dataesr/scientific-paragraphs-categorization huggingface dataset:
0x10 -9 to 5. 0x10 -9 m 2. s -. Apart from terpenes, for which variable diffusion coefficient values were measured, with an SD of 89. 4%, the diffusion coefficients measured in the three experiments for all VOCs were quite similar, with SD of less than 49. 5%. The results for terpenes could be explained by their higher variability between PB samples compared to other VOCs. Indeed, variations in terpene emission rates between PB samples would significantly affect the first concentration profile under the airtight conditions used to determine the diffusion coefficient Di, resulting in more scattered diffusion coefficient values for terpenes. According to the data presented in Table  5 , formaldehyde once again differs from the other VOCs monitored. Based on the results of this study, the enhanced formaldehyde emission rate as a function of the ACR could be explained by the following three hypotheses: (i) Subject to the dynamic equilibrium concentration between the material and the air, the VOC mass transfer needs to be strengthened if we are to satisfy this balance when the ACR increases.
This analysis would be worth a full paper. The influence of environmental parameters on VOC emissions will need to be further investigated as temperature has been observed to have a significant effect on overall VOC emissions at chamber scale. The influence of the environmental conditions (temperature and RH) on key emission parameters (Ki, C0,i, Di) should then be assessed at material scale. Since the impact of environmental parameters on emission parameters has not been extensively documented, with data only available for formaldehyde, relationships between environmental parameters and each emission parameters could be proposed for several VOCs emitted from a single solid material. This paper provides new insight into the mechanisms governing VOC emissions. Moreover, some VOCs were shown to display unique behaviour with respect to ventilation, in particular formaldehyde.
II. SIMULATION DETAILS MD simulations of WiS electrolytes were carried out using the LAMMPS package  33. All cubic simulation boxes contained 473 water molecules and 128 salt ion pairs that were randomly distributed using Packmol  34. The SPC/E water model was used in these simulations  35  , whereas the parameterization of the anions was made in the framework of the CL&P  36  force field for ionic liquids, for which we employed the set of parameters reported in Ref. 37. Lithium cations were modelled as a single site whose Lennard-Jones (LJ) parameters are = 6. 25 kcal/mol and σ = 1. 25992 Å. It must be noted that the charges of both ions were uniformly scaled by a factor of 0. 8 so as to accelerate the dynamics of the mixtures, which was shown necessary to have good agreement with the experimental results in LiTFSI WiS  38.
Unlike in ionic liquid/water systems, where the interaction between H 2 O molecules and anions is generally stronger than with the cations  50  , in WiS electrolytes they are strongly attracted by the lithium ions  8. At infinite dilution the first solvation shell of Li + is made of four water molecules. At the molality studied here, the ratio H 2 O/Li is smaller than four. As a consequence the first solvation shell of lithium cations includes both water and anions  16, 22. It was previously reported that as the concentration of the mixture increases the coordination environment is expected to change from solvent-separated ion pairs (SSIP) to a majority of contact ion pairs (CIPs) and ionic aggregates (AGGs)  17, 23, 51. In the present work, we employed the trajectory analysis program TRAVIS  52  to analyze the formation of domains in these electrolytes, whose methodology is based on Voronoi tessellation  53, 54.
ACKNOWLEDGMENTS This work was supported by the  French National Research Agency  ( Labex STORE-EX , Grant No. ANR-10-LABX-0076 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 771294 ). We acknowledge support from  EoCoE , a project funded by the  European Union  Contract No. H2020-EINFRA-2015-1-676629 , from the  DSM-Energie programme   of CEA  and from the  Eurotalent programme. This work was granted access to the HPC resources of CINES under the allocation  A0080910463  made by  GENCI.
Solving the system of equations (  14 ) with Matlab, the computed theory obtained is in good quantitative agreement with the experimental data points without any fitting parameter (see Fig. 3(b) ). Both the linear response at small pressure and the strong non-linearities at larger pressure are well predicted by the model. We obtain, for long enough chains (i. e. for large J m ), an S-curve. It means that two phases may coexist for one given pressure, one stretched, the other barely strained. When the mechanical response of a system (assumed homogeneous) includes a regime where strain decreases with load, and later a subsequent stiffening (i. e. S shaped curved), a phenomenon analogous to a phase separation occurs. In such cases, a Maxwell construction  [8]  leads to a constant load for which two different state coexist in a spatially extended system.
