dataesr/scientific-paragraphs-categorization huggingface dataset:
When this happened, the assumed experimental pressure was determined by trial and errors so that the calculated physical state of the system agrees with the experimentally observed state. The first guessed pressure is always the atmospheric pressure. In the 10 Excel files ie0c01734_si_001. xlsx (complete database), Such a BAC corresponds to a binary system involving two hydrogen-acceptor (HA) molecules or two hydrogen-donor (HD) molecules. Corresponding origin of the deviations from ideality and list of the references in which the experimental data can be found. By convention, molecule 1 refers to the more volatile component. )available as Supporting Information that contain all of the experimental data, the experimental pressure appears as unknown and the assumed experimental pressure is indicated in parentheses.
g. , Figure  4e ). The opposite may also happen: the binary system exhibits a unique critical point, but 2 are returned by the model (see, e. g. , Figure  4f ). In such cases, the experimental critical points are all declared out of model. 3. 3. Grading of a Thermodynamic Model. Our objective is to give a unique mark over 20 to a given thermodynamic model to immediately be able to benchmark its accuracy against the accuracy of other models. A grade of 20/20 is the highest attainable mark. It is obtained only if the model is able to exactly reproduce (with 0% deviation) all of the experimental data available in the proposed database. In all other cases, a score lower than 20 will be assigned to the model. Once the MAPEs are calculated (see eq 5), the scoring of a thermodynamic model starts by giving a mark over 20 to each of the nine families of binary systems, each identified by its BAC.
This mark was developed in a similar manner to Mark h M so that the following remarks apply. Remarks: 1. Analogously to h M , c P M goes to 0 when the mole fraction of the studied phase (z 1 ) goes to 0 or 1. In such cases, huge percent deviations and consequently very bad marks on c P M can be obtained. To avoid such a bias, if in eq 18 this quantity is set to 200%. 2. The choice of eq 18 is based on the fact that for the data points embedded in the proposed database, we noticed that a deviation of 50% on c P M leads to an average deviation of only 2% on the molar isobaric heat capacity of the corresponding stream calculated as c P,stream = z 1 c P,1 + z 2 c P,2 + c P M. As a consequence, eq 18 is developed so that a deviation of 50% on c P M leads to a good mark of 15/20. Important remark: Any mark below 0 is raised to 0.
ILLUSTRATION: GRADING AND DISCUSSION AROUND THE ACCURACY OF THE PENG-ROBINSON EOS WITH CLASSICAL MIXING RULES AND A TEMPERATURE-DEPENDENT BIP 4. 1. Model. In this section, it was decided to use the benchmark database developed in this study to assess the accuracy and to grade the Peng-Robinson  991, 992  (PR) EoS with classical mixing rules and a temperature-dependent BIP (denoted k ij ). We found no reason to introduce a volume shift because all of the experimental properties contained in the database are unaffected by a temperature-independent volume translation. 993, 994 or a pure component, the PR EoS is P RT v b a T v v b b v b ( ) ( ) ( ) i i i i i = - - + + - (20) Although nonconsistent  [995] [996] [997]  at very high temperatures, it was decided to classically use the Soave-type  998 i i i i ii i i i i i i i i i i i i i i i 1 1 1 b c, c, b c, c, a 2 c, 2 c, a c, 2 2 2 3 3 3 α α ω ω ω ω ω ω ω = • • =[ + - + + ] ≈ = Ω Ω = + ≈ = = Ω Ω = + - ≈ = + - ≤ = + - > = + - + - - - l Ç Å Å Å Å Å Å Å Å Å Å Å Å Å i k j j j j j j j y { z z z z z z z É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ ( 21 ) where P is the pressure, R is the gas constant, T is the temperature, a i and b i are the cohesive parameter and the molar covolume of the pure component i, respectively, v is the molar volume, T c,i is the experimental critical temperature, P c,i is the experimental critical pressure, and ω i is the experimental acentric factor of pure i.
