dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical model The identification of overall performance was divided on two steps; the first one consisted on determining the heat thermal coefficient "U" and "ΔP' on a single pattern and the second one was the calculation of the heat exchange duty "q HX " on the complete HX. To accomplish the first step, a numerical model was created using the commercial CFD software StarCCM+. After identification of representative pattern, boundaries conditions, physical model and mesh criteria this model was validated by comparing the results obtained on a metallic geometry (M com ) with the experimental data obtained by Wang  [15].
The thermo-physical properties were set as constant: density of 1. 18 kg. m -3 , thermal capacity of 1007. 62 J. Kg -1 , dynamic viscosity of 1. 85•10 -5 Pa. s and thermal conductivity was equal to 0. 026 W. m -1. K -1. The air heat exchange coefficient (U a ) was computed after solving the Navier-Stokes equations with these boundary"s conditions, physicals properties, and turbulence models using the commercial CFD software StarCCM+.
The concentration of siloxane at the outlet of the tube was monitored versus time using a Flame Ionization Detector (FID) from a Perkin Elmer GC "Autosystem". The signal was converted into breakthrough curve using Total Chrom software. The dynamic adsorption experiment was performed until complete saturation of sorbent bed as reported in our previous work  [28]. The adsorption capacity at equilibrium defined as the quantity of adsorbed siloxane (in milligram) at saturation per gram of sorbent was calculated from the breakthrough curve according to the following equation: = (. - ( where Q is the inlet flow (mL min -1 ), m the adsorbent mass (g), C0 the siloxane initial concentration (mg mL -1 ), C(t) the siloxane outlet concentration at given time t and the bed exhaustion time (min). Table  2.
ACKNOWLEDGMENTS The authors thank the  Brazilian agencies CAPES (Science without Borders Program) ,  CNPq  (Grant N o  308225/2018-0 ),  FAPEMIG ,  FAPERJ  (Grant N o  E-02/202. 961/2017 )for the financial support, the  Brazilian Society of Mining  and Metallurgy for the niobium oxide samples, and the  Military Institute of Engineering and IRCELyon/CNRS  for physical infrastructure. This work was also supported by  Long-term development plan UHK.
Introduction Oxy-fuel combustion is currently known to have significant advantages over conventional combustion concepts where fossil fuel is oxidized with air, affecting the environment in terms of emission increase of carbon dioxide (CO 2 ), NO x formation through the oxidation at high temperature of nitrogen (N 2 ) in air. According to the Emission Database for Global Atmospheric Research  [1] , global emission of CO 2 increased to 35. 8 billion tones in 2016, compared to 22. 06 billion tones registered two decades ago. The concept of carbon capture and storage (CCS) is actually one accepted strategy towards cleaner combustion of fossil fuels. Three main approaches are currently emerging, namely, pre-combustion capture, post-combustion capture, and during-combustion capture or Oxy-fuel combustion (see in  [2] ).
Numerical Implementation The reported simulations have been carried out by means of the open source code OpenFOAM  [60] , in which the hybrid ESF/FPV was implemented  [4]  and extended in this study to be coupled with LES turbulence models. The solution procedure is carried out through various numerical steps. According to  [4] , once the time loop starts, N number of stochastic Fields for the two controlling variables are created, which means 2*N new fields are created for both scalars: N fields for the mixture fraction, f : f n with n = {1,. , N} and N fields for the progress variable, PV:PV n with n = {1,. , N}. The random Wiener term is computed, at the same stage, separately for each individual stochastic field in all spatial directions: dW n j with j = {1, 2, 3} allowing the random procedure of these created fields for both chemical table controlling variables.
Acknowledgments: The authors of this work gratefully acknowledge the financial support by the  European Union 's  Horizon 2020 research and innovation program  [CLEAN-Gas] with grant number  643 , 134 , also the financial support by the  DFG (German Research Council) CRC/Transregio 150 Turbulent , chemically reactive, multi-phase flows near walls. All the numerical simulations were carried out on the  Hessen High Performance Computer (HHLR) at the Technical University of Darmstadt.
Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R 2 = 0. 9965) and the lowest root mean square error (RMSE = 0. 0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with.
The MLP method was optimized using either Levenberg-Marquardt (LMA) or Bayesian Regularization (BR) techniques. The results obtained using three methods (MLP-LMA, MLP-BR, GEP) are then compared with results calculated using thermodynamic models based on Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) equation of states. Statistical indicators, including the determination coefficient (R 2 ) and Root Mean Square Error (RMSE), are used to evaluate the accuracy of the methods, in addition to graphical assessments using cross plots and bar plots. In the end, outliers detection is performed to test and analyze the validity of the best-developed model and quantify the doubtful experimental points from the database. It is worth noting that the two backpropagation-based learning algorithms (LMA and BR) that were employed in the training process of MLP, alongside the explicit correlations established to predict the CO 2 solubility in ILs, make the current work different from previously published works in the literature.
Comparison with PR and SRK EoS Results The results obtained by the MLP-LMA model were compared with values calculated using thermodynamic models based on PR and SRK EoS. Tables  4  and 5  provide the CO 2 solubility and thermodynamic properties respectively. Furthermore, Table  6  summarizes a comparison, where the RMSEs and R 2 of the best-proposed model, i. e. , MLP-LMA, and PR and SRK EoS were mentioned. To perform this comparison, two samples of ILs that had not been included in the training phase of the intelligent models were considered. These samples included [bmim][Tf2N] and [bmim][PF6] ILs. Further description is given in Tables  4  and 5. All three models (MLP-LMA, and the PR and SRK EoS) gave accurate descriptions with R 2 above 0. 98. For the two ILs, the MLP-LMA model had a higher coefficient of determination and lower RMSE compared to the PR and SRK EoS, in the following order of reliability: MLP-LMA > SRK > PR in the case of  Cross plots in Figure  3  were used to compare the models' predictions against the experimental data further.
In these plots, the predicted values using the three methods were represented versus the previously established experimental data for the solubility of CO2 in ILs. The criterion of precision for the different models in typical cross plots can be indicated by the compressed gathering of data points cloud nearby the 45° line, otherwise, all points falling on the unit slope would correspond to a perfect model. The accumulation of data cloud nearby the 45° line was attributed to the elevated value of R 2 close to the unit and to the low value of RMSE near zero. It can be observed in Figure  3 , that the MLP-LMA, MLP-BR, and GEP models exhibited distributions of predictions near the unit slope line for both training and test datasets, confirming their high accuracy in estimating the solubility of CO2 in the considered ionic liquids.
Acknowledgments: We acknowledge the support of the  German Research Foundation (DFG)  and the  Bauhaus-Universität Weimar  within the  Open-Access Publishing Programme. Furthermore , we acknowledge the  Research Council of Norway  and the industry partners,  ConocoPhillips Skandinavia AS, Aker BP ASA, Vår Energi AS, Equinor ASA, Neptune Energy Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, and Wintershall DEA , of  The National IOR Centre of Norway  for support. Acknowledgments: We acknowledge the support of the  German Research Foundation (DFG)  and the  Bauhaus-Universität Weimar  within the  Open-Access Publishing Programme. Furthermore , we acknowledge the  Research Council of Norway  and the industry partners,  ConocoPhillips Skandinavia AS, Aker BP ASA, Vår Energi AS, Equinor ASA, Neptune Energy Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, and Wintershall DEA , of  The National IOR Centre of Norway  for support.
Numerical scheme We have built the numerical scheme using as a basis the open-source code Basilisk  [4]. Basilisk provides both ready-to-use Finite Volume (FV) solvers for fluid dynamics problems (shallow-water, compressible, incompressible, multiphase. ), and an ensemble of useful c-language libraries in order that users can tailor, with a moderate effort, their own specific code. The incompressible Basilisk solver uses a second-order in space time-splitting projection method. The interface is tracked with a color variable, c(x, t), which represents the volume fraction. c is convected with the fluid, ∂ t c + u • ∇c = 0. ( 13 ) The above volume fraction equation is solved by successively advecting (sweeping) c along each of the spatial directions, x and y (or r in cylindrical coordinates), using a one-dimensional scheme.
