dataesr/scientific-paragraphs-categorization huggingface dataset:
The nominal values correspond to Eu contents based on the Eu/(Eu+Ca) molar ratios used in the synthesis (0, 10%, 30 and 50%, green line in the figure), assuming substitution of 1 mol of Ca 2+ by 1 mol of Eu 3+. Observation of Figure  3c  reveals that i) experimental values are lower than nominal ones (green line) in both series and ii) Eu 3+ contents in NaMo samples are even lower than in AMo samples. As described above, the elemental composition data suggest a substitution of 3 moles of Ca 2+ with 2 moles of Eu 3+ in AMo samples, and the substitution of 2 moles of Ca 2+ with 1 mol of Eu 3+ in NaMo samples. In both cases, therefore, the experimental Eu 3+ content is expected to be lower than the nominal one. Likewise, on the basis of the proposed substitution mechanisms, the Eu 3+ content in NaMo samples is expected to be lower than in AMo samples, in good agreement with the data points in Figure  3c.
ACKNOWLEDGMENT The authors thank the  European Commission  for the financial support through the project  SUSPOL-EJD 64267. M. J. -L. T. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  752405. A. P. D also acknowledges funding from  ERC  (grant number:  681559 ). O. C. is Research Associate of the  F. R. S. -FNRS in Belgium.
Such a model would also have many industrial applications (e. g. , aluminium, chromium and iron production, and their corrosion, solve scaling problems in geothermal energy and oil production). Comparisons of solubility and activity calculations with the experimental data in binary and ternary systems indicate that model predictions are within the uncertainty of the data. Limitations of the model due to data insufficiencies are discussed. The solubility modeling approach, implemented to the Pitzer specific interaction equations is employed. The resulting parameterization was developed for the geochemical Pitzer formalism based PHREEQC database.
Therefore, the use of such approach can considerably reduce the laboratory experiments and field works. Geochemical calculations and modeling results are strongly dependent on the model solution and solid phase parameters and experimental database used in parameterization. The traditional activity models (as the extended Debye-Hückel model, Davis model), commonly implemented in geochemical calculation codes, are not able to represent the interactions between all solution species, limiting their applications to dilute solutions, generally up to the salinity of sea water. To describe behavior of highly saline solutions, a specific and robust approach is needed, which includes all possible solution species and interactions in solutions, as well as all experimentally determined equilibrium solids.
The specific interaction approach for describing electrolyte solutions to high concentration introduced by  Pitzer [1, 2]  represents a significant advance in physical chemistry that has facilitated the construction of accurate thermodynamic models. It was showed that the Pitzer approach could be expanded to accurately calculate solubilities in complex brines and to predict the behavior of natural fluids at standard temeperature[3-5], as well as at wide range of temperatures from 0 o to 300 o C [6-9] , and from extremely low  [10] [11] [12]  to very high concentration of solutions  [8, 9, 35]. However, the existing Pitzer's databases have different limitations as the number of implemented solution species and precipitating solids, the temperature and concentration ranges of application. This is why a permanent updating work must be done in order to increase their applicability, and consequently the quality of predicted results.
This paper continues our series () concerning parameterization of Pitzer approach based comprehensive models which can assist in the development of a complex thermodynamic database that can accurately predict the chemical behavior of aluminium Al 3+ [10-17], chromate (CrO4)  [18] , dichromate (Cr2O7)  [19, 20] , and Iron (as Fe 2+ [21], and Fe 3+ [21,35] solution species and minerals in a sea-type natural fluid systems. The main objective of this study is the development of a high accuracy thermodynamic model for solution behavior, and highly soluble M(III)Cl3(s) (M= Al, Fe, Cr) minerals solubility in Na-Al(III)-Cr(III)-Fe(III)-Cl-H2O system at 25°C. Sodium chloride fluids constitute most of the deep groundwater resources. Comprehensive thermodynamic models that accurately predict aluminum, chromium and iron aqueous chemistry and M(III) mineral solubilities as a function of pH, solution composition and concentration are critical for understanding many important geochemical and environmental processes involving these metals (e.