Such experimental properties were extracted from the DIPPR database, thus avoiding their estimation by group contribution methods. 1000 o apply this EoS to a mixture, mixing rules are necessary to calculate the values of a and b of the mixture. Classical van der Waals one-fluid mixing rules  1001  are used in this study: a T z z z a T a T k T b z z b ( , ) ( ) ( ) 1 ( ) ( ) i N j N i j i j ij i N i i 1 1 1 ∑ ∑ ∑ = • [- ] = = = = l m o o o o o o o o o o n o o o o o o o o o o (22) where z i represents the mole fraction of component i and N is the number of components in the mixture (in this work, N = 2). The k ij (T) parameter is the BIP characterizing the molecular interactions between molecules i and j. In this study, the equation that gives the changes of k ij with respect to temperature (see eq 23) is the one used in the well-established PPR78  [1002] [1003] [1004] [1005] [1006] [1007]  and PR 2 SRK 1008 models and contains two parameters (A ij and B ij ) for the binary system i + j.
( ) k T A ( ) 2 ij ij T B A a b a b a b a b 298. 15 / 1 2 ij ij i i j j i i j j = - - × × - i k j j j y { z z z (23) 4. 2. Fitting Procedure. The selected model is a correlative (i. e. , nonpredictive) model, and for a given binary system, the two parameters involved in eq 23 have to be fitted against experimental data. In this work, it was decided that the two parameters would be determined, system by system, to ensure the best reproduction of the experimental data available in the proposed database by minimizing an objective function accounting for the deviations between experimental data (VLE data, LLE data, VLLE data, critical data, azeotropic Industrial & Engineering Chemistry Research data, heat capacity of mixing data, and enthalpy of mixing data) and model predictions. The selected objective function expression is F F F F F F F F F F F n n n n n n n 2 2 2 • F obj,x or x α quantifies the deviations on the liquid-phase composition for VLE data and on the α-liquid phase for LLE data: F x x x x x x x x x x x 100 0.
• F obj,c P M quantifies the deviations on the mixing heat capacities: where n cP M is the number of mixing heat capacity data points. Important remark: Section 3 of this paper, devoted to the grading of a thermodynamic model, dictates the methodology that has to be strictly followed to evaluate the deviations between model predictions and experimental data by means of Industrial & Engineering Chemistry Research the proposed database. If not, it becomes impossible to fairly compare two thermodynamic models. In turn, the parameterization of the tested EoS (in the present case, the fitting procedure described above in Section 4. 2) is not imposed and can be freely chosen. We are of the opinion that a researcher who wants to score a thermodynamic model (defined as the association of an EoS and a parameterization procedure) should be free to choose the number of adjustable parameters in the model, the experimental data to which such parameters are fitted, and the weighting given for each considered property during the fitting stage of the model parameters.
In the previous section, a correlative model (the PR EoS with classical mixing rules and a T-dependent k ij ) was selected so that the experimental data of the proposed database were used to fit the parameters of eq 23. If our goal were to assess the accuracy of a predictive thermodynamic model in which the binary interaction parameters are determined by group contribution (e. g. , PSRK,  1009, 1010  VTPR,  1011, 1012  UMR-PRU,  1013, 1014  PPR78, 1002 MHV1-UNIFAC,  1015  etc. ), the experimental data of the proposed database would not have been used for parameterization purposes. From the above, an EoS may thus get different grades depending on its parameterization. For example, it would be possible in this paper to grade the PR EoS by setting all k ij to 0. By doing so, a lower score than with temperature-dependent k ij would be obtained.
In short, a grade is in fact always given to a model, i. e. , to the association of both {an equation of state + a selected parameterization}. The proposed database can thus be used to test the influence of the parameterization on EoS performance, and we know by experience  1016  that such an influence may be huge. 4. 3. System-by-System Results of the Fitting Procedure. A system-by-system analysis of the {PR EoS + classical mixing rules with a T-dependent k ij } performances to correlate the data included in the proposed database can be found in Table  S2  of the file ie0c01734_si_011. pdf, available as Supporting Information. This very detailed analysis (over 1000 pages) includes the following. 1. Information on how the system deviates from ideality. 2. Values of the A and B optimal parameters (see eq 23).
CONCLUSIONS A benchmark database is presented in this paper to enable a proposed-standardized assessment of the performance of a given thermodynamic model or to compare two models. To classify a database's systems on the basis of their thermodynamic complexity, 107 pure components were first divided into four categories according to their associating character and nine classes of binary systems were defined combining these four pure-component categories. Each of these classes is identified by a binary association code (BAC). We are indeed convinced that binary systems must be classified according to the type of association they exhibit. For each class of systems, the proposed database includes a variety of binary systems ranging from ideal to highly nonideal. In the end, the proposed database embeds 200 binary mixtures uniformly distributed among the nine BACs and includes for each system both phase equilibrium data (VLE, LLE, VLLE, azeotropic data, critical data) and energetic data (h M and c P M data).