It is impossible, however, to analyze the phase between these two signals. The measured shear rate is a macroscopic quantity and is low when the most viscous part of the sample is located in the thinnest part of the Couette gap. By contrast, the density is a local value measured at a fixed location, and we do not know whether it corresponds to the thinnest part of the gap or not. The previous analysis made using the normal stresses points out that the high-density zones are correlated to high viscosity, which suggests that local data are not collected at the thinnest gap location. To estimate the stress applied by the density inhomogeneities on the rotor, we have measured the lateral stiffness of the rotor, by measuring the force exerted to move the rotor with a rigid tip of small contact area positioned at the same location as the observed density inhomogeneity.
Polyvinyl chloride We used three geometries to measure the rheological behavior of PVC. In Fig. 1 , we displayed the data measured in a homemade Couette cell with a rotor radius equal to 28 mm and a gap equal to 1. 2 mm. The experiments have been performed using a Kinexus rheometer from Malvern and an ARG2 rheometrer from TA Instruments. The PVC sample is loaded in the Couette cell, and we set the geometry in the desired position and wait 60 s before starting the experiments. The first set of measurements has been obtained under constant imposed stress ramped up from 1 to 500 Pa in 36 logarithmically spaced steps of 60 s. The second set of measurements is different. The PVC sample is loaded in the Couette cell, and we set the geometry in the desired position and wait 10 min before starting the experiments.
We applied a given shear stress for 1 hour and recorded the temporal evolution of the shear rate. At the end of the experiment, we discard the sample and start with a fresh one to study another shear stress. We have measured the lateral stiffness of the rotor of the Kinexus rheometer, by measuring the force exerted to move the rotor with a rigid tip of small contact area positioned at the top of the rotor. This stiffness is equal to 1. 3 × 10 4 Nm -1. Data of Fig. 3  have been collected in a Couette cell devoted to x-ray scattering with a rotor radius equal to 24 mm and a gap equal to 1 mm. The experiments have been performed using an ARG2 from TA Instruments. In this situation, the PVC sample is loaded in the Couette cell, and we set the geometry at the desired position and wait 60 s before starting the experiments.
We apply steps of shear stress over 600 s. The various shear stresses applied are 5, 20, 50, 75, 100, 125, 150, 200, 300, 400, 200, and 50 Pa. We have measured the lateral stiffness of the rotor of the ARG2, by measuring the force exerted to move the rotor with a rigid tip of small contact area positioned at the top of the rotor. This stiffness is equal to 1. 5 × 10 4 Nm -1. In the Supplementary Materials, we display data obtained in a coneand-plate geometry of 2 ° angle and 40-mm radius using an ARG2 rheometer from TA Instruments. The geometry is sandblasted. The rheological procedure is the following. The PVC sample is loaded in the cone-and-plate geometry, and we set the geometry in the desired position and wait 10 min before starting the experiments. We applied a given shear stress for 1 hour and recorded the temporal evolution of the shear rate.
The calibration was performed with polystyrene standard (PS). A differential refractive index (RI) detector was used. The M n values that were obtained by SEC are multiplied by a 0. 58 correlation factor for PLA analyzed with PS standards, corresponding to the Mark Houwink equation. T g , T m and ∆H m values were determined by differential scanning calorimetry (DSC) using Q200 TA instruments. Polymeric samples were first heated from 25 • C to 200 • C at 10 • C/min. All thermal data were obtained from the first scan.
Many studies have shown high ability of machine learning algorithms to offer faster and more robust computational schemes than the classical empirical correlations and methods and can thus save time and costs related to designing or performing future experimental studies  (Javadian et al. , 2018; Hemmati-Sarapardeh et al. , 2013; Nait Amar et al. , 2019a; Ayegba et al. , 2017; Ahmadi et al. , 2018; Raja et al. , 2017; Shokir et al. , 2017; Benamara et al. , 2019a). The main purpose of this study is to advance the research on development of high exactness and simple-to-use machine learning approaches that can predict the viscosity of CO 2. This will be done by modeling CO 2 viscosity as function of temperature and density based on an extensive database encompassing wide ranges of pressure and temperature conditions.
