dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017b,a). The engine specifications are given in Table  4. Experiments have been performed by injecting different fuels, alternatively, inside the cylinders. Pure Diesel fuel data were used as a reference and data were acquired for all engine operating conditions (test procedure as full load and several speed values, every 200-rpm in-between 1200 and 2400 rpm. ). Afterward the engine was fueled with B7 and B20 biodiesels for the same operating conditions. Table  5  below displays some of the physicochemical properties of fuels tested in the present study. After every testing session the engine was fueled with pure Diesel with the purpose of purging the fuel lines and injection pump of possible gum deposits. For each investigated regime, two hundred consecutive cycles were acquired in a repetitive procedure at least five to ten times.
The engine global performance parameters were registered and then averaged and corrected according to standard correction procedures considering the values of ambient parameters. The ignition delay τ id was determined experimentally based on the averaged needle lift and heat release rate curves. This was done by computing the time interval in CAD between the start of injection (SOI) event when the needle lift jumps over 5 µm and the first positive value of the rate of heat release (RHOR) meaning the start of combustion (SOC) event (Fig. 1 )  (Miron et al. , 2020). The SOC was also determined by using a statistical method to evaluate different order derivatives of pressure and rate of heat release by smoothing the raw data on a relevant crank angle interval (namely [-7, 1] CAD) selected after the experimental data acquisition.
Results The in cylinder pressure for pure diesel fuel and biodiesel B20 shown in Figs. 12 and 13, the needle lift shown in Fig. 14  and the rate of heat release (as a function of in cylinder pressure) shown in Fig. 15  were computed as a mean of 200 consecutive cycles for pure Diesel fuel, for all tested engine speeds. Similarly, to pure Diesel fuel, data values were attained for B7 and B20 biodiesel and have been used to find the ignition delay values for all tested fuels on all engine operating conditions. Concerning the performance characteristics Figs. 16 and 17 show the dependence of brake power and brake specific fuel    consumption (BSFC) on engine speed at full load condition. It can be noticed that the usage of B7 and B20 provides similar engine performance as for Diesel fuel. A slight degradation in the engine efficiency with an average value of 2% for B7 and of 5% for B20 is registered in the BSFC variation for 2400 RPM.
27, 28 The high oxidation state of vanadium make the NSs absorbing mainly in the violet region, consequently displaying the complementary color in the yellow region (Fig. 2a ). From the UV-visible spectroscopy analysis, it seems that the colloidal solutions synthesized at 6000 Hz and 7000 Hz were the ones that absorbs the most in the UV-visible region meaning consequently that they contain the largest amount of NSs (Fig. 2a ). In order to be quantitative, the vanadium concentration contained in each colloidal solution was determined by Atomic Emission Spectroscopy (AES). The data were plotted in Fig. 2b. By tting the data, the optimal repetition rate producing the highest vanadium concentration was determined to be around 6652 Hz AE 290 Hz. By taking the reciprocal value of this repetition rate, the cavitation bubble lifetime can be estimated around 0.
The electrochemical impedance spectroscopy was performed in a double-walled electrochemical cell fabricated from Pyrex glass (volume capacity of 5 mL) fitted with a conventional three electrode system: a modified gold electrode as working electrode (0. 19 cm 2 active area defined by an O-ring seal), a platinum counter-electrode and a saturated calomel electrode (SCE) used as a reference electrode. Impedance spectroscopy experiments were carried out at room temperature by using an "Autolab PGSTAT 302 N" impedance analyzer driven by analysis system software (FRA2) adapted to impedance measurements.
Nyquist diagrams for each lanthanide concentration were recorded. The equivalent circuit parameters for the electrolyte interface of the functionalized surface of Au/bis-N-MPyC were directly deduced from the experimental data by applying the software FRA2, after testing several models of equivalent circuit. The impedimetric responses of bis-N-MPyC modified electrodes to the lanthanide ions Gd 3+ , Eu 3+ , Tb 3+ and Dy 3+ were investigated.