Spatial discretization and the adaptation algorithm The open source code Basilisk discretizes the computational domain using a structured grid of square finite volumes (termed hereafter cells) that can be either uniform or non-uniform. If a non-uniform grid is preferred, the discretization is arranged hierarchically in a quadtree structure  [56]  (see Fig. 1. b). In this type of structure, the size of a cell, h, is characterized by its level, , at which is located. Hence, the size of the cells at that level h ∝ 2 -. A prototypical cell of level can be parent of 4 children cells (at the level + 1). The root cell is that corresponding to = 0 from which the rest of the cells at a higher level hang down. A leaf cell is a cell without any child. In the example shown in Fig. 1. b, the grid would be formed by 16 leaf cells with four of them of level = 3, one of level = 1 and the rest of level = 2.
Our study is inevitably limited in extent due to the limited availability of X-ray and neutron beamtime together with the prohibitive expense of deuterated chemicals. However, we believe that this work both hints at interesting differences in behaviour for phospholipids monolayers on DES and demonstrates how our approach to fitting can provide a quantitative understanding of this behaviour. Recent developments in computational resources and software have enabled powerful methodologies and algorithms to be harnessed by those from non-expert backgrounds. This has benefitted significantly from opensource software projects such as the Python language  [28]  and the Jupyter notebooks framework  [29]. In the area of NR and XRR, the landscape of data-analysis software is diverse, with a range of software packages available from a variety of sources; refnx  [30, 31] , MOTOFIT  [32] , Rascal  [33]  Aurore  [34] , Refl1D  [35] , and GenX  [36].
The use of a Python library, such as refnx, enables the implementation of custom models that contain chemically-relevant information as well as the application of probability distribution function (PDF) sampling techniques. The Python library emcee  [37]  allows refnx to access the Goodman & Weare Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble method  [38]. This allows the sampling of the high-dimensionality parameter space, relevant in reflectometry analysis, in a Bayesian fashion, where the new samples are generated with consideration of those sampled previously  [39]. Bayesian inference gives an understanding of the PDF for the fitted parameters and therefore estimations of their inverse uncertainties and inter-parameter correlations.
The aluminium Wilhelmy plate was used over a traditional paper plate due to the low wettability of paper by the DES. The NR experiments were performed on FIGARO at the Institut Laue-Langevin using the time-of-flight method  [41]. Data at two incident angles of 0. 62 • and 3. 8 • were measured to provide a momentum transfer range from 0. 005 Å-1 to 0. 18 Å-1. Two surface pressures for each system and contrast was measured (DMPC: 20 and 25 mN m -1 , DPPC: 15 and 20 mN m -1 ). Similar to the X-ray procedure, samples were given enough time to equilibrate (at least two hours), kept under an inert atmosphere, and all measurements were conducted at 22 • C.
Throughout all of the analyses, the reflectometry scale factor was allowed to vary freely, while the background was constrained to the intensity of either the largest or second-largest q-value. In the second step, the head and tail component volumes, and head layer thickness determined from XRR were fixed for the refinement of the custom model against the NR measurements. This approach means that the number of variable parameters to fit the NR data can be reduced to two, namely the thickness of the tail layer, d t , and the interfacial roughness, σ t,h,s , for the co-refinement of two datasets. Table II also gives the details of the scattering lengths and SLDs used as invariant parameters for the NR fitting. In both cases, the refinement of the custom model to the experimental data involved the transformation of the reflectometry calculated from the model and the data into Rq 4 such that the contribution of the Fresnel decay was removed, before using the differential evolution method available to refnx from the scipy library  [58] , to find the parameters that gave the best fit to the data.
