dataesr/scientific-paragraphs-categorization huggingface dataset:
Here the "RBF" kernel was chosen as it is adapted to nonlinear systems. Polymers 2022, 14, x FOR PEER REVIEW 9 of 23 UHMWPE, flow rate, screw rotation speed and maximal imposed temperature), and only around 27 data, each corresponding to an extrusion configuration. To find a law linking the outputs to these inputs, it is consequently necessary to use algorithms able to perform with few data. As regression methods are well adapted to this case, Support Vector Machine Regression (SVR) and Sparsed Proper Generalized Decomposition (sPGD), two regression methods, have been tested for those data. Their specific modes of operation are described hereafter. The process is as follows: the dataset is first randomly divided into training and test groups. Then, the training inputs and outputs are implemented in the algorithm, which, depending on the method, will "learn" from these data, creating a model linking the inputs to the outputs.
Comparison of Estimated and Measured Viscosities The viscosities of the extruded materials deduced from the pressure measured at the die were compared to the rheometer measurements. The viscosity curves obtained from die pressure measurements are compared to data calculated from rheometer experiments in Figure  6A  only for the samples resulting from HDPE 390 • C extrusion. Figure  6B  compares the zero shear viscosities obtained for all samples with the two different methods. A Carreau-Yasuda model was thus used to fit the experimental viscosity curves, which validate the use of such a model for the degraded PE. With this type of representation, the closer the points on the x = y line are, the closer the values of the two methods for the same sample are. The samples from extrusions performed at different temperatures or with the UHMWPE give similar results.
Conclusions HDPE and UHMWPE were degraded by twin-screw extrusion under different high temperatures (320 < T • C < 420) and for different process conditions (flow rate and screw rotation speed), leading to numerous different extrusion configurations. Several parameters were measured for each configuration, creating a dataset with four different inputs, five outputs, and thirty-eight data. The shear viscosity curves of the extruded materials were estimated from the measured die pressure and temperature. Their comparison with frequency sweep measurements showed that despite the numerous simplifications, the results were accurate. This fact shows that this method can be used to rapidly obtain an approximation of the final zeroshear viscosity of extruded materials. Two methods were tested to estimate the molecular weight of extruded polyethylene.
The Ludovic ® (SC-Consultants, Saint-Etienne, France) twin-screw extrusion simulation software was used as a classical model of the extrusion experiments. Since the degradation mechanisms occurring in the extruder are unknown, the simulation was performed considering the viscosities of the raw materials, which led to overestimated pressures and temperatures. Consequently, SVR and sPGD Machine-Learning methods were applied to the dataset and succeeded in modelling the extrusions' torque, engine power, die pressure, and die and centre temperatures. They also gave good results for the predictions of Mw. Mn has also been successfully predicted but with more inaccuracies, probably caused by its method of determination. Besides, whereas the SVR method gave inaccurate results for zero-shear viscosity modelling, sPGD's results were more acceptable.
37 For phase formation and phase change processes, the ability to conne electrochemical reactions to small volumes allows the investigation of a few or even single events. 54, 55 In this context, the use of SECCM in hopping mode,  56  where the SECCM meniscus is landed at a series of spots on a surface, is particularly powerful, as it is possible to build up large datasets with the same, or different, experimental conditions applied to each spot. In this work, we combine SECCM with the surface sensitivity of IRM to investigate phase formation in tiny droplet cells at an ITO electrode surface. First, we employ this new hybrid technique to visualise and monitor the evolution of the SECCM meniscus dimension during electrochemical actuation. Subtle variations in the meniscus surface area are detected when faradaic current ows at the ITO/ electrolyte interface.
