dataesr/scientific-paragraphs-categorization huggingface dataset:
Membrane morphology The morphology of the prepared membranes containing RILC8_Br was characterized by atomic force microscopy (AFM) and scanning electron microscopy (SEM). It should be remembered that the membrane surface on the glass side of the Petri dish (bottom surface) can be smoother to that of the membrane surface exposed to air (top surface)  [36]. Therefore, SEM images were registered for the top surfaces of the membranes. Fig. 11  reveals that the membrane surface roughness for the sample before and after the transport is very similar. Only the evaluated roughness parameters, expressed as the root mean square roughness (R q ), average arithmetic height (R a ), and maximum vertical distance between the highest and lowest data points (R max ) differ and equals 3. 97, 3. 18, and 39.
Compared to the results of membrane transport experiments performed with Aliquat 336 as a carrier, the fluxes of Cd ions are lower; however, the selectivity of the transport is much higher. The morphology of the prepared membranes examined by AFM and SEM showed that PIMs with a carrier a and plasticizer do not have a microporous structure, i. e. the membrane structure is dense. Despite some changes in the membrane surface morphology before and after the transport experiments, these differences seem to be insignificant for transport properties, which was confirmed by the stable behavior of the PIM system during a long-time continuous operation. It should be noted that the model for the transport description proposed in this manuscript provides a very good fit to the experimental data and indicates that the fluxes calculated from the model proposed by Danesi  [28]  are in many cases underestimated.
Generalities The data for compounds 1THF and 32. 5 dioxane were collected at 150(2) and 180(2) K, respectively, on a Nonius Kappa-CCD area detector diffractometer using graphite-monochromated Mo-Kα radiation (λ = 0. 71073 Å). 2  The crystals were introduced into glass capillaries with a protective coating of Paratone-N oil (Hampton Research). The unit cell parameters were determined from ten frames, then refined on all data. The data (combination of φ-and ω-scans with a minimum redundancy of 4 for 90% of the reflections) were processed with HKL2000. 32 Absorption effects were corrected empirically with the program SCALEPACK. 3 The structures were solved by intrinsic phasing with SHELXT,  4  and refined by full-matrix least-squares on F 2 with SHELXL-2014. 5 All non-hydrogen atoms were refined with anisotropic displacement parameters.
ACKNOWLEDGMENT The authors acknowledge for financial support of this work:  CEA ,  CNRS ,  CINES  (project  sis6494 ), the  CHARMMMAT Laboratory of Excellence , the  European Research Council  ( ERC Consolidator Grant  Agreement no. 818260 ),  ADEME  (fellowship to AA). This research was supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 768919 ,  Carbon4PUR  project.
After each deliquescence process, particles were recrystallized to compare the initial and final 2D-projected areas. The particle images were processed using ImageJ software. 41 Multicomponent aerosol surrogate particles in the coarse size range were examined for physical-chemical effects of hygroscopic ageing. Several previous works have demonstrated that DRH values of particles larger than 100 nm are not affected by initial size of the particles. 6, 7, 10, 15, 16, 42, 43 The growth factor (GF) of the particles during humidification was calculated from the ratio between the 2D projected area of the humidified particle (A) and the projected area of the dried particle (A 0 ). The uncertainties linked to the projected area measurements are about 1 pixel corresponding to 0. 26 mm. The mutual deliquescence relative humidities (MDRH) and the deliquescence relative humidities (DRH) were deduced from the plots of the GF as a function of the RH (herein humidograms).
Acknowledgements This work was supported by funds from  Labex CaPPA   WP-2  and the  CPER  research project  CLIMIBIO. The  CaPPA   project (Chemical and Physical Properties of the Atmosphere)  is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract '' ANR-11-LABX-0005-01 ''. The authors thank the  French Ministere de l'Enseignement Superieur et de la Recherche , the  Hauts-de-France Region  and the  European Funds for Regional Economic Development  for their financial support to this project. M. C. , N. V. and S. C. thanks the  Universitede Lille  and the  Institut de Recherches Pluridisciplinaires  en.