F. Interparameter correlations The use of Bayesian inference and MCMC sampling allowed for the probing of the probability distribution function for each parameter individually. However, it also enables the pairwise inter-parameter PDFs to be investigated, an example for DMPC at 30 mN m -1 is shown in Figure  5  (similar plots for the other XRR datasets and the NR datasets are available in Figures S3 -S21 of the ESI). These two-dimensional PDFs give important information about the correlations that are present between the parameters of the chemically-consistent model. The less circular in nature that the 2D PDF is, the greater the correlation that is present between the parameters, e. g. a north-east/south-westerly skewed PDF indicates a positive correlation (where an increase in one parameter correlates an increase in the other) while a north-west/south-easterly skewed PDF indicates a negative correlation (an increase in one correlates with a decrease in the other).
Substantial correlations are present in the parameters fitted to the XRR datasets, indicating important uncertainty that must be considered. In particular, as can be seen in Figure  5 , there is a positive correlation between the lipid head thickness, d h , and the solvent concentration in the head layer, φ h. This correlation can be rationalised as a result of the SLD of the solvent and the head layer (which is ∼50 % solvated) being similar, and therefore the boundary between the two is not easy to define. Such correlations are unavoidable without considering many neutron contrasts of the lipid and solvent, due to the solvophilic nature of the lipid heads. Another important correlation is that between the head thickness and the tail thickness, d t , again this is due to the lack of a well-defined boundary between the head and tail layers.
Such applications may include fundamental investigations of phospholipid monolayers in extreme environments (total or partial absence of water, cryogenic temperatures), protein-membrane interactions and development of new technologies for drug delivery. However, the PG component did show a significant difference; having a larger head component volume than observed for the same system in water. This suggests that the transfer of lipids to a DES is not just a simple substitution of the subphase. In this specific case, we have proposed an explanation based on the dissociation of the PG head component salt and the subsequent interaction with the DES. Finally, the use of MCMC sampling of the reflectometry model parameter space gives insight into the correlations present in XRR data modelling, that should be considered in work of this nature.
ACKNOWLEDGMENTS The authors thank  Andrew Nelson  for useful discussions & assistance with the refnx software. Additionally, we thank  Oliver Hammond  for insightful discussions regarding the effect of the DES. A. R. M. is grateful to the  University of Bath and Diamond Light Source  for cofunding a studentship (Studentship Number  STU0149 ). Thanks also to the  European Spallation Source  and the  University of Bath Alumni Fund  for supporting A. S. -F. We also thank  Diamond Light Source  (Experiment number  SI10546-1 ) and  Institut Laue-Langevin  ( DOI: 10. 5291/ILL-DATA. 9-13-612 )for the awarded beamtime.
The thermal behavior of RbHB was also analyzed at isothermal conditions (80, 100, 120 and 140 °C) in a stainless steel reactor. Inside the glovebox, 100 mg of sample was weighed, placed into the reactor and then closed tightly. Outside the glovebox, the reactor was connected to a pressure controller and immersed in an oil bath kept at the designated temperature. The dehydrogenation of the sample was followed by measuring the pressure change. The collected data were converted into mol H2 per mol of RbHB (i. e. equiv H2) and were plotted as a function of time.
For dried conditions, membranes were dried under vacuum (33 mbar) at M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 8 membrane samples were immersed in water to become wet before EIS measurements. EIS tests were performed with a Zahner-elektrik IM6 workstation in potentiostatic mode with amplitude of 10 mV in the frequency range from 1 Hz to 1 MHz. EIS measurements were recorded ex situ in a homemade cell specifically designed to this purpose, which consists of two stainless steel rods between which round membrane pieces of 1. 26 cm of diameter were placed. Measurements were performed from room temperature to 90 ºC by placing the cell inside a conventional oven with temperature control. The data were both collected and analyzed with Thales software from Zahner-elektrik (Germany) to obtain Bode plots  [43].
Simulation procedures To perform MD simulations we employ the LAMMPS simulation code  [43]. The NPT ensemble with zero external pressure is adopted and the Nose-Hoover algorithm is used to control the temperature and pressure. The Ewald sum is applied to calculate the electrostatic energy. For each simulation the time step is fixed at 1 fs. The structure is first minimized (0K Static Force Field) then an equilibration step is performed at the desired temperature during 7500 steps (7. 5 ps). The results are then collected after the 250 ps of the final MD step. The starting simulation supercell contains 8×8×8 CeO2 fluorite unit cells with periodic boundary conditions. We study the doping of this structure by Gd, Sm and Gd-Sm (1:1) cations. For each doped system the doping ratio is fixed at x=0.