For each property, a detailed procedure explaining how deviations between model predictions and experimental data have to be estimated is provided. Finally, the performances of the Peng-Robinson EoS with classical van der Waals mixing rules and a temperaturedependent binary interaction parameter were evaluated over the full database. In light of the obtained results, a discussion about the strengths and weaknesses of the model was conducted. Such a thermodynamic model got an overall respectable score of 12. 3/20. Its main drawback is the poor correlation of systems in which hydrogen bonds are broken without the possibility of forming new ones. For such complex systems, the addition of an association term to the EoS and the selection of more sophisticated mixing rules could help improve the model efficiency.
Last but not the least, we hope that the proposed database will be widely adopted by the scientific community to identify the strengths and weaknesses of the many published thermodynamic models and to identify the remaining scientific obstacles that need to be overcome. We also hope that such a database will be used by model developers to test a new concept. As an example, experimental data of binary systems, the BAC of which is 7, 8, or 9, could be very useful to test the influence of various association schemes.
• The first one, named ie0c01734_si_001. xlsx, is a light Excel file that only contains the experimental data and the corresponding references. This file comprises 201 sheets. The first one, named "ReadMe", provides an overview of the database and explains how it is organized. The other 200 sheets each contain the data related to a given binary system. BAC 9 ) are large-size Excel files that provide figures to have a visualization of the experimental data (as a complement to data tables) and the singularities of the binary systems. These files contain several sheets.
■ ACKNOWLEDGMENTS The authors would like to present their grateful acknowledgment with thanks to  Profs. John M. Prausnitz  and  Alexey Victorov  for sending them many scans of papers that the authors could not find in Europe. Thanks are also due to all of the members of the working party (WP) on Thermodynamics and Transport Properties (https://wp-ttp. dk/) of the European Federation of Chemical Engineering (https://efce. info/) for their continuous encouragement and support during the development of the database depicted in this paper. Last but not the least, the  French Petroleum Company TOTAL , and more particularly  Dr. Laurent Avaulleé  and  Freddy Garcia  (experts in thermodynamics), are gratefully acknowledged for sponsoring this research.
• The first one, named ie0c01734_si_001. xlsx, is a light Excel file that only contains the experimental data and the corresponding references. This file comprises 201 sheets. The first one, named "ReadMe", provides an overview of the database and explains how it is organized. The other 200 sheets each contain the data related to a given binary system. BAC 9 ) are large-size Excel files that provide figures to have a visualization of the experimental data (as a complement to data tables) and the singularities of the binary systems. These files contain several sheets. • The nine others, one for each BAC, and named ie0c01734_si_002. xlsx (BAC 1 ), ie0c01734_si_003. xlsx (BAC 2 ), ie0c01734_si_004. xlsx (BAC 3 ), ie0c01734_- si_005. xlsx (BAC 4 ), ie0c01734_si_006. xlsx (BAC 5 ), ie0c01734_si_007.
Corresponding origin of the deviations from ideality and list of the references in which the experimental data can be found. By convention, molecule 1 refers to the more volatile component. bThe following part of this article (Section 3) aims at explaining how the proposed database should be used to grade a thermodynamic model to assess its accuracy. For illustrative purposes, Section 4 clarifies the grading of the thermodynamic model: {Peng-Robinson EoS + classical VdW mixing rules + a temperature-dependent BIP} and opens a discussion around its accuracy.
Acknowledgements The authors thank the  Research Funding Program  at the  Lebanese University, Lebanon , and the  Agence Universitaire de la Francophonie ,  Moyen-Orient  ( PCSI 2018-2020 ) for funding the project. Tracy El Achkar  is grateful to the  Université du Littoral Côte d'Opale  and the  Lebanese University  for providing her a scholarship. Fanny SIMELIERE from  Institut Galien Paris-Sud  ( Châtenay-Malabry, France ) and  Jacques LOUBENS  from  TA Instruments (Guyancourt, France ) are greatly acknowledged for their generous help. This work partially benefited from the support of the project  ParasiDES   ANR-19-CE18-0027  of the  French National Research Agency (ANR).