42 spite the interest shown in the fire safety of structural energy storage devices, to the best of our knowledge, there is no information regarding the fire/combustion properties of their individual components or of devices as a whole reported in the literature. Moreover, there is no information available concerning the fire/combustion properties of epoxy-IL blends. The purpose of the work reported here is to address this omission in the literature. Here, we present data concerning the fire properties of structural electrolytes comprising of epoxy resin and IL/DES, determined using a cone calorimeter and two heat fluxes, 35 and 50 kW/m 2. The effect of composition on the ignition time, HRR, mass loss, and the gases emitted are presented and discussed.
| DES preparation The DES was synthesized by mixing ChCl and EG in 1:3M ratio  43  at 80 C and constant stirring using a magnetic stirrer for 24 hours. The ionic conductivity of synthesized DES was determined to be 7. 58 mS/cm which is in agreement with data reported in the literature. 43 3 | Preparation of the structural electrolytes 2. 3. 1 | Synthesis of structural electrolytes via reaction induced phase separation DGEBA was dissolved in the required amount of IL followed by the addition of the measured amount of iPDA. Next, the mixture was stirred until a homogeneous solution was obtained following degassing. Prepared formulations were cured using horizontal silicon moulds to produce plaques with dimensions 100 mm × 100 mm and thickness of 3 to 4 mm. For curing the following cycle was used: 1.
ACKNOWLEDGMENTS Natasha Shirshova  would like to acknowledge the  EPSRC  for their financial support through projects " Safety and Fire reaction of structural power storage devices "  EP/T013044/1  and "Beyond structural: multifunctional composites that store electrical energy"  EP/P007546/1. Natasha Shirshova  also acknowledges help with SEM imaging provided by  Dr W. Quan. This work pertains to the  French Government programme "Investissements d'Avenir" (LABEX INTERACTIFS , reference  ANR-11-LABX-0017-01 ). The authors are also immensely thankful to the  "Institut des Risques Industriels Assurantiels et Financiers" of the University of Poitiers  for the technical support it provided.
In the field of water content measurement, the calibration of coulometric methods (e. g. , coulometric Karl Fischer titration or evolved water vapor analysis) is often overlooked. However, as coulometric water content measurement methods are used to calibrate secondary methods, their results must be obtained with the highest degree of confidence. The utility of calibrating such instruments has been recently demonstrated. Both single and multiple point calibration methods have been suggested. This work compares these calibration methods for the evolved water vapor analysis technique. Two uncertainty estimation approaches (Kragten's spreadsheet and M-CARE software tool) were compared as well, both based on the ISO GUM method.
This is done by regularly analysing a set of reference or calibration standards. The EWV was operated according to the manufacturer recommendations, the calibration samples were used to calculate a correction factor, called cell constant, (Eq. 2) to compensate for possible bias of the system-a single point calibration. If the measured water content differed by more than ± 5 % from the reference value, the sensor was regenerated and the sample oven cleaned. Work was resumed once calibration sample measurements were within ± 5 % of the reference value. To account for the residual water remaining on the sample boat or in the system, a "tare" (empty sample boat) was measured before each measurement sequence. The obtained value was subtracted from subsequent measurement results. Kaolinite clay, wood pellet and cardboard samples were analysed as case studies to assess the performance of the EWV analysis and data treatment methods with real life samples.
Multiple Point Calibration The calibration curve was constructed separately for each measurement day, by pooling all CC and tare measurements from 1 day, the measurements with varying amounts of reference materials were spread randomly throughout the measurement sequence. As the number of electrons measured by the instrument is linked the amount of water, the calibration curve was constructed as follows: the amount of water measured in a calibration sample on the X-axis and the reference amount of water in the sample (calculated from the sample amount and reference water content) on the Y-axis (Fig. 2 ). The acquired data was processed using a software tool (later as M-CARE) published previously by Collège Français de Métrologie  [19]. This tool enables the use of generalized Gauss-Markov regression, which allowed uncertainties to be assigned to quantities on both axes.
Different aliquots of standard samples were weighed in such a manner, that different amount of water was measured in each CC measurement. For example, a 0. 1000 g sample of RM contains 980 μg water and a sample of 0. 0500 g of RM 490 μg of water. This way, several calibration points could be prepared from a single standard sample material. The standard sample sizes were chosen to encompass the analytical range-to ensure that all sample measurements were calculated via interpolation as opposed to extrapolation; the latter being less accurate. All tare measurements were included as 0;0 points. As shown in Eq. 3, the amount of water found in tare measurements was subtracted from the amount of water in the samples before data processing. The tare measurements were included in the calibration curve because they were intrinsic to data processing and there was an uncertainty component associated with tare measurement (as seen in Table  2  and Fig.
