dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful for the financial support from the  University of Groningen  (start-up package in the area of green chemistry and technology for  Jun Yue , and Ubbo-Emmius Fund 2015). This work was also financially supported by the  University of Nantes ,  Region Pays de la Loire  ( Chaire Connect Talent ODE ). The authors thank the technical support from  Gwenaël Biotteau  and  Nicolas Lefevre  at the  University of Nantes ,  Erwin Wilbers ,  Marcel de Vries ,  Léon Rohrbach  and  Daniël Vreugdenhil  at the  University of Groningen.
Although scientists spend a great deal of time building and revising EoS, and much journal space is dedicated to introducing these valuable tools, the question of their accuracy inevitably arises because these models are, by definition, a simplification of reality. In this paper, to address the relevant issue of model reliability, it was decided to build a high-quality reference database usable for assessing the accuracy of a thermodynamic model or to cross-compare the performances of two different models. This work was conducted with the full support of the working party (WP) on thermodynamics and transport properties (https://wp-ttp. dk/) of the European Federation of Chemical Engineering (https://efce. info/). In 2010, this WP published 3 a paper entitled: "Industrial requirements for thermodynamics and transport properties" and one of the many conclusions, which fully justifies this work, was "new models are published very frequently, but unfortunately most, if not all, of these models are tested against only a few data sets.
The issue of having large, standardized set of data against which all models may be tested is a very pertinent one that needs addressing and will be of great benefit, as it will allow models to be compared on equal footing. " In the first part of this paper devoted to the building of a benchmark database, a total of 200 nonelectrolytic binary systems was selected to cover all categories of mixtures and divided into nine groups according to the associating character of the components, i. e. , to their ability to be involved in a hydrogen bond. The nature and strength of the association phenomena are indeed considered a measure of the complexity to model the thermodynamic properties of mixtures. In the second part of this paper, it is explained how to assess the accuracy of an EoS and to grade it with the proposed database.
In particular, the exact procedure that has to be used to unambiguously calculate the deviations between model predictions and experimental data is discussed in detail and emphasis is given to the selection of specified and calculated variables. For illustration purposes, in the last part, the Peng-Robinson (PR) EoS with classical van der Waals mixing rules and a temperature-dependent k ij is scored after being used to correlate the various data of the proposed database. The grades are then interpreted through a discussion dealing with the accuracy of such a thermodynamic model.
CRITERION TO BUILD A BENCHMARK DATABASE 2. 1. Literature Review. Two databases that contain a vast number of experimental data points are widely used by researchers in the area of applied thermodynamics. The first one is the Dortmund Data Bank (DDB) (http://www. ddbst. com/ddb. html), which provides the largest data bank worldwide for thermophysical properties of pure components and their mixtures. In February 2020, the DDB contained 8. 7 million data points for approximately 75 000 components from 88 200 references. The second one, provided by the National Institute of Standards and Technology (NIST), also contains pure-component and mixture data, divided into 90 Nation's Standard Reference Data (SRD) databases (https://www. nist. gov/srd). The trustworthiness of the data is assessed by experts, so that they can be used with confidence to make significant decisions.
Although extremely useful, such databases cannot be used as such to assess the accuracy of a thermodynamic model. First, they are so comprehensive that a model evaluation procedure accounting for all of the data would require an excessive amount of time. Second, the different types of systems (e. g. , those that form ideal solutions, that contain size-asymmetric components, etc. ) and data (e. g. , vapor-liquid equilibrium (VLE) data, liquid-liquid equilibrium (LLE) data, etc. ) are not equitably distributed; an evaluation over these data would lead to biased conclusions about the model efficiency. Our goal is thus to build a subdatabase from these very complete databases after a strict selection of the systems and data so that the diversity of molecular interactions and all kinds of fluidphase behavior are represented.
To the best of our knowledge, the unique benchmark database for nonelectrolytic and nonpolymeric systems available in the literature is the one proposed by Danner and Gess in 1990. 4 This database that embeds data for 104 binary systems shows the following characteristics. • Only subcritical VLE data are available. • Data quality is ensured by performing consistency tests. • Pure compounds are classified according to their polarity, from nonpolar to strongly polar, leading to the definition of nine classes of binary systems. • For a given class, binary systems are categorized with respect to their deviations from ideality and there is an even distribution between ideal and nonideal systems. In our opinion, such a database has many desirable features, including the classification scheme of the binary systems.