Determination of the Coverage of Modified Gold Wafers The coverage (θ) of the gold surface by functionalized cyclam film was deduced from the variation of the real part of the impedance (Zr) with respect to the inverse of the square root of the sinusoidal excitation pulsation (ω -1 2 ). Figure  5  shows such data for the three electrodes: gold, Au/bis-N-MPyC and Au/bis-N-MPyC-Gd 3+. The extrapolation of the linear zone at high frequencies corresponds to the resistive part of the impedance. The coverages of 90. 9% and 93. 7% for the Au/bis-N-MPyC and Au/bis-N-MPyC-Gd 3+ electrodes were calculated from the following relationship:   Nyquist diagrams were fitted with several models of electrical circuits using the data processing software FRA2 of the Autolab instrument until a satisfactory fit to the experimental data was reached.
This proved the formation of a coordination complex between the Gd 3+ ions and bis-N-MPyC molecules. Nyquist diagrams were fitted with several models of electrical circuits using the data processing software FRA2 of the Autolab instrument until a satisfactory fit to the experimental data was reached. The equivalent electrical circuit presented in Figure  7  fitted at best all data of the Nyquist diagram of the Au/bis-N-MPyC electrode (Figure  6b ) with good accuracy (chi-square of the order of 10 -2 ). The parameters of this equivalent electrical circuit for the electrolyte/bis-N-MPyC/Au structure are R s , R p , R f , CPE dl and CPE f , representing respectively the ohmic resistance of the bulk electrolyte; the polarization resistance of the electrolyte/film interface; the resistance of the film; the constant phase element as a non-ideal capacitor of the electrolyte/film interface and the constant phase element as a non-ideal capacitor of the film.
OFC technology is implemented and investigated for the powertrain of inland waterway vessels to help achieve zero-carbon emissions for meeting the strict emission standards of the European Union (EU) on the non-road mobile machinery. The object of this article is mainly to explore the effects of intake charge on OFC in a dual-injection SI engine fuelled with gasoline at economical oxygen-fuel ratios. One-dimensional simulation by software GT-Power is used to analyse engine performance and combustion characteristics under three injection strategies, including GDI (only using GDI), P-G (50% PFI and 50% GDI) and PFI (only using PFI). This article will contribute a theoretical basis for implementing OFC technology in ICE, especially dual-injection SI engines.
The research selected a typical engine operating condition, which is a medium load of 6 bar Brake Mean Effective Pressure (BMEP) at the engine speed of 2000 revolutions per minute (rpm). In order to clearly illustrate the approach of this research, a flow chart is depicted in Figure  4. First, experimental data of 2000 rpm-6 bar BMEP condition should be collected from a dual-injection engine testbed under CAC mode. Then, the simulation model of this research is available to be verified. After that, engine performance is to be simulated under OFC mode by replacing nitrogen with CO2, followed by exploring the effects of intake charge under three injection strategies (GDI, P-G and PFI), including varying intake temperature and OMF. To help reflect and analyse the combustion characteristics, some key parameters are introduced in this work.
Engine specifications and experimental facilities A downsized turbocharged dual-injection engine which specifications are listed in The transient cylinder pressure data is detected by piezo-electric pressure transducers (AVL-GH13Z) and a crank encoder (Kistler 2614CK1), followed by amplifying through a Kistler 5018A charge amplifier, then recorded and analysed by an AVL 641combustion analyser. A lambda meter (ETAS LA4) is used to measure the overall air-fuel ratio. The uncertainties of the measured parameters in the test are shown in Table  3  by the root mean square method provided by Holman  [50]. To reduce the test error, cylinder pressure data is recorded as an average of 200 consecutive cycles. The overall air-fuel equivalence ratio (lambda), coolant and intercooler output temperature are held constant at 1 ± 0.
