N: 384/2010 ). The simulation work was supported by  King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)  with funds given to the  Clean Combustion Research Center. We acknowledge funding from the  KAUST Clean Fuels Consortium  and its member companies.
154184 nm). The continuous scanning was registered in the 2θ range of 10-80 • with a step size of 0. 02 • and a scanning speed of 2 s per step. Phase identification was conducted by comparison with the joint committee on powder diffraction standards database cards (JCPDS). According to the Scherrer equation, the crystallite size (d) was calculated based on the six most intense XRD reflections of Co 3 O 4 (111), (  220 ), (311), (400), (  511 ) and (440) crystal planes. The lattice constant (a) was computed from the following equation based on the (111), (  220 ), (311), (400), (  511 ) and (440) crystal planes of Co 3 O 4 : a = λ 2sinθ • ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ h 2 + k 2 + l 2.
√ Where λ (0. 154184 nm) is the X-ray wavelength, θ is the Bragg angle, h, k and l are Miller indices of Co 3 O 4. All the calculations were conducted using MDI Jade software. Raman spectra were obtained on a HORIBA Jobin Yvon Raman instrument with a 514 nm excitation wavelength and a CCD detector. The scanning range was set at 150-800 cm -1. Fourier Transform Infrared (FT-IR) analysis was carried out on a PerkinElmer FT-IR C92712spectrometer. The spectrum was recorded in the range of 400-4000 cm -1 with a resolution of 1 cm -1. N 2 -sorption isotherms were recorded at -196 • C on a Micromeritics TRISTAR II apparatus. Before the measurement, the samples were outgassed at 300 • C under vacuum for 3 h. The Brunauer-Emmet-Teller (BET) method was applied to obtain the specific surface area (SSA) of the catalysts.
The weight loss recorded during the TPD experiment was discussed in terms of desorbed oxygen species, according to the signal registered by online mass quadrupole (Thermostar TM, Balzers). The X-ray photoelectron spectroscopy (XPS) measurement of the catalysts was performed on a Thermo Fisher ESCALAB 250Xi electron spectrometer with the excitation source of Al Kα (hν = 1486. 6 eV) radiation. The charge of energy shift of the XPS spectra was corrected by setting the binding energy (B. E. ) of contaminative carbon (C 1s) at 284. 6 eV. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) data were recorded on a Nicolet Nexus 670 FTIR spectrometer with 64 scans at an effective resolution of 4 cm -1. The sample was pretreated at 300 • C in Ar for 1 h and then cooled to 220 • C. The background spectrum was collected and subtracted from the sample spectrum at 220 • C.
The activity measurement of toluene oxidation was carried out similarly to that of propane oxidation. The feed gases were replaced with 0. oxidation tests were performed at a WHSV of 40,000 mL g -1 h -1 on the same apparatus as that of propane oxidation. In this work, CO 2 yield instead of propane/propene/toluene conversion based on the cooling ramp was used to establish the catalytic performance. Since these VOCs, especially toluene, can be adsorbed onto the catalyst surface, forming some intermediates at low temperatures that will be further converted to CO 2 at higher temperatures, which can cause overestimation of the catalytic performance. The data were collected under a transient temperature ramped regime and the conversion of propane/propene/toluene to CO 2 was calculated by the following equation: X C3H8 (%) = [CO 2 ] out -[CO 2 ] in 3[C 3 H 8 ] × 100 X C7H8 (%) = [CO 2 ] out -[CO 2 ] in 7[C 7 H 8 ] × 100 X C3H6 (%) = [CO 2 ] out -[CO 2 ] in 3[C 3 H 6 ] × 100 Where [CO 2 ] out , [CO 2 ] in, [C 3 H 8 ], [C 7 H 8 ] and [C 3 H 6 ] represented the outlet and inlet CO 2 concentrations, and the initial propane, toluene and propene concentrations, respectively.
Kinetic measurements For propane oxidation, the kinetic data were measured at atmospheric pressure. The reaction rate, r (mol g -1 s -1 ), was obtained following the equation  [27] : r = X⋅V/M Where X and V were the conversion and the flow rate (mol s -1 ) of propane, M was the weight of the catalyst (g). The specific reaction rate, r', was calculated by the equation  [27] : r' = X⋅V/S Where S represented the surface area (m 2 ) of the catalyst (S = SSA⋅M). The apparent activation energy, E a , was calculated based on the Arrhenius formula. ln r = -E a RT + lnA Where R was the ideal gas constant (8. 314 J mol -1 K -1 ), T was the reaction temperature (K), and lnA was a constant. E a could be obtained from the slope of the linear plot of ln r versus 1/RT. The conversion below 10% was applied in the calculation of E a.
Acknowledgements This work was financially supported by the  National Key Research and Development Program of China  ( 2016YFC0204300 ), the  National Natural Science Foundation of China  ( 21577035 ), the  commission of Science and Technology of Shanghai Municipality  ( 15DZ1205305 ) and the  Auvergne Rhône Alpes region  ( PAI  project number  1900797501-10687 ). The contributions of the  China Scholarship Council , the  Institut de recherches sur la catalyse et l'environnement de Lyon (IRCELYON) ,  CNRS ,  Université Claude Bernard Lyon 1  ( UCBL1 ) and  Istituto per lo Studio di Materiali Nanostrutturati-CNR  are also acknowledged.
1 and ChemEQL software  [33, 34]  for 0. 002 M initial metal concentration is presented in Fig. 5. Visual MINTEQ 3. 1 and ChemEQL programs are among the most commonly used. Nevertheless, some discrepancies between calculations made with this software are observed. For example, calculations of Cu (II) speciation carried out with the ChemEQL program do not take into account anionic chloro complexes present in the aqueous solution, i. e. The result of calculations showed that the anionic species are easily formed in an HCl solution. The monovalent CdCl + and PbCl + species are the first chloro complexes formed in a solution of low chloride concentration (below 0. 5 M). At higher concentrations, the neutral and In the separation processes such as solvent extraction and transport through liquid membranes, it is usually assumed that the extraction/ transport mechanism of metals by basic extractants/carriers occurs as an anion exchange process  [35, 36].