However, several weak points can be pointed out: VLE data are limited to the subcritical domain and other types of phase equilibrium data, e. g. , LLE, vapor-liquid-liquid equilibrium (VLLE), or derived properties like enthalpies and heat capacities are not included. Moreover, such a database was made available by their authors "on a 5 1/4 or 3 1/2 in. MS-DOS formatted floppy disk", and we cannot assert that it is still possible to get a copy or to read this support. This short literature review makes it possible to conclude that the time has come to propose a revised version of the database developed by Danner and Gess,  4  which we hope will be widely adopted by others for assessing the accuracy of a thermodynamic model or cross-comparing two models. To guide the reader over the understanding of the choices we made to build this database, the following sections aim to address three questions.
First, it is indisputable that a good reproduction of phase equilibrium data is mandatory to ensure that processes meet their compositional specifications. However, depending on the temperature and pressure conditions, phase equilibrium data may involve two subcritical compounds, a subcritical and a supercritical compound or possibly two supercritical compounds. In the two latter cases, binary critical points appear on Industrial & Engineering Chemistry Research the corresponding phase diagrams. For some systems, azeotropic behavior and three-phase VLLE can occur as well. To consider such a diversity, it was decided to embed all kinds of phase equilibrium data in the developed database, i. e. , VLE, LLE, VLLE, and azeotropic data points, and to cover a very wide range of compositions, temperatures, and pressures.
Eventually, we decided to include both mixing enthalpy and mixing heat capacity data in the proposed benchmark database. To conclude this section, we want to emphasize that we intentionally did not incorporate excess volume data in the proposed database. This is because we know by experience that volume change on mixing is generally small (and often very small). As a direct consequence, the calculated density of a phase mainly depends on the accuracy with which the density of the pure components is calculated but is only slightly influenced by the mixing rules used. This implies that before grading a thermodynamic model with the proposed database, it is necessary to provide information on how the model behaves to correlate/predict the properties of pure compounds and in particular the vapor pressures, the liquid densities, the enthalpies of vaporization, and the liquid heat capacities.
It was also decided to exclude electrolytic systems and to only consider fluid-state binary systems, meaning that the proposed database does not include liquid-solid, solid-solid, or adsorption  9  equilibrium data. 2. 3. Which Binary Systems Should Be Included? The Danner and Gess database retained our attention as it not only compiles experimental data but also provides a classification for binary systems, revealing their thermodynamic complexity (here, thermodynamic complexity means a measure of the nonideal character of a system inducing difficulties for a model to reproduce thermodynamic macroscopic properties). In the paper by Danner and Gess, pure compounds were categorized with respect to their polarity and were classified as nonpolar, weakly polar, strongly polar, or very strongly polar.
Such authors define polarity as the ability of a molecule to donate or accept a hydrogen atom and the presence of hydrogen bonding. Starting from these four categories of pure compounds, Danner and Gess defined nine classes of binary systems (nonpolar/nonpolar, nonpolar/weakly polar, etc. ), and for each class, their database includes a variety of systems ranging from ideal to highly nonideal. We were totally convinced by this approach and decided to propose a somehow similar classification of binary systems but giving more emphasis on the associating character of the pure compounds, i. e. , on their ability to be involved in a hydrogen bond, rather than on their polarity. Indeed, the various publications devoted to the CPA  [10] [11] [12]  or SAFT  [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]  EoSs clearly indicate that an actual scientific challenge for EoSs is their ability to properly describe the very complex association phenomena.
Di-or trihalogenated compounds or the ones with a terminal alkyne group belong to this family. The labile hydrogen earns its acidity from the electronegativity of its closest neighbors. In short, this category includes polar but nonassociating components. • Category 4 (associating character denoted SA for "selfassociating"): The molecule possesses both a labile hydrogen atom and a lone pair of electrons. It is strongly polar. Such molecules fulfill the two necessary and sufficient conditions to establish a hydrogen bond. They are thus self-associating. Water, alcohols, and carboxylic acids belong to this family. In short, this category includes polar and associating components. Table  S1  of the file ie0c01734_si_011. pdf which is part of the Supporting Information lists the 107 pure compounds included in the proposed database and indicates for each of them the corresponding associating character among nonassociating (NA), hydrogen-acceptor (HA), hydrogen-donor (HD), or self-associating (SA).
2. 3. 2. Definition of Binary Association Codes (BACs) to Describe Association in Solution. With this associating character coding, the binary systems in the database were immediately divided into the following ten families by performing all of the possible combinations (see Table  1 ), and a binary association code (BAC) was assigned to each family. From our experience, much less experimental data are reported for HA/HA and HD/HD binary systems than for any other family. The lack of experimental data on HD/HD binary systems can be explained by noticing that HD molecules are mostly refrigerants that are generally encountered as pure compounds in chemical processes. Therefore, it was decided to merge HA/HA and HD/HD families, which are both nonassociating mixtures, into a single family, thus reducing the number of BACs to nine.