Acknowledgements We acknowledge use of the  Spectroscopy Research Technology Platform, University of Warwick. D. V. , P. C. , F. K. and P. R. U. acknowledge support from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie MSCA-ITN Single-Entity Nanoelectrochemistry , SENTINEL [812398]. P. M. , I. J. M. and P. R. U. acknowledge an  EPSRC Programme Grant  (grant  EP/R018820/1 ) which funds the Crystallisation in the  Real World consortium. P. R. U. thanks the  Royal Society  for a  Wolfson Research Merit Award.
controlled by LabVIEW soware (release 2019, National Instruments). Image recording was triggered from the WEC-SPM platform, by outputting a TTL Paper Faraday Discussions signal to the camera input trigger port. The camera output trigger's pulse train was also fed to the FPGA card, and recorded concurrently with the electro- chemical measurements, allowing synchronous analysis and presentation of SECCM and IRM data. The entire equipment (microscope, stage, positioners, and camera) was enclosed in a Faraday cage, lined with thermal isolation panels, and supported on an active optical table (CleanBench, TMC, USA). Electrochemical data processing and plotting was carried out using the MATLAB (release R2019b, The MathWorks, Inc. , USA) and OriginPro (release 2021, OriginLab, USA) soware packages.
6-2. 0 methyl substitution per glucopyranose unit) as a function of pH and temperature. The corresponding thermodynamic data of encapsulation were evaluated and analyzed. Two analytical methods for extracting the data were compared: a first step-by-step approach, and a global one. These two procedures revealed to be complementary for optimizing the determination of the studied physicochemical parameters, leading to the identification of the optimal conditions of binding and dissociation, and to evaluate the potential of CD derivatives for the effective extraction of phenolic acids.
In each DOSY experiment, a series of 16 spectra with 20 K points were collected, with 16 scans. The pulse gradients have been incremented in 16 steps from 2% to 98% of the maximum gradient strength in a linear ramp. Diffusion times (D) and gradient pulse durations (d) were optimized for each experiment in order to achieve a 95% decrease in resonance intensity with respect to the guest molecule; typically, d has been set between 5 and 1. 5 ms and D between 400 and 75 ms. After Fourier transformation, phase and baseline correction, the diffusion dimension of the 2D-DOSY spectra was processed by means of MestRenova software (version 11. 0. 2). The diffusion constants were calculated by Peak Heights Fit method with 128 points in diffusion dimension. Among the DOSY measurements, only two were performed in duplicates, and the uncertainty was estimated to be between 2% and 4%.
The time lapse between the increase and drop of flue gas temperature defines a thermal cycle. The pellets mass flow rate is deduced from the calibration of the feed screw, according to the angular position of the screw. All sensors are connected to a data acquisition system with a dedicated code developed under Labview software. ature is reached, the pellet supply stops. Thus, a long stop of the pellet supply (12 min) can be observed in Figure  2. The pellet supply disruptions induce a drop in the burnt gas temperature in the combustion chamber with a temporary delay. The time lapse between the increase and drop of flue gas temperature defines a thermal cycle. The pellets mass flow rate is deduced from the calibration of the feed screw, according to the angular position of the screw. All sensors are connected to a data acquisition system with a dedicated code developed under Labview software.
Results and Discussion The experimental results are discussed to characterize the boiler operation. They are then compared to the simulations. In order to validate the model, measurements of the flue gas temperature profiles in the combustion chamber and at the heat exchanger outlet as well as measurements of water temperature at boiler outlet are carried out. The dynamic model input data are the experimental flue gas temperature in the burner (T exp fg,bur ), the experimental water temperature at the boiler inlet (T From the analysis of the flue gas temperature profiles in the combustion chamber (Figure  10 ), sudden and fast temperature changes occur during the boiler start-up due to the uncontrolled combustion of a large mass of pellets during this step. Before the combustion start, pellets are heated during several minutes (about 15 min) with an air heater.
Electrochemical Procedures Electrochemical equipment and procedures were detailed in a preceding publication. [26] Briefly, measurements were performed using a conventional 3-electrode setup and a potentiostat equipped with a fast electronic compensation of the ohmic drop. [27, 28] Because DES are generally less conductive than a molecular solvent, considering the ohmic drop interference in the treatment of the data is an absolute requirement when working in a DES even at low scan rate. [29] We used a home-made potentiostat equipped with a positive feedback electronic compensation following an adder scheme. [27, 28, 26] This electronic scheme adds noise in the signal, extreme peak noise signals were numerically removed from the curves shown in the Figures  1  and 2  to get a full scale of the useful signal.