However none of these studies provided mole fraction profiles for the fuel or the decomposition products for benzaldehyde pyrolysis. Also to our knowledge no oxidation study has been reported in the literature at present. The aim of this study is to propose a first detailed set of data for the oxidation of benzaldehyde in a jet-stirred reactor (JSR) coupled with gas chromatographs (GCs). Using different type of detectors coupled to gas chromatography, mole fractions profiles were obtained for benzaldehyde and 48 of its products. These profiles were then used to update the benzaldehyde oxidation subset in the current version (1902) of the CRECK kinetic model.
Kinetic model The CRECK model adopted in this work (466 species and 16,263 reactions) implements a C0-C3 core subset obtained by coupling the H2/O2 and C1/C2 from Metcalfe et al. [17] , C3 from Burke et al. [18] , and heavier fuels from Ranzi et al. [19, 20]. It covers reactions from syngas to heavy diesel fuel pyrolysis and combustion, including PAH formation  [2]  as well as a new subset of reactions for oxygenated aromatics of interest as bio-oil surrogate components  [7]. The thermochemical properties for species not specifically belonging to the benzaldehyde subset were adopted, when available, from the ATcT database of Ruscic  [21]  or from Burcat's database  [22]. For the species involved in the benzaldehyde subset and not already implemented in the CRECK mechanism, the properties have been determined with the software THERGAS  [23]  based on Benson group additivity method  [24].
Conclusion In this work, benzaldehyde oxidation was investigated for the first time. Using a JSR coupled with gas chromatographs, 48 compounds were identified for various equivalence ratios covering from fuel lean to rich conditions. 28 mol fraction profiles were obtained for species from major ones (CO, CO2, phenol) to some minor products (acrolein, acetaldehyde, benzene, cresol, etc. ), other species being present in the form of traces. These new data were compared with simulation results obtained with an updated version of the CRECK kinetic model. The model update allowed for a better description of benzaldehyde oxidation. The decomposition of benzaldehyde mainly leads to the formation of phenyl radical which then oxidize to give phenoxy radical (C6H5O), ruling the formation of almost all the observed products.
4 min, 2% phase B; 13. 4 min, 57% phase B; 13. 5-15. 8 min, 100% phase B; 18 min, 2% phase B. Separation was carried out on an Agilent Poroshell HPH-C18 column (4. 6 mm × 100 mm, 2. 7 µm particles-Waghaeusel-Wiesental, Germany) used with a pre-column, UHPLC guard Poroshell HPH-C18, 4. 6 mm. The column was maintained at 40 • C ± 0. 8 • C in a thermostatted column compartment (G1316A) during the analyses. The fluorescence detector was set to an excitation wavelength of 340 nm and an emission wavelength of 450 nm. The total runtime of the method was 20 min. Chromatographic data were acquired and evaluated with the HPLC 1260 OpenLAB software (Waghaeusel-Wiesental, Germany). Internal calibration was done by spiking 20 µL of L-Norvaline in each sample and standard.
Possible chemical and spectral interferences Further possible interferences on NO 2 detection in the presence of high levels of O 3 were also studied, since a large excess of O 3 could trigger the following reactions with rate constants that are few orders of magnitude lower than k 1 (from the NIST Kinetics Database): NO 2 + O 3 → NO 3 + O 2 k 2 = 3. 8 x 10 -17 cm 3 molec -1 s -1 at 25 °C (R2) NO 2 + O 3 → 2O 2 + NO k 3 = 1. 0 x 10 -18 cm 3 molec -1 s -1 at 25 °C (R3) NO 3 + O 3 → 2O 2 + NO 2 k 4 = 1. 0 x 10 -17 cm 3 molec -1 s -1 at 25 °C (R4) To study those possible interferences, 100 ppb of NO 2 produced by a permeation tube were pumped through the ozonizer and the spectrometer at a flow rate of 1 L min -1 while varying the concentration of O 3 from 0 to 10 ppm. NO 2 concentration was stable at low ozone concentrations, while a drop of 14 % was observed at high levels of O 3 ( 8 ppm).
Acknowledgements. The research leading to these results has received funding from:  PARCS  project (Pollution in the ARCtic System, a project of the  CNRS French Artic Initiative : http://www. chantier-arctique. fr/en); the  LabEx OSUG@2020  (" Investissements d'avenir " - ANR10 LABX56 ) ; the  French National program LEFE (Les Enveloppes Fluides  et l'Environnement) ; the  Agence Nationale de la Recherche (ANR)  via contract  ANR-16-CE01-0011-01  EAIIST ; the  Foundation BNP-Paribas  through its  Climate initiative program  and by the  French Polar Institute (IPEV)  through programs  1177  ( CAPOXI 35-75 ) and 1169 (EAIIST). The authors would like to thank the  LabEx OSUG@2020  for funding the ThermoFisher™, 42i NOx analyzer and IPEV for funding the ThermoFisher 42iTL™, NOx trace analyzer that were used during the development of the IBBCEAS instruments presented here.