From Table  1 , it is possible to determine at a glance in which binary systems hydrogen bonds occur. Indeed, the requirement for the existence of a hydrogen bond remains to be the presence of both a labile hydrogen atom and a lone pair of electrons. In the case when the labile hydrogen atom and the lone pair of electrons originate from two different species, the hydrogen bond is said to be formed by "cross-association" between compounds 1 and 2. The last column of Table  1  highlights that all types of binary systems (those in which nonassociation, self-association, cross-association, or both cross-association and self-association are taking place) are included in the proposed database. In other words, four categories of binary systems based on the type of association they exhibit can be distinguished.
Therefore, it is not always possible to determine a priori how the mixture is going to behave (thermodynamically) and if the mixing leads to an intensification or a relaxation of hydrogen bonding. 2. 3. 3. Quantification of Deviations from Ideality. For each class of binary system, identified by its BAC, our motivation is to include in the proposed database a large variety of binary systems revealing the variety of deviation-from-ideality types. This section aims to explain how such deviations were sorted from both quantitative and qualitative viewpoints. Qualitatively, a system will be considered ideal if it is formed by molecules having (i) similar sizes, (ii) similar shapes, and (iii) showing similar energetic interactions (nature and strength), regardless of the temperature, pressure, and composition.
Such an overview of the possible causes of deviations from ideality is important as many thermodynamic models are built by summing the contributions of an enthalpic (related to the energetic interactions in the system) and an entropic (related to the size of the molecules) term. To provide a benchmark database for EoS model evaluation, it is necessary to include mixture data spanning the whole range of departure-from-ideality types (from ideal to nonideal, including enthalpic, entropic, and combined effects). Doing so, our database will allow the identification of model failures in their enthalpic and/or entropic modeling patterns. Quantitative information on how deviations from ideality were quantified and how the origin of such deviations was assessed is now detailed. To start, it is worth recalling that to determine the magnitude of the deviations from ideality, the activity coefficients have to be calculated from experimental VLE data.
It can be done easily using the VLE relationship stemming from the γ-φ approach: T x P y P T x ( , ) / ( ) i i i i exp ,exp sat exp ,exp γ = • [ • ] (1) Equation 1 however requires a correlation to estimate the vapor pressure of component i, so that only subcritical VLE data (T < T c,i ) can be used to estimate the deviations from ideality. In the case subcritical VLE data are not available for a system included in our database, the message deviations from ideality not assessed is reported for such a system in Table  2. For each binary system for which subcritical VLE data were available, a set of four parameters (p 1 , p 2 , p 3 , p 4 ) of the Margules excess Gibbs energy model was fit over both VLE data and enthalpy of mixing data (when available). x g T RT x x A T A T p p T p T P T ( , ) ( ) ( ) ln exp 1 E 1 2 1 2 3 4 = • • = - - + l m o o o o o o o n o o o o o o o i k j j j y { z z z (2) The p i parameters were determined to minimize the mean average percent error (MAPE) between calculated and experimental VLE and h M data.
Industrial & Engineering Chemistry Research factor. In other words, the following classical VLE relation, especially suitable to correlate subcritical data, was used: Py P x i i i i sat γ = (4) The vapor pressures were calculated by means of correlations extracted from the DIPPR database. Once the four parameters (p 1 , p 2 , p 3 , p 4 ) have been fit, the magnitude of the deviations from ideality can be estimated. For each temperature involved in the regression procedure, g E , h E , and s E were calculated at the unique composition x 1 = 0. 50. Based on our experience, a binary system can be considered ideal (see Table  2 ) if |g E /RT| x = 0. 5 < 0. 1 for each of the For nonideal systems, the origin of the deviations from ideality (entropic, enthalpic, or combined) was only evaluated when h M experimental data were available and thus included in the regression procedure.
We indeed noticed that the calculation of the excess enthalpy and entropy of a binary system could be very uncertain when the four p i parameters were determined over VLE data only. For nonideal systems, when h M data were lacking, the systems were thus declared (see Table  2 ): nonideal with nonassessed causes. In turn, when h M data were available: 1. If |(h E /RT)/(s E /R)| x = 0. 5 > 2 for all temperatures, it was considered that nonideality stemmed from enthalpic effects. In Table  2 , the system is labeled nonideal with enthalpic causes. 2. If |(h E /RT)/(s E /R)| x = 0. 5 < 1/2 for all temperatures, it was considered that nonideality stemmed from entropic effects. In Table  2 , the system is labeled nonideal with entropic causes. 3. Otherwise, deviations from ideality are the consequence of combined effects, i.
e. , both enthalpic and entropic effects, and none is dominant. In Table  2 , the system is labeled nonideal with enthalpic and entropic causes. An overview of how the deviations from ideality are distributed among the nine BACs can be found in Table  2 , whereas the different steps that were followed to build the proposed database are summarized in Figure  2. 2. 4. How Many Systems and How Many Data Should Be Included? Commercial databases, especially the Dortmund Data Bank, report VLE data for tens of thousands of binary systems. It would be impractical to cross-compare EoS on such a huge number of data; therefore, our objective is to work with a much smaller number of wisely chosen binary systems to cover all categories of mixtures. For each of the nine families of binary systemsdefined each by a distinct BAC our objective is to consider systems, deemed as good representative of their family.