To do so, three advanced data-driven techniques, namely multilayer perceptron (MLP), group method of data handling (GMDH) and gene expression programming (GEP) were implemented. Both the GMDH and GEP methods produce the output as an explicit function of the input parameters, where GMDH applies a polynomial form while for GEP the type of mathematical operations is user specified. Numerous statistical and graphical assessment criteria were considered in the evaluation of the newly proposed models. A comparison of the performance of our results with the best available prior Machine Learning models and with well performing explicit correlations was performed. A trend analysis was conducted to observe variations of CO viscosity with respect to density and temperature. Further, outlier detection was performed to quantify potential experimental points deviating from main trends in the database.
There are some important differences between this work and existing studies where ML has been applied to model CO 2 viscosity: (1) more widespread temperature (220-673 K) and pressure (0. 1-7960 MPa) range conditions were considered in the training of the models, (2) four backpropagation based learning algorithms were evaluated in the training of the MLP based CO 2 viscosity model, (3) this study is not limited to black box ML models, but also two distinct explicit correlations for CO 2 viscosity based on GMDH or GEP were derived, (4) The database of experimental points and the best of the developed ML models are provided in Excel files to the benefit of the readers. The rest of the paper follows this structure: Section 2 describes the database and main assumptions utilized in this study to develop the models and explicit correlations.
Data gathering and preparation Consistent models need to be developed from a reliable database that contains a large number of experimental measurements. The viscosity of CO 2 has been investigated experimentally in several studies, where the effects of variables such as temperature and pressure on this parameter were in focus. Among such works, Laesecke and Muzney  (Laesecke and Muzny, 2017)  gathered the largest known database on CO 2 viscosity as function of temperature and pressure. In this work, a widespread experimental database including samples for CO 2 viscosity with corresponding values of temperature, density and pressure was utilized for developing the proposed models. These experimental measurements were collected from several literature sources  (Laesecke and Muzny, 2017; Van Der Gulik, 1997; Haepp, 1976; Vogel and Barkow, 1986; Golubʹev, 1970; Estrada-Alexanders and Hurly, 2008; Abramson, 2009; Kestin and Whitelaw, 1963; Vogel, 2016; Michels et al.
, 1957; Sch€ afer et al. , 2015; Hendl et al. , 1993). The database was randomly divided into a training set (80% of the database) and a test set (20% of the database). Table  1  represents the ranges of variables applied in this study. Our models (either using MLP, GMDH or GEP) assumed that the viscosity of CO 2 could be modelled as function of temperature and density, in line with the literature  (Laesecke and Muzny, 2017; Abdolbaghi et al. , 2019)  where it has been claimed that more accuracy can be obtained using density rather than pressure as the second input parameter. Hence, the models and correlations take the following form: μ CO2 ¼ f ðT; ρÞ (1) When performing the modeling task using MLP, the data points were normalized between À 1 and 1: x norm ¼ 2ðx i À x min Þ ðx max À x min Þ À 1 (2) where x norm is the normalized value of x i , x min and x max are the minimum and maximum values of the variable x (corresponding to viscosity, temperature and density), respectively.
The data were not normalized when applying the GMDH or GEP methods. Mean square error (MSE) was considered as the assessment function during the training phase of the different models. MSE is defined as follows: MSE ¼ 1 N X N i¼1 ðμ exp i À μ pre i Þ 2 (3) where μ is the viscosity of CO 2 , N is the number of samples in the dataset and the superscript exp and pre indicate experimental and predicted values, respectively.
Performance evaluation To evaluate the performance and robustness of the proposed models, statistical indicators, namely root mean square error (RMSE) and coefficient of determination (R 2 ) are used in the evaluation. The mathematical expressions of the latter are specified as follows: RMSE ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 1 N X N i¼1 À μ exp i À μ pred i � 2 v u u t (20) R 2 ¼ 1 À P N i¼1 À μ exp i À μ pred i � 2 P N i¼1 À μ exp i À μ pred i � 2 (21) Fig. 2  illustrates the obtained cross plots for the different models showing predicted CO 2 viscosity versus the experimental data. The reference unit slope corresponds to perfect match. It is seen that all the models exhibit very satisfactory distributions of predictions near the unit slope line for both training and testing datasets, demonstrating high reliability in estimating the viscosity of CO 2.