The resulting parameterization gives a good agreement with water activity values up to 16 mol. kg -1 , widely over the solubility of FeCl3, 6H2O(s) (m(sat) (FeCl3, 6H2O(s)) = 6. 2 mol. kg -1 ). Therefore, in construction of mixing Na-Fe(III)-Cl-H2O model we used our previously evaluated FeCl3-H2O binary parameters from  [35]. The solubility product (K o sp) value for FeCl3, 6H2O(s) is taken from Thermoddem database  [38]  and it is very close to those determinated by  [21]. Rumyantsev et al. [37] and [21]  developed a model for ternary system Na-Fe(III)-Cl-H2O system. The activity model of  [37]  used their own low molality isopiestic measurements data to evaluate mixing parameters. The mixing solid-liquid equilibrium model of  [21]  have been developed on the basis on the solubility data only.
Summary and Restrictions The equilibrium model presented in this article calculates solubilities and solution activities in the Na-Al(III)-Cr(III)-Fe(III)-Cl-H2O system at 25°C and to high solution concentration within experimental uncertainty. Following an approach in our previous modeeling studies on M(III) systems  [10, [14] [15] [16] [17] 21, 35] , in this work we accept that complex Al(III), Cr(III), and Fe(III) aqueous species do not exist in solutions. Binary and mixing solution parameters are evaluated using activity (when available) and solubility data. We test the model by comparing model predictions with experimental data (activity data for unsaturated solutions and solubility data in ternary systems). Since the model has been parameterized for all the constituent binary and ternary systems in the quinary sytem under study, it can in principle, be applied to all compositions in the Na-Al(III)-Cr(III)-Fe(III)-Cl-H2O system.
While both of the measurements  19, 37  were performed using the same counter-flow flame burner, large data deviation was observed. Other laminar flame speed measurements of Jet A-1 were depicted in the investigation of Chong et al. 18  The PIV optical technique has been applied on a jet wall stagnation flame to determine the laminar flame speed of Jet A-1. Regarding the laminar flame speed of Jet A-1 measured under pressure conditions higher than 0. 5 MPa, to the best of the authors' knowledge, the only experimental data available are found in the work of Vukadinovic et al. 38  In their study, the laminar flame speeds were measured at a pressure of up to 0. 8 MPa using the spherically expanding flame method. 0] [41]  Even though part of the discrepancies are due to the fact that the composition of Jet A-1 varies among different production regions, further investigation is still necessary to restrain the uncertainties introduced by methodologies, such as linear or nonlinear correlations, and by measurements devices.
The imageprocessing procedures are detailed in the following sections. 2. 4. 1. OH* Chemiluminescence. In order to obtain the twodimensional flame front of the reaction zone of the OH* chemiluminescence, the recorded image is transformed by Abel inversion using a MATLAB program. 57 By taking the inside flame front of the image after Abel inversion, the surface A can be calculated. The inside boundary is used because its laminar flame speed is defined by the velocity relative to the unburned premixed gas in the direction   of the flame surface. As the inside boundary is more attracted to fresh gas, it is more appropriate to use it as the flame boundary in order to calculate flame speed. A specific high-frequency filtering function was used to smooth the resulting curve plotted along the red line in Figure  3  and obtain the final flame front.
2. 4. 2. Kerosene-PLIF. Compared to the processing procedures of OH* chemiluminescence imaging, the data processing of kerosene-PLIF is largely simplified. The outer edge of the fresh gases is defined as the position at which the fluorescence of the aromatics molecules disappears (Figure  4 ). This location corresponds to the frontier delimiting a chemical transformation of these organic molecules through the action of chemical reactions. Typically, these organic molecules disappear before the OH radical starts to be optically detected. Once the flame front is obtained, the laminar flame speed is determined using the same calculation (eq 2) as the OH* chemiluminescence technique. 2. 5. Measurement Uncertainties. For all of the experimental results presented in this work, 30 single-shot images are systematically recorded and the resulting laminar flame speed is determined by the data processing of averaged images, as determined from the set of single-shot images.
An empirical correlation of the laminar flame speed in taking account of temperature and pressure dependency is then proposed. As illustrated in Figure  16 , the general tendency of the effect of the preheating temperature on the laminar flame speed is in agreement with theoretical predictions, i. e. , the laminar flame speed increases in line with the preheating temperature. L0 L , 1 L ,1 L,2 2 L,3 3 0 0 ( 1) ( 1) ( 1) The resulting parameters of α for different equivalence ratios are listed in Table  3. High-pressure measurements are performed for Jet A-1/air mixture at conditions of T = 423 K and P = 0. 1-0. 8 MPa for equivalence ratios φ = 0. 7-0. 8. As illustrated in Figure  18  laminar flame speeds are plotted with pressure variation using a logarithmic scale. Apart from the experimental data represented by symbols, the linear fit lines to =     ments reported in Figure  19  were performed at T = 400 K, P = 0.