Acknowledgments Dr. Jean-François Bergamini  (ISCR) is warmly thanked for his help in the building of the potentiostat with direct electronic ohmic drop compensation. China Scholarship council (CSC) of the People's Republic of China  is thanked for a grant to FZ. Dr. C. Amatore  and  Dr. I. Svir  ( CNRS, ENS-Paris ) are warmly thanked for making us available a preliminary version of the KISSA 1D package.
Differently Defined Karlovitz Numbers Let us, following common practice  [2] [3] [4] , define Kolmogorov length, time, and velocity scales as follows 𝜂 𝐾 = (𝜈 3 𝜀 ⁄ ) 1 4 ⁄ , 𝜏 𝐾 = (𝜈 𝜀 ⁄ ) 1 2 ⁄ , 𝑢 𝐾 = (𝜈𝜀) 1 4 ⁄. (2) Henceforth, all turbulence characteristics are evaluated in unburned gas upstream of a flame if the opposite is not specified. In homogeneous isotropic turbulence  [51] , 𝜀 = 𝐶 𝜀 𝑢 ′ 3 𝐿 = 15𝜈 ( 𝑢 ′ 𝜆 ) 2. (3) Here,   is a constant, which is often assumed to be of unity order, but its exact value is not known a priori. In the simplest case of homogeneous isotropic turbulence, an increase in   with decreasing Reynolds number   ≡ ′  ⁄ is well documented in Direct Numerical Simulation (DNS) studies  [52, 53] , with the effect being strongly pronounced at   < 150. These DNS data also indicate that   tends to a finite constant value  ,∞ (about 0.
Before introducing the sample, the oven was preheated to a desired temperature. The time when the door of the oven was closed was defined as the starting time (t = 0) of our experiment. Changes in length L(t) of the filament as a function of time t were monitored with a camera placed outside the oven. L(t) was deduced from the taken images using ImageJ software  [41]. From the measurement of L(t), we deduced the time dependence of the Hencky strain ε(t) according to ε(t) = ln[L(t)/L(0)], with L(0) being its initial length at t = 0. With this approach, we could determine ε(t) with an uncertainty of ca. ±0. 003 at a time resolution of ca. ±10 s. Since the typical cross-sectional area of the filament is of the order of A ∼ 10 -9 m 2 and the thermal diffusivity D T of polystyrene is of the order of D T ∼ 10 -7 m 2 s -1 for the studied temperature range  [42] , the typical diffusion time t Diff of temperature across the sample is t Diff ∼ 10 -2 s, which is much shorter than our time resolution (of the order of seconds).
The present recommendations do not cover complex phase diagrams or solubility in supercritical fluids. Ideally, all SLE measurement results should be reported in sufficient detail to allow them to be critically assessedrecommendations on reporting phase-equilibrium data are given in another IUPAC project  [7]. It is always useful to estimate the expected phase diagram type for a studied system. This estimate should include possible metastability and formation of solvates, as well as characterization of the nature of the solid phase. If the solid phase is unknown, the full material balance should be reported, especially for systems with more than two components and/or involving various ionic species (e. g. , observed in mixtures with ionic liquids). To recommend reference systems for the evaluation of SLE measurements, the NIST/TRC SOURCE database available in ThermoData Engine (SRD 103b)  [8]  was scanned for binary mixtures with the largest amount of SLE data.
Literature sources, accepted solubility data, smoothing equations, and uncertainty analysis are given for each mixture listed below. Data rejection was done either on the basis of the smart rejection procedure implemented in ThermoData Engine  [8]  or anomalies or flaws discovered in specific publications. A description of the uncertainty assessment procedure used for the studied systems is detailed in Part 1 of these Technical Reports  [10]. Both temperature and composition representations of the SLE data are provided for convenience, except for the low-solubility category, where only compositions as a function of temperature are given, as most experiments are typically done in this mode. Smoothing equations have also been included in the present report to support method verification at any point within the experimental conditions covered for the proposed systems.
Urea (carbonic diamide) + water system One of the best-studied systems with consistent experimental SLE data in this category is urea + water. The selected literature sources associated with the SLE data for this system are listed in Supplement 2A. It should be noted that there were also a few series of works on ternary SLE diagrams at fixed temperatures, where urea was one of the solutes and binary SLE data for urea + water were reported  [11] [12] [13] [14] ; however, the binary mixture was not the primary focus of the works, and the solubilities were similar within each series. To avoid any bias of the generated recommendation toward the data from those laboratories, only representative cases were included in the assessment. Most of the selected sources are consistent, with the prominent exception of all data points from Refs.
