dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of the surge limit in transient test The setup of this line is carried out as follows: for a selected rotation speed, the valves after the compressor and before the turbine are adjusted to set an operating point close to mild surge. Then the compressor downstream valve is progressively closed until deep surge is achieved. Data are recorded for a duration of 5 s at 1 kHz acquisition rate. Fig. 6  presents the evolution of air flow and the pressure downstream the compressor P2c for an initial speed close to 80% Nmax. The beginning of the surge is determined from the RMS (Root Mean Square) or SD (Standard Deviation) calculation of the air flow signal. The SD shows a greater sensitivity  [14]  as shown in Fig. 7.
Surge limit evolution closing-opening Some experiments have been carried out for closing and opening of the electric throttle. Different sets of profiles have been performed from a wide open initial position and a compressor speed close to 80% of Nmax. For this speed, the opening of the valve downstream the compressor is set to obtain a point close to the mild stationary surge line. Test results presented are for setpoint profile according to Fig. 3. The measurements are recorded for 5 s at a data acquisition rate of 1 kHz. The turbine pilot valve being fixed, compressor speed increases when closing and decreases when opening, as shown in Fig. 9. Special care is needed during experiments to avoid overshoot of Nmax. The results of the measurements are presented in Fig. 10. It is possible from this figure to extract the surge loop with a time step of 0.
Acknowledgement We thank  Prof. L. González  for fruitful discussions. MF, CD and EG acknowledge funding from the  Agence Nationale de la Recherche  within projects  ANR-10-LABX-0026  ( Chimie des Systèmes Complexes ) and  ANR-15-CE29-0027-01  (DeNeTheor) as well as the  Frontier Research in Chemistry Foundation ,  Strasbourg. The calculations have been performed at the  High Performance Computer Centre (HPC) ,  University of Strasbourg , and on the cluster of the  Laboratoire de Chimie Quantique (CNRS / University of Strasbourg ). F. P and S. M. acknowledge funding from the  Austrian Science Fund (FWF)  within project  I2883 (DeNeTheor)  and the  University of Vienna , as well as the  Vienna Scientific Cluster (VSC)  for generous allocation of computing time. Support from the  COST action CM1305 (ECostBio)  is acknowledged.
Characterisation of the Powder and Monolith Catalysts The X-ray diffraction data of the powder and coated monolith supports were collected using a SIEMENS D5000 XRD diffractometer (SIEMENS, Munich, Germany), as explained in  [26] , using Cu radiation with a wavelength of λ Cu = 0. 15406 nm and the samples were scanned in a range of 2θ from 5 • to 90 • with the step size of 0. 04 • and step time of 2s. The diffraction patterns obtained were compared with the library data from JCPDS. Data handling was carried out using the software package HighScore. Specific surface areas and pore volumes for the prepared powder and monolith catalysts were determined by nitrogen adsorption-desorption isotherms at -196 • C, using an ASAP 2020 instrument (Micrometrics, Norcross, GA, USA). The surface area was calculated using the BET equation and pore size distribution was determined by using the BJH method.
After pre-treatment, adsorption of 15% NH 3 in He is done at room temperature for 60 min and followed by flushing with He for 30 min to eliminate the physisorbed ammonia  [43]. The NH 3 desorption was carried out from RT to 500 • C (5 • C min -1 ) for the powder-form AlSix. The concentration of desorbed NH 3 was analysed by a TCD detector. The total acidity of samples was determined by integration of the area between 50-500 • C by AutoChem software. The FESEM images for monoliths were obtained by a Field Emission Scanning Electron Microscope ZEISS ULTRA PLUS (ZEISS, Oberkochen, Germany) equipped with an Energy-Dispersive X-ray Spectrometer (EDS) at an accelerating voltage of 15. 0 kV. Prior taking images, the monoliths were cut to fit in the FESEM chamber and carbonized. This analysis was done to verify the morphology of the coated phase and its composition.
Acknowledgments: The authors are grateful to  Jorma Penttinen  and  Markus Riihimäki , whose were helping during this work in the characterization of the samples. The staff of the  Center of Microscopy and Nanotechnology (University of Oulu, Finland) and Institute of Chemistry of Poitiers, Materials  and natural resources ( University of Poitiers, France ) are acknowledged for the characterization as well. The work was done with the financial support of  PHC Volubilis  (No. 24618ZJ ), and  Oulu University Scholarship Foundation  ( Yliopiston Apteekin  rahasto).
Silver layers To complete the demonstration of the performance of this new spectrophotometric bench, we measured the transmission of several silver layers deposited on 1 mm thick silica substrates, characterized by increasing thicknesses from 23 nm to 256 nm. The deposition apparatus used to manufacture these samples is the HELIOS machine from the Institut FRESNEL's Optical Thin Films Group. 4 identical silica substrates were installed in 4 different substrate holders: after 26. 5 s of silver target sputtering under nominal conditions, a first substrate was extracted through the instrument's load-lock chamber. The second substrate was taken out after an overall deposition duration of 66. 2 s, the third after 132. 5 s and the last after 265 s. are defined by minimizing the root-mean-square deviation between experimental results and theoretical data (colored continuous lines) computed using the silver refractive index data published by Johnson  [23].