Properties of pure ionic liquids The density and heat capacity were measured for pure ionic liquids and the purity before and after each measurement was studied, in N N + R 1 R 1 [C 2 C 1 Im] + = 2 [C 4 C 1 Pyrr] + [C 4 C 1 Im] + = 4 [C 6 C 1 Im] + = 6 N + N - N N P - F F F F F F N -S S O O O O F F F F F F S O O - O O [Dca] - [PF 6 ] - [NTf 2 ] - [C 2 SO 4 ] - [C 4 C 1 Im][NTf 2 ] 0. 5, 1. 0, 3. 0 - - [C 2 C 1 Im][C 2 SO 4 ] 0. 5, 1. 0, 3. 0 - - [C 4 C 1 Pyrr][NTf 2 ] 1. 0, 3. 0 1. 0, 3. 0 1. 0, 3. 0 [C 6 C 1 Im][PF 6 ] 0. 5, 1. 0, 3. 0 0. 5, 1. 0, 3. 0 0. 5, 1. 0, 3. 0 particular the water content which may increase during/after the data collection. Thus, the water content after heat capacity assessments was checked by weighing the mass of the pan, and directly by using Karl Fischer titration after density measurement.
No changes were recorded which means that the pans containing studied samples were sealed from the environment and water absorption. It was previously reported that water may influence the physical properties  [38]. In the case of heat capacity, 1% of water shifts the heat capacity for about 1. 6% (1-butyl-3-methylimidazolium tetrafluoroborate)  [39]. Nevertheless, density is much less dependent on the water content -20% of water content changes the density of 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide for about 1%  [40]. The experimental data for the density and the heat capacity of the pure ILs are reported in supporting information files (SI1 and SI2). Their values were also compared to those previously reported. An extensive comparison to NIST ILs database was performed by taking into account all available literature positions, the relative deviations are presented in Figs.
Density of ionanofluids The density of ionanofluids containing different loading of carbon nanotubes, boron nitride or graphite nanoparticles dispersed in selected ILs was measured as a function of temperature. The results are shown in Figs. 4, 5 and 6 for ionanofluids with carbon nanotubes, boron nitride and graphite, respectively. The parameters of Eq. (  1 ) are summarized in Table  3. It can be seen that, for all cases, the density decreased as a function of temperature, whereas the loading of nanoparticles caused the density shift to higher density values, as expected. It indicates that the bulk density of all solid materials exhibits higher values. The experimental data were collected in an Excel file (SI1). It can be observed that the addition of nanoparticles into ionic liquid is the base fluid independent.
Heat capacity of ionanofluids The results of the heat capacity measurements for pure ILs and systems with multi-walled carbon nanotubes, boron nitride and graphite are shown in Figs. 11, 12 and 13 for carbon nanotubes-, boron nitrideand graphite-doped ionanofluids, respectively, while the parameters of second-order Eq. (  4 ) are summarized in Table  4. The experimental data are shown in SI2. Unfortunately, very few datasets have been previously reported for similar systems, to date, which limits the comparison that can be made. As can be seen the heat capacity of all data increases with temperature, as expected. The specific heat capacity was also found to increase with nanomaterials loading (with restriction up to 3% in nanoparticles mass fraction units, as investigated), as can be clearly seen in Figs.
Results The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n° 291049 -2G-CSafe and from the Agence Nationale de la Recherche (ANR-12-VPTT-0008). A cylindrical quartz tube was set between three heat sources and heated by hydrogen/oxygen flames. The temperature profile along the outer side of the reactor was measured by a FLIR A655sc infrared camera with an emissivity correction factor. A spectroscopy EMCCD camera (ProEM 1600) with a CH* band-pass filter was used to detect the flame positions. C 2 H 4 /air mixtures were supplied in a 1 mm internal diameter reactor at atmospheric pressure. Stable flames (a) have flat profiles due to t he narrow reactor section.