[14] [15] [16]  as well as one data point from Ref. [17]  and two points from Ref. [12]  (Figure  1 ). The value in Ref. [16]  is accompanied by the sign "+," which probably indicates that the actual solubility is above that reported value. Some researchers (e. g. ,  [12] ) have claimed three different states of solid urea. These claims are doubtful given that reliable heat capacity measurements, by adiabatic and triple thermal bridge calorimetries, found no solid-to-solid phase transitions up to the melting point of urea  [18] [19] [20]. The selected SLE data were smoothed with the NRTL (Non-Random Two-Liquid) excess Gibbs energy model  [21]  by the ThermoData Engine software  [8]. For a binary mixture, this model gives the following expression for the excess molar Gibbs energy G E : G E /(RT) = x 1 x 2 [c 1 exp(-αc 1 )/(x 1 + x 2 exp(-αc 1 )) + c 2 exp(-αc 2 )/(x 2 + x 1 exp(-αc 2 ))] (1) with the temperature-dependent parameters c i defined as follows: c i = A i + B i (T/K) -1 + C i ln(T/K) + D i (T/K), (2) where α is an empirical fitting parameter called the "non-randomness parameter"; A i through D i are the empirical fitting parameters for component i; T is the temperature in K; x i is the mole fraction of component i; and R is the molar gas constant.
Since T tp , Δ fus H m , and Δ fus C p,m were assigned fixed values during the NRTL fitting, any inaccuracy in them was absorbed by the fitting parameters α and A i through D i , so their uncertainties were not needed for the purposes of the present project and thus were not evaluated. The performance of the NRTL model relative to the selected experimental data is shown in Figure  1  and as deviation plots in Supplement 2B. Although the NRTL model was based only on the SLE data, available experimental VLE pressures for the mixture (given in Supplement 2B) are also in fair agreement with it, which can serve as additional verification for the selected SLE data and model. Smoothed solubility data are listed in Table  2. The eutectic point, according to the model, is at T = (262. 30 ± 0. 29) K and x 1 = 0.
Component i Parameters of eq. (  9 ) Phase-transition parameters for eqs. (  3 ) or (10) a The performance of the Wilson model with the parameters from Table  3  relative to the selected experimental data is shown in Figure  2  and as deviation plots in Supplement 2E. Since the model was obtained solely to fit the selected SLE data, it cannot be used for deriving other properties (e. g. , excess enthalpy or vapor pressures). Additional verification of the selected SLE data was done by simultaneously fitting the Wilson model to the available literature SLE, excess enthalpy, and vapor pressure data between 260 and 323 K. The results of that more generic fit are provided in Supplement 2E, where it is clearly seen that the selected SLE data are in fair agreement with other properties. It must be emphasized that the parameters from Table  S2  are not recommended for SLE calculations, since their only purpose is to prove consistency with other properties.
Potassium chloride + water system The solubility of potassium chloride in water was discussed in SDS-47  [31]  (hereafter, the abbreviation SDS-X-Y is used for "Solubility Data Series, Volume X, Part Y"; if Volume X does not have separate parts, its abbreviation is contracted to SDS-X) and in Krumgalz's review  [32]. Additional data sources available from the NIST/TRC Source database  [8]  (Supplement 2F) do not affect their evaluations, since they are consistent with the previous data except for two points from Refs. [33, 34]. The experimental data cited in Refs. [31, 32]  (excluding the points consistently rejected in both reviews) as well as all additional data from Supplement 2F are shown in Figure  3. The two solubility evaluations  [31, 32]  are comparable, but the SDS one was selected here due to its better performance at lower temperatures, which is of interest for the current project.