After an in situ reduction for 2 h at 450 °C (5 °C/min) under 40 mL/min of H 2 , the powder was desorbed at 450 °C under N 2 (100 mL/min) for 4 h. After cooling down to 30 °C, the catalyst was exposed to 5% CO 2 in N 2. Then, the reactor was flushed for 30 min under N 2 before the temperature was increased from 30 °C to 450 °C (5 °C/min) to desorb CO 2. The outlet gases were analyzed by infrared spectroscopy and the CO 2 quantity was determined from the asymmetric stretching bands at 2450-2100 cm -1 using a calibration curve. X-ray fluorescence (XRF) analysis was performed using an Epsilon 4 spectrometer from Malvern Panalytical. The generator was used at 50 kV and 60 µA under air atmosphere. Data were collected at the Mo Kα energy level (17. 44 keV) using a 3-point calibration curve verified by inductively coupled plasma -optical emission spectroscopy (ICP-OES).
Acknowledgment  A. Nassereddine  ( IRCELYON ) is acknowledged for preliminary catalysis experiments. Y. Lefkir & S. Reynaud  ( Laboratoire Hubert Curien ) and  M. Aouine  ( IRCELYON ) are acknowledged for their support in STEM analysis. CLYM is acknowledged for access to the  FEI Titan and Jeol NeoARM microscopes. N. Bonnet & P. Mascunan  ( IRCELYON ) are acknowledged for XRF and BET analyses. Agence Nationale de la Recherche  ( UltraCat  project,  ANR-17-CE06-0008 ) is acknowledged for financial support. JL is a Serra Húnter Fellow and is grateful to the  ICREA Academia program  and projects  MICINN/FEDER   RTI2018-093996-B-C31  and  GC 2017 SGR 128.
An extra energy sink term is introduced in the volume D equal to the power output of the turbine at each time step. The extrapolated turbine maps by the models described in  (Serrano et al. , 2020b)  were used to compute the stator (C) and rotor (E) nozzles using the techniques described in  (Serrano et al. , 2008) , and the efficiency is quasisteadily obtained as represented in  (Serrano et al. , 2020b). Gas dynamic effects on the compressor side are modelled using two volumes and the connecting tube as described in  (Galindo et al. , 2019). Moreover, few different submodels for estimating the mechanical losses  (Serrano et al. , 2013)  and heat transfer effects  (Payri et al. , 2014; Serrano et al. , 2015b)  in the turbochargers are also taken into account during the simulation. All the model information were transferred into GT-Power software by creating an external library link.
By using this library link, the CMT-DETCM model can be able to simulate in the GT-Power software as a gas stand or just coupling to the engine model. The CMT-DETCM model was validated beforehand with the data obtained from the gas stand. The data was acquired with more accurate instrumentation, placing several thermocouples at the inlet and outlet of the turbine for temperature measurement. More details about this work can be found in  (Samala, 2020). It is worth highlighting that the internal and external heat transfer models used in this work are previously developed and calibrated for a single/VGT turbocharger. However, the heat exchange process in double-entry turbines will be different due to the imbalance of flows and different levels of temperatures coming from the engine cylinders to the turbine inlets.
The test facility is controlled automatically by a control system called PUMA V5. It allows acquisition of a set of variables that characterize the behaviour of the different systems of the engine. The data obtained by the sensors through this system are within a frequency of 10-20 Hz. In order to record the instantaneous measurements at the intake manifold and in the cylinders, Kistler type 4045A and AVL ZI33 sensors were used. The AVL sensor ZI33 is a spark plug with an integrated pressure sensor. In an engine test bench, the instantaneous pressure installation at the exhaust manifold is more restrictive due to the design and alignment position of the dual-volute turbine with the engine. Furthermore, there were no pressure probes on the turbine as well. Therefore, a low-speed piezoresistive pressure sensor of Kistler type 4262A was installed.
Acknowledgements The work has been partially supported by the  Spanish Ministerio de Economía y Competitividad  through grant number  TRA2017-89139-C2-1-R. P. J. Martinez-Hernandiz  is partially supported by an  FPI  contract ( FPI-S2-19-21993 ) of the  "Programa de Apoyo para la Investigación y Desarrollo  ( PAID-05-19 )" of the  Universitat Politècnica de València. The authors also wish to thank  Mr. Gabriel Alcantarilla  for his inestimable assistance during the experimental campaign.