The main ingredients of the concentrated phosphate slurry (CPS) samples were CaO and P 2 O 5 , even though they also contained other elements, such as SiO 2 , CO 2 , Fe, MgO, SO 3 , and Al 2 O 3. The phosphate content of phosphorite P 2 O 5 is often expressed as a percentage of bone phosphate of lime (BPL)  [37]. Table  1  summarizes the elemental concentrations of each sample. The samples have a similar composition that can be considered a representation of what is specifically mined in Morocco. Phosphorite deposit compositions may differ from one location to another. The analysis of the particle size distribution provides essential data for forecasting the slurry behavior in the transport process or its physical properties. Measurements were carried out for each sample using a Mastersizer 3000 granulometer from Malvern (Worcestershire, UK).
644 mm. The experiments were conducted at controlled shear rates in the same range as those expected in pipe flows. The experimental data of the CPS samples were obtained at room temperature. For each measurement, the suspensions were vigorously mixed in a container beforehand. A pre-shear is required to erase material memory and to have similar initial conditions for all samples. Then, after prolonged mixing (>30 s), the necessary volume was quickly poured into the outer cylinder. The inner cylinder was subsequently immersed into the slurry. The stress τ exerted on this cylinder was recorded as a function of the shear rate. γ. The rotational tests were carried out in a preset shear rate ramp mode, descending in steps, while recording the shear stress for each set shear rate. In order to allow the sample to adapt itself to each shear rate step, a duration of three seconds was maintained for each measuring point.
It was also observed that an expanded particle size distribution yielded a lower apparent viscosity for all cases. The results show that apparent viscosity can be reduced at a constant loading by up to 20%. Later, it was found that the accuracy of the pressure loss prediction relied on the accuracy of the rheological model. Despite the fact that a significant amount of rheological data for the concentrated phosphate slurry were evaluated in this study, the data did not cover the complete range of the shear rates. Hence, we are aware that some conclusions are limited to the range of the data considered in this study. These findings can be exploited for better control of phosphate dispersion in biocomposites polymeric-based systems, which allow for better control of the filler dispersity and avoid harmful aggregation.
Regarding toluene, Shulka et al. [13]  performed lowpressure pyrolysis experiments in a flow reactor, and PAHs with mass numbers up to 522 were detected with a mass spectrometer. Matsugi and Miyoshi later proposed a kinetic model  [14]  based on their theoretical calculations  [15 , 16]  to interpret the measurements for two to three ring aromatics reported in  [13]. Yuan et al. [17]  also conducted toluene pyrolysis experiments in a flow tube reactor and discussed the formation pathways of measured PAHs with a detailed kinetic model. However, the above-mentioned flow reactor pyrolysis experiments were carried out at atmospheric or subatmospheric pressures. The roles of the proposed pathways under high pressure conditions more relevant to practical combustion still need to be revealed. The goal of this work is to provide reliable speciation datasets using shock tube and gas chromatographic (GC) techniques and to develop a predictive kinetic model for benzene and toluene pyrolysis, with emphasis on detailed formation mechanisms of two-to four-ring PAH intermediates.
The guidelines to constitute a database containing, for 1800 pure fluids, more than 300,000 pseudo-experimental vapor pressure, liquid density, enthalpy of vaporization, and liquid heat capacity data are described. Such a database provides a tool for a fair assessment of the performances of equations of state to correlate (or predict) the properties of pure components. In this paper, we use it to assess the performance of four cubic equations of state (CEoSs): original Soave-Redlich-Kwong (SRK), original Peng-Robinson (PR), and the translated-consistent versions of PR (tc-PR) and RK (tc-RK). The deviations between calculated and experimental data are compared for the four CEoSs, which make it possible to discuss the influence of the α-function on the calculated vapor-liquid equilibrium properties and how the volume translation impacts the calculated volumetric properties.
| INTRODUCTION Since the appearance of the first process flowsheet simulators, different physical-property databanks as well as many thermodynamic models have been incorporated in their brochures. Developers often claim that their models perform well in this or that region, can be reliably extrapolated, and are suitable for certain systems or applications. However, it is difficult to have a fair overview of the capabilities and limitations of a given thermodynamic model. Thus, it would be pertinent to define a common basis for comparison. At this stage, the following questions appear: "Which type of data are available for many fluids?" "Which type of properties are considered as essential?" "How could a thermodynamic model be evaluated quantitatively?" These questions have been addressed by Jaubert et al.