[27]  We start our potential refinement by considering the parameters (A i j , ρ i j and C i j , k cs 2 , k cs 4 and q) of Ref. [27]  and perform a simultaneous fit of the potential on 44 experimental observables including the atomic positions, lattice parameters and dielectric constants for α-, β -, γ-TeO 2 using the general utility lattice program (GULP) program. [31]  This procedure differs from that of Ref. [27] , where a sequential fit was performed on the crystalline phases of TeO 2. We here consider all three phases together and repeat the fitting until convergence of all potential parameters is Ref. [27]. Compared to the old potential  [27] , we here find smaller A (O-O) and C (Te-O) values which lead to a lower electronic and van der Waals interactions on the O-O and Te-O bonds, respectively.
Liquid and Amorphous TeO 2 We resort to classical molecular dynamics simulations (MD) as implemented in the DL POLY program  [45]  together with the obtained IAP to generate high temperature model of TeO 2. We consider a 768 atoms system (256 Te + 512 O) and 768 shells in a cubic box of side length 23. 00 Å corresponding to the experimental density. [27]  The temperature is controlled through a Berndson thermostat and a time step of 1 fs is used to integrate the equations of motion. The system has been equilibrated in the NVT ensemble for 60 ps at T=2100 K. For comparison, we also generate a second model based on the rigid-ion IAPs of Ref. [27]. Figure  1  shows the mean square displacement of Te and O as obtained from both models. We find that the new parametrization of the IAP leads to high mobility of both Te and O with a liquid-like diffusion coefficients amounting to 1.
A force measurement system using a data acquisition (DAQ) system (National Instruments, Austin, TX, USA) and a linear stage was built to evaluate the sensing properties of the tactile sensor. The probe tip can control the position of the load. The DAQ is used to obtain piezoelectric response from sensor and control the linear stages and signals processing steps simultaneously (Figure  8b ). Details on the setup for pressure measurements are provided elsewhere  [51]. As shown in Figure  S3a , the sensor array electrodes, with a pitch of 300 μm with respect to the 300-μm-wide electrode terminal, were connected to the printed circuit board (PCB) and placed on a stage. To make this connection, the sensor electrode and electrode pad (Figure  S4 ) on PCB were aligned using an anisotropic conductive film (ACF) bonder (GZC-YPJ09) and bonded together with the z-axis (i.
e. , the thickness direction) conductive adhesive transfer tape (3M ECATT 9703), as shown in Figure  S4  of the Supplementary Materials. The tape allows for electrical conduction throughout the adhesive and mechanically bonds the flexible tactile sensor to the PCB, without the need for thermal curing. The open-circuit output voltage of the tactile sensor to a force of 0. 2 N, applied using a plastic A force measurement system using a data acquisition (DAQ) system (National Instruments, Austin, TX, USA) and a linear stage was built to evaluate the sensing properties of the tactile sensor. The probe tip can control the position of the load. The DAQ is used to obtain piezoelectric response from sensor and control the linear stages and signals processing steps simultaneously (Figure  8b ). Details on the setup for pressure measurements are provided elsewhere  [51].
Funding: This research was supported by the  Civil & Military Technology Cooperation Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science and ICT  (NO. NRF-2014M3C1A9060874 ), by the  Korea Evaluation Institute of Industrial Technology (KEIT)  funded by the  Ministry of Trade, Industry and Energy (MOTIE)  (NO. 10074267 ), and by the  Samsung Research Funding Center of Samsung Electronics  under  SRFC-IT 1501-03.
The geometry was firstly designed as a CAD model (nodes and elements) and then transformed to a particle model as explained in the Appendix. Simulations were run with the Open source code LAMMPS  [23]. In the simulation, the flow is driven by a periodic acceleration (Figure  2 ) given by (  1 ) where G0=400 ms -2 , ω = 2πf is the angular frequency, n = 13 and ϕ = π⁄10 as discussed in Stevens et al. , 2003. The value of G0 is determined to achieve full opening of the valve that gives an average flow rate around 600 mL s -1 for valves at normal condition, which is consistent with the literature e. g. [  [24] [25] [26].