Additionally, a reduction in soot volume fraction and soot number density was noted. These observations were mainly attributed to the ionic wind acting on the flow field and the soot formation process. The main objective of this study is to investigate numerically the effects of the electric body forces on a flame which was studied previously experimentally  [30]. In this work, a numerical model was developed for a laminar diffusion ethylene flame using the CFD software FLUENT. The electric field equation with the source terms for the governing equations were integrated to the model by implementing User Defined Functions (UDF) codes in the solver and resolving User Defined Scalar (UDS) equation for the potential. Here, the impact of electric field on gas dynamic during combustion is studied.
In the first three cases, only the radiation energy lost by H2O, CO2 and CO (if it exists in the scheme) was considered, whereas in the fourth case, the radiation loss by soot is added to the gaseous species radiation loss. In this case, soot characteristics were calculated by resolving a simplified Moss-Brookes model. The Plank mean absorption coefficient for gaseous species was fitted using n-th degree Gaussian functions based on  [39] ; ks,Plank was calculated by applying the following expression  [40] : * C,IAJKL = 1464 N 4 C = ( 13 ) Where fs is the soot volume fraction and T is the local temperature. The choice of the chemical schemes and the validation of the numerical model were performed according to the measured axial velocity and temperature profiles of Santoro's experimental data  [33].
It was found that the k d value (1. 06 × 10 -5 m/s) for 100% ultrasonic amplitude nearly corresponded to the k d value (1. 11 × 10 -5 m/s) of 100 rpm rotation speed. In addition, the transmitted acoustic power (P T ) was measured at various ultrasonic amplitudes using the method presented by Mason et al. [16]  and Contamine et al. [17]. In this method, a thermocouple was placed in the inner reactor containing ultrapure water (7 mL). The circulation of the cooling/coupling fluid was stopped. The temperature equalized with the reactor sample and the calorimetry experiments were performed thereafter. The temperature increase, due to the conversion of mechanical energy into heat, was recorded every second by using a National Instruments thermocouple controlled by a LabView software. Herein, the acoustic powers are quoted as W/dm 3.
The charges (Q) from the CA curves were determined using the EC-Lab software. Faradaic efficiencies (FE) were calculated using equation (  1 ):  where n is the number of moles of gaseous products in the gas phase, z is the number of electrons transferred in the CO2RR to produce the product, F is the Faraday constant (96,485. 3C/mol) and Q is the charge in C. During all CA experiments, the sonoelectrochemical reactor was completely gas tight. A 100 µl sample of the headspace atmosphere was collected immediately after each CA experiment using a Vici Series A-2 gas syringe. The sample was injected into a gas chromatograph (GC; Model 8610C, SRI Instruments) for product analysis using both thermal conductivity detector (TCD) and flame ionization detector (FID) as detectors. The GC used a 1. 8 m Hayesep-D column with argon (Ar) for a carrier gas.
The GC was equipped with a TCD for H 2 detection and a FID for detecting volatile organics such as CO, CH 4 , and C 2 H 4. The analysis of the products and faradaic efficiencies were computed from the GC data based on calibration experiments that used small molecule calibrant standards (Restek Corp. ). FE = n × z × F Q × 100% (1) The liquid products were collected and analyzed by nuclear magnetic resonance spectroscopy ( 1 H NMR) using a Bruker 500 MHz liquid-phase NMR. The cell solution from each experiment was mixed in a 9:1 mass ratio with D 2 O (Sigma-Aldrich). Dimethyl sulfoxide (DMSO; Sigma-Aldrich) was used as an internal standard due to its single 1 H peak at a chemical shift of 2. 7 ppm. Data was collected using solvent suppression to reduce the 1 H signal from the water at roughly 5 ppm.