1  in their work on the development of a high-quality reference database containing binary-system data for the cross-comparison of thermodynamic models and the assessment of their accuracies. With such a database, it is possible to determine how well a thermodynamic model predicts the phase-equilibrium properties of binary mixtures (vapor-liquid equilibrium [VLE], liquid-liquid equilibrium, vapor-liquid-liquid equilibrium, property change on mixing, 2 azeotropic and critical points) according to the associating character (AC) of their components. Moreover, it could be used to assess the impact of the selected mixing rules and the number of parameters included within. Models such as the Peng-Robinson (PR) equation of state (EoS) armed with classical or advanced mixing rules, as well as the PC-SAFT EoS with classical mixing rules, no induced association scheme, and no regressed BIPs have been graded following the proposed procedure recently.
To reach this objective, this article presents the guidelines that were used for constituting a reference database. The data collected for the proposed database are a fraction of those available in the database commercialized by the Design Institute for Physical Properties (DIPPR) and includes experimental vapor pressure, liquid density, enthalpy of vaporization, liquid heat capacity, as well as critical temperature, pressure, and volume data for 1800 pure fluids. It is believed that such a database, which contains more than 300,000 experimental data points, is the perfect tool to identify the strengths and weaknesses of a given thermodynamic model for pure compounds. Additionally, the deviations between the calculated and experimental data are determined for the >300,000 data points available in the proposed database for the original Soave-Redlich-Kwong (SRK),  7  original PR,  8  tc-RK, and tc-PR EoSs  9  in order to assess their accuracies.
This article is organized into two sections. In the first one, a detailed description of how the database was built is presented. This includes the choice of key properties, molecules, and temperature ranges, resulting in more than 300,000 data points, which will be used to assess the accuracy of any thermodynamic model for pure components. In the following section, the four CEoSs benchmarked in this study are briefly introduced. Their capacity to reproduce experimental data is discussed then. Because it is often said that associating molecules cannot be properly modeled from EoSs that are not based on a theory accounting specifically for association bonding, the discussion is oriented toward the relation between the AC of the studied molecules and the capacity of EoSs to model their behavior.
| DESCRIPTION OF THE GUIDELINES TO CONSTITUTE A REFERENCE DATABASE OF PURE COMPONENTS The objective of this section is to explain our scientific approach and answer the following questions: 1. How can the existing commercial databases help us to reach our objective (i. e. , to define a reference database for evaluating the performance of thermodynamic models to correlate (or predict) the properties of pure components)? 2. What is the minimum list of properties that must be available for a pure species to be included in the reference database?.
Finally, the reproduction of pure-component critical properties (especially T c , P c , and to a lesser extent v c ) is essential for an accurate representation of binary critical lines  12  (made up of binary critical points). The ideal scenario would be to assess model performances considering molecules for which all the properties of interest (P sat , ρ sat liq , Δ vap H, and c sat P,liq ) are available. Nevertheless, due to a lack of experimental data, such a choice would drastically limit the number of pure components that could be embedded in the pure-component reference database we plan to build. In consequence, it is necessary to define the indispensable properties that should be available for any molecule included in the proposed database. Such properties are vapor pressure and liquid density; the reasons of this choice are as follows: 1.
Satisfactory accuracy on the reproduction of P sat and ρ sat liq is the minimum requirement for the industry and computer-aided product design developers. 2. In the case of CEoSs, to make an α-function consistent, the corresponding parameters must be fitted to P sat data, at least. 10  Moreover, if the volume-translation concept is applied, at least one data point of the ρ sat liq T ð Þ curve is required. 3. In the case of SAFT-type EoS, Ramírez-Vélez et al. 11  concluded that it is necessary and sufficient to fit the PC-SAFT EoS parameters to both P sat and ρ sat liq data. In summary, molecules included in the database should have accurate data for P sat and ρ sat liq ; when available, Δ vap H and c sat P,liq must also be included. It is worth noting that the EoS can predict only residual properties.