Among these systems, our objective is to reach a distribution of one-fourth of systems containing high-pressure VLE data (for which deviations from ideality cannot be assessed), one-fourth of ideal systems, and half of nonideal systems. The nonideal systems should include similar numbers of systems for which the origin of the deviations from ideality could not be assessed due to a lack of h M data or stem from enthalpic, entropic, or (enthalpic + entropic) effects. To have a meaningful representation of such a diversity, it was found acceptable to include around 20 binary systems per BAC. Another key point is to only retain high-quality-certified data explaining why the consistency of the data was checked with the help of four consistency tests: the point test,  27  the integral test,  28  the differential test, and the infinite-dilution test,  29  all performed with the Data Preparation Package (DPP), which is a software package for the regression of thermophysical property models commercialized by Dechema.
It is however worth noting that the aforementioned tests only apply to VLE data when both compounds are subcritical. As a direct consequence, among the many data included in our benchmark database, only the quality of the subcritical data can be guaranteed. For an ideal binary system, passing the point test was considered as a sufficient consistency condition, while for nonideal systems, the point test and two of the three remaining tests had to be passed to keep the experimental data in our database. Indeed, as explained by Wisniak et al. ,  30  the quality of the equilibrium data should be checked using simultaneously several of the available consistency tests. In the end, the proposed database comprises 200 binary systems and contains about 30 000 experimental data points (around 18 000 VLE/LLE data points, 8500 h M data, 1500 c P M data, and 2000 data points relative to critical points, azeotropic points, or three-phase lines).
2. 5. Built Database. An extensive screening of thousands of binary systems was first realized, and a complex strategy was developed to only keep the 200 systems that met our criteria in terms of (i) types of association, (ii) deviations from ideality, and (iii) quality, quantity, and diversity of the experimental data. Indeed, as previously discussed, we made our best in order that the selected systems are such that phase equilibrium data (VLE, LLE, VLLE, azeotrope) and mixing properties (h M , c P M ) are abundant in large temperature, pressure, and composition ranges. The abbreviations used for the associating character of a pure compound are NA for nonassociating, HA for hydrogen-acceptor, HD for hydrogendonor, and SA for self-associating. b Liquid phase refers indifferently to a VLE or an LLE data point.
Industrial & Engineering Chemistry Research As previously stated, an overview of how the deviations from ideality are distributed among the nine BACs can be found in Table  2. Similarly, an overview of how the different types of data are distributed among the nine BACs can be found in Table  3. For each BAC, Tables 4-12 detail the list of the selected binary systems, specify the origin of the deviations from ideality, and list the references in which the experimental data can be found. A scan of the 958 original references from which the data were extracted was realized, and the authors are committed to sending a copy of an article to any reader of this paper that would, e. g. , necessitate more details on the used experimental setup. Remark. Table  3  shows that the proposed database contains information for 225 azeotropic points.
Some of them can however not be found in the original publications from which the data were extracted. We indeed decided on some azeotropic systems to interpolate the experimental VLE data to determine the temperature, pressure, and composition of the azeotropic point. Several Excel files are available as Supporting Information. Here also, the first one, named ReadMe, provides an overview of the database and explains how it is organized, whereas the other sheets each contain the data related to a given binary system.
GRADING OF A THERMODYNAMIC MODEL BY MEANS OF THE PROPOSED DATABASE TO ASSESS ITS ACCURACY This section focuses initially on the methodology that has to be followed to evaluate the deviations between model predictions and experimental data (in particular, which property of each data set must be specified and which property must be calculated from the thermodynamic model and compared to an experimental value). It is then explained how a thermodynamic model may be graded. The methodology described hereafter to assess the accuracy of a model is specific, but we believe this approach is necessary to fairly compare the scores obtained by two thermodynamic models. It is exactly what happens with the grading of students: the professor establishes a very detailed marking scheme before marking the examination papers.
989 For each property available in the proposed database, Table  13  reports the variance and the specified and calculated variables. In other words, Table  13  dictates the methodology that has to be strictly followed to evaluate the deviations between model predictions and experimental data by means of the proposed database. Such a BAC corresponds to a binary system involving a hydrogen-donor (HD) molecule + a nonassociating (NA) molecule. 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.