The most recent publication Ref. [37]  cites those and re-applies the Pitzer model. We additionally verified the consistency of the solubility data with other measured thermodynamic properties with the Pitzer model using the parameters and procedures implemented in the PHREEQC program  [38, 39]  designed for aqueous geochemical calculations, as shown in Figure  S6  in Supplement 2G. It should be noted that the comparison is limited to T/K ≤ 523. m 1 /(mol kg -1 ) x 1 T SLE /K U(T SLE )/K T SLE /K m 1 /(mol kg -1 ) x 1 U(x.
Sodium chloride + water system The solubility of sodium chloride (1) in water (2) has been evaluated in NIST Technical Note 1387  [40] , in SDS-47  [31] , by Krumgalz  [32] , and discussed briefly by one of us  [41]. The last included some key experimental details, such as the correct drying of solid sodium chloride, that are often overlooked. It should be noted that a hydrate NaCl⋅2H 2 O is formed at T < 273. 15 K: the peritectic point is T = 273. 15 K and x 1 = 0. 0990 according to SDS-47  [31]. However, it appears that there were not enough consistent experimental data to develop a Additional data sources available from the NIST/TRC Source database  [8]  for that phase diagram portion (Supplement 2H) are generally consistent with the previous data. The experimental data cited in Refs. [31, 32]  (excluding the points consistently rejected in both reviews) as well as all additional data from Supplement 2H are shown in Figure  S7  in Supplement 2H.
Thermodynamic evaluations of sodium chloride + water that corroborated the solubility data by models consistent with other property data have been done by Pitzer et al. [42] , Archer  [43] , and Krumgalz  [32]. We additionally verified the consistency of the solubility data with other measured thermodynamic properties with the Pitzer model using the parameters and procedures implemented in the PHREEQC program  [38, 39] , as shown in Figure  S9  in Supplement 2I. It should be noted that the comparison is limited to T/K ≤ 523. In contrast to the present model, the Krumgalz polynomial equation  [32]  and the Pitzer model from PHREEQC  [38, 39]  show a minimum-solubility temperature around 285 K, which may be a fitting artifact that cannot be reliably proven because of the experimental data scatter in that temperature region.
Category 3: non-aqueous systems The most studied SLE data sets for non-aqueous systems belong to aromatic hydrocarbons. One of the best studied organic systems with consistent data is naphthalene + benzene  [45] , which unfortunately was excluded from consideration in the current project due to the toxicity of benzene and restrictions on its use. A similar but less toxic system is naphthalene + toluene discussed in SDS-59  [45]  and SDS-98-3  [46]. Two additional sources  [47, 48]  were not mentioned in those reviews. The selected literature sources associated with the SLE data for this system are listed in Supplement 2K. The experimental SLE data are generally consistent; only two points from Ref. [17]  deviate noticeably from the other data (Figure  5 ). The selected SLE data were smoothed with the NRTL model given by eqs.
(  1 )-(  5 ) with the use of the ThermoData Engine software  [8]. Since there is only one experimental work at T < 247 K, naphthalene solubilities below that temperature were not included in the fit. The parameters of the resulting NRTL fit for naphthalene (1) + toluene (2) are presented in Table  8. This table also contains the melting parameters for both mixture components used in the modeling. For naphthalene, the melting parameters were adopted from Ref. [49]  (note: the selected melting temperature and the enthalpy of fusion are in excellent agreement with the recommendations of Ref. [50] ); for toluene, they were taken from Ref. [51]. Since T tp , Δ fus H m , and Δ fus C p,m were assigned fixed values during the NRTL fitting, any inaccuracy in them was absorbed by the fitting parameters α, A i , and B i , so their uncertainties were not needed for the purposes of the present project and thus were not evaluated.
The performance of the NRTL model relative to the selected experimental data is shown in Figure  5  and as deviation plots in Supplement 2L. Although the present model was developed using only the SLE data, available experimental VLE pressures for this system (shown in Supplement 2L) are in good agreement with it, which can serve as additional verification for the selected SLE data and model. Since naphthalene + aromatic hydrocarbon systems are in general nearly ideal, an additional verification became possible, namely a comparison of the naphthalene solubility profiles in the homologous solvent series: benzene, toluene, and ethylbenzene. The comparison over the range of validity of the developed NRTL model is shown in Figure  S12  in Supplement 2L, where all the experimental data for the three mixtures are in excellent agreement.