| Assessment of correlation accuracy In the previous section, it has been stated which experimental properties should be available in order to incorporate a pure component in the proposed database. However, even if temperature-dependent correlations are available in the DIPPR database, it is necessary to guarantee their reliability. This is a key issue since, as Agarwal et al. 6  wrote: "one of the most heartbreaking experiences for a thermodynamicist is to watch someone spending a large effort refining a model with far more precision than the basic data allows. ".
| Presentation of the three types of errors In order to evaluate whether for a given molecule this or that property correlation is accurate, a strategy inspired by the work of Piña-Martinez et al. 10  has been followed. For each correlation, three types of error were defined: • Type I error: error reported in the DIPPR. Such an error is set by the DIPPR developers to the maximum uncertainty among the data series used to determine the coefficients of a correlation. • Type II error: actual average uncertainty. Such an average error (not reported in the DIPPR database) is calculated from the knowledge of the uncertainty associated with each data series that was used to determine the coefficients of a correlation. For a given property and a given pure fluid, it is determined as Type II error ¼ P n series j¼1 N data,j Â Error j P nseries j¼1 N data,j ð1Þ where N data,j is the number of experimental data points available in the series j, and Error j is the corresponding experimental error (as reported in the DIPPR database).
• Type III error: mean absolute percentage error (MAPE) over 50 points between pseudo-experimental data (generated by a DIPPR correlation) and calculated values returned by the generalized version of the tc-PR model (the acronym of which is gen-tc-PR). It is evaluated according to Equation (2): Type III error ¼ 100 N T X NT i¼1 X i,exp À X i,calc X i,exp ð2Þ X i,exp and X i,calc correspond to the pseudo-experimental and calculated values of the property X. In this article, N T = 50 (the 50 points are evenly distributed between T min and T max that define the temperature range in which the correlation is valid). Type III error was introduced because, from our experience with the DIPPR database, we believe that type-I and type-II errors are often overestimated. Consequently, many correlations would be disregarded if only those for which type-I or type-II errors are less than 5% would be kept.
| Correlation acceptance tests A correlation is declared as acceptable if it passes both tests described in this section. Test 1: The type III error of a given property correlation X X∈ P sat , ρ sat liq , Δ vap H, c sat P,liq n o is less than the threshold of acceptance (noted TH X ) defined as follows: TH test1 X ¼ MAPE X þ 6SD X ð5Þ where MAPE X (for mean average percent error) and SD X (standard deviation) of property X are obtained from Table  1. This test aims at detecting possibly typing errors in the coefficients of the correlations or in the values of uncertainties reported in the DIPPR database. It also aims at disregarding correlations that lead to totally unexpected values so that the properties of such a component cannot be correlated by a classical EoS (this usually happens for a few inorganic molecules).
| Molecules to be incorporated in the database The DIPPR provides constant properties and a set of T-dependent correlations for 2230 molecules. Among these (see filter 2 below), only the ones for which both key properties (P sat , ρ sat liq ) are available were kept. In addition, all molecules requiring a specific treatment (see filter 1 below) were disregarded. Therefore, a molecule was declared as suitable if it remained after application of both filters described below. • it is banned by the DIPPR due to ambiguous data, self-accelerating decomposition, or instability; • it is a quantum fluid (e. g. , hydrogen, helium, etc. ). After this filter, 47 molecules were removed and 2183 remained. Filter 2: A molecule is removed if one correlation for P sat (T) or Note that after this last filter, 385 molecules should be removed actually.
| Associating character The thermodynamic behavior of molecules showing AC, that is, involved in a hydrogen bond, is considered by most of developers (and SAFT EoS authors in particular) as more complicated than that of non-associating molecules. Thus, capturing the behavior of associating molecules appears as a challenge. As a consequence, how a model performs with non-selfassociating (NSA) and self-associating (SA) molecules gives a deeper insight into its performance. In the case of binary systems, a major feature of the database proposed by Jaubert et al. 1  is that it provides a binary system classification according to the AC of their components. It is thus possible to have a measure of the complexity of modeling the thermodynamic properties of mixtures depending on the nature and strength of the association phenomena involved in the system.