By close inspection, the MLP-LM and MLP-BR models have the least scatter around the unit slope line, while more scatter is seen for the MLP-SCG and MLP-RB and even more for GMDH and GEP models. The goodness of the match is illustrated more directly in Fig. 3  which shows comparative bar plots of RMSE and R 2 values for the proposed models evaluated on the overall dataset. As seen, all models have R 2 values almost identical to 1, indicating very good prediction, although the RMSE values clearly distinguish between the models in terms of their matching ability. Especially, these values demonstrate the same quality trend as seen visually in Fig. 2. The statistical parameters of the calibrated models evaluated for the training, test and overall datasets are listed in Table  3. Evaluation of the statistical parameters reported in Table  3  and Fig.
Comparison with the existing models To compare performance of the proposed MLP-LM model and the GEP model correlation in estimation of CO 2 viscosity, two prior approaches were selected; the best established paradigm by  Abdolbaghi et al. (2019) , which consists of a radial basis function neural network optimized by PSO (RBF-PSO) and the correlation of Laesecke and Muzney  (Laesecke and Muzny, 2017). The four stated models were applied to the collected CO 2 viscosity database. The performance results of the models by  Abdolbaghi et al. (2019)  and Laesecke and Muzney  (Laesecke and Muzny, 2017)  are stated and compared with our newly proposed models (MLP-LM and GEP) in Table  4  and Fig. 6. As demonstrated, our best implemented smart model (MLP-LM) outperforms the best prior model proposed by  Abdolbaghi et al.
2017 ) with an RMSE of 0. 016 compared to 0. 020. The correlations have notably higher RMSE than the machine learning models, but overall, they all have very high performance. It is worth noting that the proposed GEP-based explicit correlation can be applied more directly than less transparent intelligent schemes and hence, it can more easily be integrated into different software while studying and simulating tasks related to CO 2 utilization. With some programming, intelligent schemes can however also easily be implemented and applied. Hence, there is no practical limitation to the use of our best-established paradigm in this study, namely the MLP-LM model. For illustration, we have developed an Excel macro to allow its application and utilization. The procedure of using this Excel macro is described in Appendix A.
Conclusions In this paper, several machine learning techniques were applied to establish robust and simple-to-use models to accurately predict the viscosity of CO 2 under wide ranges of pressure and temperature conditions, using density and temperature as input parameters. A dataset of 1128 experimental points was used to calibrate and validate the models. Multilayer Perceptron optimized with four distinct back-propagation algorithms, including LM, BR, SCG and RB, and two robust 'whitebox' techniques yielding explicit correlations, namely GMDH and GEP, were the applied data-driven methods. The analysis revealed that MLP optimized with LM (MLP-LM) resulted in the best paradigm for predicting the viscosity of CO 2 with very low RMSE values: 0. 0012 during the training, 0. 0011 for the test data and 0.
Acknowledgments Pål Ø. Andersen acknowledges 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.
This work was funded by the FWV ELASTOPLAST Interreg Grant and the authors thanks the European Regional Development Fund (FEDER) and the University of Lille. Chevreul Institute (FR 2638), Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation and Région Hauts de France are also acknowledged for supporting and funding partially this work. Financial support from Région Hauts de France and FEDER for both SAXS laboratory equipment is gratefully acknowledged. Notes and references § The name and structure of the t-BuP1, t-BuP2 and t-BuP4 phosphazenes are provided in the SI section § § We also assessed an amine and an amide as co-initiator, but the results were less convincing than those obtained with benzyl alcohol. The experiments are provided in the SI section as Table  S2.