Acknowledgments: This work has benefited from CNRS funding. The authors would like to thank  P. Baroni  for innovative technical and instrumental developments. One of the authors (L. N. ) is particularly grateful to  F. Volino  for the multiple discussions on the shear elasticity in liquids. A special thought also to the late P. G. de Gennes who accepted to chair the first PhD thesis (H. Mendil) on the identification of low-frequency shear elasticity.
INTRODUCTION The livestock sector is a significant source of anthropogenic greenhouse gas (GHG) emissions. In the United States, emissions from livestock production contributed an estimated 48% of the 2015 agricultural GHG emissions  (US EPA, 2017). In Europe (EU-28), 59% of estimated agricultural GHG emissions were from livestock in 2015 (http:// ec. europa. eu/ eurostat/ web/ agriculture/ data/ database; accessed December 5, 2017). Methane (CH 4 ) and nitrous oxide are the 2 most important GHG from agricultural activities. Methane, a potent short-lived (12. 2-yr lifetime;  Myhre et al. , 2013)  GHG, is emitted from livestock operations through enteric fermentation in the animal's gastrointestinal tract (reticulo-rumen and hindgut) and similar methanogenic processes in manure. Globally, enteric CH 4 emissions make up about one-fifth of the 10 to 12 Gt CO 2 -equivalent/yr GHG emissions from the Agriculture, Forestry, and Other Land Use sector  (IPCC, 2014).
Examination of the δ 13 CH 4 database used in the Schwietzke et al. (  2016 ) study (https:// www. esrl. noaa. gov/ gmd/ ccgg/ d13C -src -inv/ ; accessed December 4, 2017), however, shows a relatively large variability and uncertainty in the δ 13 CH 4 data, from -68‰ (SD = 3. 0‰) for C3 plant-based ruminant diets to -54‰ (SD = 3. 0) for C4 plant diets; the authors used δ 13 CH 4 of -66. 8 ± 2. 8‰ as a global average for ruminants, which is very close to that for wetlands (-61. 5 ± 0. 6‰). Wang et al. (2015)  also reported similar δ 13 CH 4 for ruminal and swamp CH 4 samples. In the  Schwietzke et al. (2016)  database (over 8,100 observations), δ 13 CH 4 of fossil fuel CH 4 (average of -45. 0 ± 6. 96‰ with minimum and maximum of -64. 1 and -29. 1‰, respectively) had a standard deviation as high as 15 to 16‰.
0 and 63. 3% (manure), and -19. 3 and 19. 2% (total emissions), respectively  (Hristov et al. , 2017). In that analysis, major sources of uncertainties for enteric CH 4 were animal BW (lower and upper 95% confidence bounds across cattle categories: -18 to -24% and 21 to 29%, respectively), DMI (-21 to -29% and 21 to 29%), and CH 4 yield (-18 to -41% and 19 to 42%). In a model designed to estimate enteric CH 4 from Dutch dairy farms,  Bannink et al. (2011)  reported that the largest uncertainty (18%) was related to VFA stoichiometry. Estimates for total livestock CH 4 emissions in the  Hristov et al. (2017)  study were comparable to current US EPA (2017) estimates for 2012 (last census of agriculture) and to estimates from the gridded Emission Database for Global Atmospheric Research (EDGAR, 2011) inventory.
However, the spatial distribution of emissions in the  Hristov et al. (2017)  analysis differed significantly from that of EDGAR and a recent gridded inventory based on US EPA's emission database  (Maasakkers et al. , 2016). For example, the combined enteric and manure CH 4 emissions from livestock in Texas and California (the largest contributors to the national total) in the  Hristov et al. (2017)  study were 36% lower and 100% greater, respectively, than estimates from EDGAR. These differences originate from differences in emission factors between the 2 analyses [lower emission factors for feedlot cattle (i. e. , Texas) and higher emission factors for dairy cows (i. e. , California) in the  Hristov et al. , 2017 analysis]. Gridded bottom-up emission inventories, such as EDGAR, are commonly used to assess the contribution of CH 4 from different sectors within a region.
g. , diet composition and DMI) to develop robust, broadly applicable CH 4 prediction equations for applications such as livestock CH 4 inventories. Contributors supplying data to the GLOBAL NETWORK project used various methods for measuring enteric CH 4. Three databases were created, one each for dairy cows, beef cattle, and small ruminants (sheep and goats). In Table  1 , we present data for the main measurement techniques that were included in the dairy database of the GLOBAL NETWORK project. The RC sub-database included cows with DMI and milk yield that were lower than those of cows included in the GF sub-data set but comparable to those in the SF 6 data set. Also, the range of DMI was narrower for GF and SF 6 than for RC. As evident from the data, significant variation was associated with all measurement methods for CH 4 emission rate, yield, and intensity; the coefficient of variation (CV) for emission rate (g of CH 4 /d) averaged 30, 18, and 28% for RC, GF, and SF 6 , respectively.