Thermochemical evolutions of turbulence/combustion-chemistry interaction are built from the EMST micro-mixing model and a syngas oxy-flames chemistry. • The thermochemical database is preprocessed using unsupervised algorithm K-means and Local Principal component analysis. • The resulting set of data serves as input to train neural networks for the time integration of chemistry. • Using the ANNs instead of a dedicated stiff solver for chemistry, the computational time is reduced by an order of magnitude.
org/10. 1016/j. egyai. 2021. 100082 2666-5468/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ) combustion chemistry, the CPU time devoted to their integration remains quite significant and in most cases, too large to allow for performing the multiple simulations needed to complete the quest of the optimal design for a combustion system. These computing limitations motivated the introduction of artificial neural networks (ANN) to deal with chemistry reduction and its time integration  [6]  and also to develop data driven turbulent combustion modeling  [16]  , or to analyse experimental measurements  [24]  and to setup digital-twins for process control  [3]. Among these works, ANN and convolutional neural networks (CNN) were trained on a representative dataset to replace chemistry integration  [22]  and the method was applied to perform direct numerical simulation (DNS) of a syngas oxy-flame.
Nevertheless, the training of the full dataset using a single or two ANNs required specific treatments, such as data augmentation through manufactured intermediate solution points for dealing with some fast reacting species  [22]. These operations requiring fine tuning by the user were found necessary to secure good accuracy over the whole domain covering from chemically frozen-flow mixing up to the equilibrium state and pollutant emissions, with fast ignition and fuel oxidation in between. To avoid relying on these additional more or less ad-hoc operations, thus allowing for having a computerised and fully automatic procedure, a novel method is proposed in the present study for turbulent non-premixed flames in which techniques such as data clustering with K-means  [15]  and dimension reduction using LPCA  [1]  , are combined with ANN training to secure, by construction, accuracy over the entire composition space domain (mixing, ignition, combustion and equilibrium state).
Database generation A database of chemical evolutions is first generated by solving for the mass fractions and the enthalpy of a set stochastic particles featuring at initial condition the concentration in the fuel or in the oxidizer inlet. Then, the species mass fraction    (  ) and sensible enthalpy ℎ   (  ) of each stochastic particle evolve according to the following equations: 𝑑𝑌 𝑝 𝑖 ( 𝑡 ) 𝑑𝑡 = MIX 𝑝 𝑖 ( 𝜏 𝑇 ) + ω 𝑝 𝑖 , ( 1 ) 𝑑ℎ 𝑝 𝑠 ( 𝑡 ) 𝑑𝑡 = MIX 𝑝 ℎ 𝑠 ( 𝜏 𝑇 ) + ω 𝑝 ℎ 𝑠 , ( 2 ) where 'MIX' denotes the stochastic turbulent micro-mixing closure for the diffusive budget in a non-premixed system. denotes the micromixing time and ω   and ω  ℎ  are the species and enthalpy chemical sources, respectively. In the present study, the Euclidean minimum spanning tree (EMST) micro-mixing model  [21]  is used to model the turbulent micro-mixing.
This approach to generate thermochemical conditions representative of those observed in a real system for studying its chemical response, without explicitly solving for the flow, has been shown effective for partial oxidation of natural gas  [8]  , aircraft engines combustion chambers  [5]  , large-scale furnaces with urea DeNOx automated control  [12]  , micro-and meso-scale combustion systems  [4]  and also to build the database for machine learning application to sooting flames  [19]. Here a total number of 1429 stochastic particles is employed with 740 particles (corresponding to the mass flow rate of 0. 74 kg/s) assigned to Fuel inlet and 689 particles (corresponding to the mass flow rate of 0. 69 kg/s) to O 2 inlet. From the evolution of these particles, the first step consists of identifying, from a detailed chemical scheme, the important species to be retained and whose evolutions shall be learned by the ANNs.
This is done using the direct relation graph with error propagation method (DRGEP)  [17]. For the present operating conditions, this step was previously performed and reported in Wan et al. [22]. Starting from the detailed scheme GRI3. 0  [20]  , 11 species were identified as essential to follow the dynamics of the syngas chemical evolutions: O, O 2 , H, H 2 , OH, HO 2 , H 2 O, H 2 O 2 , CO, CO 2 and N 2. A companion reduced chemistry involving 23 reactions was then optimised (see Table  2  of  [23]  ). To save CPU time during training and to demonstrate the integration of chemistry with ANN, this 11-species and 23 elementary reactions scheme is used to build the time evolution dataset of all stochastic particles, over 4999 time-steps of 0. 3 μs with a mixing time   = 0. 3 ms (a representative value for a characteristic turbulent mixingtime as observed in shear layers for Reynolds numbers typical of burners).