Experimental section The crosslinking reaction kinetics was monitored in situ using a Thermo Scientific Nicolet iS50 FT-IR spectrometer with a deuterated L-alanine doped triglycine sulfate (DLaTGS) detector in attenuated total reflectance (ATR) mode. To prepare the formulations, all components were pre-mixed at room temperature, and then cured in situ during heating from 25 to 180 °C at a selected heating rate (5, 10, 15 or 20 °C min -1 ). The FT-IR spectra were recorded each minute during heating. The absorption bands were recorded in the range of 600-4000 cm -1 with 32 scans and a resolution of 2 cm -1. The data were analysed using the OMNIC software. The conversion of functional groups at time t is denoted by α and defined by eqn (5). α ¼ A functional groups Aref 0 À A functional groups Aref t A functional groups Aref 0 ð5Þ The area of the absorbance peaks was calculated and reported for different times (A t ) after the initial time (A 0 ).
This paper concerns the experimental and numerical study of the plug-assisted thermoforming process of high impact polystyrene (HIPS). The thermomechanical properties of this polymer were characterized at different temperatures and deformation rates. To study the influence of different parameters in the real conditions of plug-assisted thermoforming process, we carried out "plug-only" tests at different temperatures and plug velocities. To model the deformation behaviour of HIPS, we proposed a thermo-elastic-viscoplastic model, which we have implemented in Abaqus software. A thermo-dependent friction model was also proposed and implemented in Abaqus software. The parameters of the proposed models were identified by the inverse analysis method in the real conditions of plug-assisted thermoforming.
They showed that the hyperelastic approaches do not give satisfactory results and suggest using temperature and strain rate dependent elastoviscoplastic models. O'Connor et al. [16]  developed a large strain thermally coupled (LSTC) material model for plug-assisted thermoforming of polypropylene yogurt container. They confirmed the requirement for the model to be fully thermomechanically coupled, and the process is most sensitive to plug-sheet contact friction and heat transfer parameters. Kittikanjnaruk and Patcharaphun  [17]  used commercial simulation package (T-SIM) to investigate the wall thickness distribution of HIPS and amorphous polyethylene terephthalate (A-PET) thermoformed parts. A nonlinear time-dependent K-BKZ model which was proposed by Kaye  [18]  and Bernstein et al. [19]  was used.
A lack of research concerning the deformation in more than one axis is evident. To address these issues, firstly, we conducted experimental study based on different mechanical tests to represent the deformations encountered during plugassisted thermoforming. The thermal dependence of the elastic behaviour was analysed using dynamic mechanical analysis (DMA). Uniaxial tensile tests at different temperatures and strain rates provided a broad experimental description of intrinsic behaviour of the material. The plugonly tests were used to give a more realistic behaviour of the complex deformation state encountered under and around the plug and during the plug phase. Secondly, we proposed a phenomenological modelling of the HIPS behaviour based on a thermoelastoviscoplastic formalism which was implemented in the finite element simulation code Abaqus via VUMAT subroutine.
The proposed model was inspired from G'Sell et al. model  [26] [27] [28] [29]  which is a phenomenological elastoviscoplastic law widely used to model the mechanical behaviour of different polymers  [30] [31] [32] [33] [34]. The effect of the contact between the sheet and the plug was considered by the temperature dependence introduced into the Abaqus simulation code via VFRICTION subroutine. The parameters of the proposed models are then identified using an inverse identification method. The proposed methodology was finally validated by comparing experimental and finite element simulation results of plugassisted thermoforming of a dairy packaging.
The elastoviscoplastic constitutive law was implemented in the commercial nonlinear finite element software Abaqus/Explicit through a VUMAT Fortran subroutine. Abaqus/Explicit provides the user subroutine VUMAT the strain increment for the current time step, the stress tensor and the temperature at the beginning of the current increment, the time increment, and a table of solution dependent state variables. The VUMAT subroutine computes and returns the value of the stress tensor and the state variables at the end of the increment for each point. EðT Þ ¼ E 0 exp A E 1 T À 1 T g s 0 ðT Þ ¼ s 00 exp A s 1 T À 1 T g KðT Þ ¼ K 0 exp A K 1 T À 1 T g hðT Þ ¼ h 0 exp A h 1 T À 1 T g ; 8 > > > > > > > > > > > > > > < > > > > > > > > > > > > > > : ð2Þ.