Because of diurnal changes in CH 4 emission over the course of each day, sampling for less than 24 h is not appropriate for estimates of daily rate of CH 4 emission. When these conditions and considerations are addressed, the SF 6 tracer technique can produce accurate CH 4 emission data from a large group of animals. In a review of CH 4 emission techniques,  Hammond et al. (2016a)  reported that, in 5 studies comparing CH 4 emissions from dairy cows obtained using RC and SF 6 (simultaneously in 2 studies), measurements of CH 4 emission were not significantly different in 4 studies and were different in 1 study (422 vs. 469 g/d). Detailed guidelines for using the SF 6 technique were published by an international panel of experts  (Berndt et al. , 2014).
Relationship of DMI with CH 4 Emission and Prediction of DMI Dry matter intake is an important factor in enteric CH 4 prediction models. Models predicting DMI can be used in conjunction with emission factors to estimate enteric CH 4 emissions in a Tier 2 approach (which is based on country-specific emission factors and other data). Appuhamy et al. (2016)  evaluated 40 prediction equations using data that included measured DMI and feed quality attributes. The best performing models in each region (North America, Europe, and Australia and New Zealand) were then re-evaluated using predicted DMI and compared with estimates that used measured DMI. Appuhamy et al. (2016)  reported that models using estimated DMI predicted enteric CH 4 emissions as accurately as the measured data if DMI could be estimated with reasonable accuracy.
Thus, enteric CH 4 emissions could be predicted well without DMI measurements for North America. For Europe, using estimated DMI rather than observed DMI resulted in satisfactory CH 4 emissions prediction. For Australia and New Zealand, CH 4 emissions could not be estimated well without actual DMI measurements. These differences were likely due to the models used. The DMI prediction model was developed based on North American data and may not work well with diets that have greater forage proportion, including cattle on pasture. In the GLOBAL NETWORK database of individual dairy cow data  (Niu et al. , 2018) , CH 4 prediction equations with a greater number of independent variables performed best and had lower root mean squared prediction error (RMSPE) as a percentage of the mean observed value (14.
7 to 19. 8%). However, less complex models requiring only DMI had predictive ability comparable to those of the more complex models (RMSPE = 15. 2 to 21. 4%). This indicates that DMI alone may be sufficient to predict enteric CH 4 emissions for inventory purposes (as discussed in  Hristov et al. , 2017). The coefficient of determination for the relationship of measured CH 4 emissions with DMI, however, can be highly variable and may be influenced by several factors, including CH 4 measurement technique. The relationships of measured CH 4 production and DMI (absolute or expressed on a BW basis) and NDF intake (NDFI) in the GLOBAL NETWORK dairy database  (Niu et al. , 2018)  were investigated using the MIXED and REG procedures of SAS (version 9. 4; SAS Institute Inc. , Cary, NC). Table  2  summarizes the results of these analyses.
2 (data not shown). A moderate relationship between DMI and CH 4 emissions has been established for both GF and SF 6 techniques. In a meta-analysis of dairy cow studies by  Grainger et al. (2007) , the relationship between DMI and CH 4 emission as measured by the SF 6 technique was R 2 = 0. 56 and was better than the relationship between DMI and CH 4 emission for RC (R 2 = 0. 39). The authors noted that in only 22% of the studies was the All data = all data in the GLOBAL NETWORK project dairy data set; RC = data from studies using respiration chambers only; GF = data from studies using the GreenFeed system (C-Lock Inc. , Rapid City, SD) only; SF 6 = data from studies using the sulfur hexafluoride tracer technique only. 2 DMI = dry matter intake, kg/d; NDFI = neutral-detergent fiber intake, kg/d; BW = body weight, kg; DMI (or NDFI)/BW = DMI or NDFI as % of BW; MY = milk yield, kg/d; FPCMY = 3.