For the evaluation of these errors, the validation dataset and the one predicted by the model were scaled (range-scaling) and their difference was evaluated and stored in a new matrix. A mean value for each observation in this matrix was then used as prediction error. The formula for this error is:  where x i is the i th prediction point, N is the number of variables, y j ( x i ) is the true value of the j th variable for the i th prediction point and ˜ y j (x i ) is the ROM's prediction for the same variable, for the same prediction point. These errors were below a value of 4% for all observations when q > 1. Different choices of trend and correlation function for the Kriging interpolation led to different results. For example, the choice of a Gaussian kernel (see Fig. 7 b ) for a PCA+Kriging model for this data-set could not reduce the prediction errors further, when increasing q from 2 to 5 or 9.
1D flame with two input parameters The methodology explained in Section 2 was tested on the same system presented in Section 4. 1 , using two input parameters. A molar fraction of N 2 was introduced in the inlet stream. A total of 147 observations were generated by OpenSMOKE++ in the range 0. 5-1. 5 for the equivalence ratio and 0. 4-1. 0 for the molar fraction of N 2. A set of 75 observations was selected with the AS strategy explained in Section 3. 5 for the training of the SM. The remaining observations were used as test data. Fig. 15  a reports the eigenvalue spectrum of the PCs, for different scaling criteria used. Fig. 15 b  shows the cumulative original data variance that was captured when more PCs were retained in the reduced basis. This is reported for different scaling criteria applied to the data-set.
1D flame with three input parameters After validating the predictive capabilities of a ROM developed on the combination of PCA and Kriging on systems with one or two input parameters, one more parameter was added to the system, namely the inlet stream temperature in the range 298-798 K. Two sets of observations were again generated by OpenSMOKE++. A set of 180 solutions, selected using the AS strategy, was used to train the model. The other set (702 solutions) was used for the validation of the ROM's predictions. Figs. 20 a and b show the eigenvalue spectra and original data variance recovery for the different scaling criteria, respectively. Over 99% of the original data variance was captured by 28 PCs. Fig. 21  shows predictions of temperature, CO, OH and NO mass fractions for the PCA+Kriging (48 PCs) and LPCA+Kriging (20 clusters) models, with a quadratic trend function and Matern52 kernel.
(2013)  and in  Cao et al. (2015)  was used. The input parameters were two, namely the inlet velocity and the molar fraction of CH 4 in the inlet stream, which was a mixture of CH 4 and N 2. A total of 30 samples was produced by OpenFOAM, spanning the two input parameters in the range 24-89 cm/s and 40-100% for the inlet velocity and inlet molar fraction of CH 4 , respectively. A total of 36 physical variables was considered: 35 chemical species and temperature. 25 observations were chosen to train a PCA+Kriging model using the adaptive strategy described in Section 3. 5. Fig. 22  a reports the eigenvalue spectrum of a PCA performed on this data-set, for different scaling criteria. Fig. 22  b shows the cumulative original data variance that was recovered when more PCs were retained. The recovery of 99% of the original data variance was achieved with 20 PCs for all scaling criteria.
2D flame with two input parameters, using a smaller parameter region The 2D data-set discussed above included observations for a high range of velocities. The parameter region to be explored in-   molar fraction of 75%. PCA was performed with a number of 5 PCs, local PCA with a number of 100 clusters. Kriging was performed with a quadratic trend function and an exponential kernel. Fig. 31  reports the parity plots for the predicted values of temperature and CO 2 , CH 4 , CO mass fractions. These figures clearly indicate that better predictions were obtained in the region of low inlet velocities in comparison to Fig. 28  , where the data spread was higher. In particular, it is observable that most predictions by the local PCA+Kriging model fell within the 5% error region (dashed red). The limitations of the predictive model observed in the previous case were due to the wide range of explored conditions: the inlet velocity was indeed allowed to change roughly by a factor of 4, which led to substantial modifications in the flame topology and structure.