Acknowledgments The authors greatly appreciated fruitful exchanges with  Markus Tilsch  at VIAVI Solutions, as well as  Brian Sullivan  at  IRIDIAN Spectra Technologies Ltd , and would like to thank the  Optical Thin-Film group of the Institut FRESNEL , especially  Thomas Begou  and  Julien Lumeau , for the manufacture of the qualification band-pass filter and the deposition of the silver layer trial samples.
Acknowledgements This work was partially supported by the  FWF   Start award  ( Y391 ), the joint  ANR / FWF  project , French state funds managed by the joint ANR Blanc International programme PACS  under reference  ANR-13-IS04-0006-01  and  FWF I1392 , and  Generalitat de Catalunya  under project  2014 SGR-581. HRC is a member of  CONICET (Argentina). K. A. -W. is grateful to the  Austrian Science Fund FWF  ( Firnberg Award   T463 ). LEB thanks the  Swiss National Science Foundation  through the  FNS Grant   200021-149847.
g. benzoate and hydrocinnamate) as will be reported in due course. We acknowledge support from the Australian Research Council (DP150101388) and the National Computing Infrastructure. Financial support from European Community's Framework Programme Horizon 2020 (CALIPSOplus, under grant agreement 730872) is gratefully acknowledged. ML thanks NIU graduate student travel award for funding his trip to conduct research at the University of Melbourne. We thank Victor Wan for carrying out preliminary DFT calculations.
Chemical equilibria will be listed in the next section involving the aqueous species listed in the Table  1. Despite its presence in solution has been demonstrated by Schmid and Krichel 6 in 1964, nitrogen monoxide will not participate at these equations. More recently in 2010, Binnewies et al. 7  explained that nitrous acid cannot be pure in solution and that it would then form equilibrium with HNO 3 and NO. This article presents concentrations of the species with molarities while most of the scientific articles deal with molalities. Conversion involves the density of the solutions. The density of the nitric acid solution varies not only with the acidity of the solution but also with the temperature of the medium. The data are repertoried in international databases. 8. 2. Chemical Equilibria of Aqueous Solutions of Nitric Acid.
4 1. 4 + + + + + = - X X X X X X C X Nitrogen atoms conservation: 2 (3. 2. 28) + + + + + + = + - X X X X X X X C X Oxygen atoms conservation: 3 3 2 (3. 2. 30) The Scilab software 34 was used to solve this system using an iterative method. Equilibrium constants or standard enthalpy calculations present uncertainties. However, because of a lack of information on uncertainties on thermodynamic data of reaction (equilibrium constants) and compound (standard enthalpies), unfortunately not always associated with the data found in the literature, it was impossible to determine uncertainties on our modeled values. The system of equations presented is therefore not strictly correct. The use of about 500 iterations allows getting close to the resolution of the system of equations. The evolution of the concentrations of HNO 3 , NO 3 -, and H + according to the variation of the temperature and the acidity of the nitric solution is presented in Figure  4.
CONCLUSION To conclude, no reliable and valid data on the concentrations of nitrogen species in solution as a function of temperature and acidity were available for nitric acid so far. This work deals with the understanding of the nitric medium and more particularly with the calculation of the concentrations and real activities of the ions in solution for large ranges of total nitric concentrations and temperatures from thermodynamic data listed in the literature. It was first demonstrated that at equilibrium, the concentration of nitrous acid decreases only when acidity increases, but the variation of the temperature of the medium from 50 to 95 °C had no impact on these concentrations. In addition, after having posed and solved, with the help of the software Scilab, a system of 8 equations with 8 unknowns, it was possible to determine the concentrations of aqueous species at equilibrium.
Outdoor sampling sites and weather data The four locations were sampled twice, in summer 2016 and in winter 2017 in order to study the effects of seasonal variations. Air sampling took place at each site during a total of 6 h. For PLFAs analysis, air samples were collected in triplicate every 2 h each of 2 h duration (i. e. every 2 h 3 samples were collected so in total 9 samples). MVOCs were also sampled at the beginning of each 2 h period in triplicate so again 9 samples were collected. Weather data (wind speed, temperature and relative humidity) was monitored on site, throughout the 6 h period with a Kestrel® weather station. The description of each sampling site, dates, time and weather conditions are detailed in Table  1.
Data analysis PLFAs data from the chromatograms obtained from the GC-FID were processed in ChemStation. Individual PLFAs were designated to microbial groups as reported by Willen et al. (Willers et al. , 2015). Chemometrics from the chromatograms obtained from GC-MS analysis were processed in batch using OpenChrom. Prior to the identification and quantitation of the VOCs, first the background of the chromatograms was subtracted. MVOCs were differentiated from VOCs using the Kegg Pathway (Kanehisa  Laboratories, 1995)  and mVOC  (Lemfack et al. , 2014)  metabolic databases as well as in the recent literature  (Schenkel et al. , 2015; Hertel et al. , 2016; Micheluz et al. , 2016). Descriptive statistics and pie charts were performed with Excel 2010. Correlations between MVOCs chemical groups were performed by Draftman's plots in Primer 6 Betta 11 and ANOVA analyses were performed with Statistica 12.
5. Heat Maps were produced using ClustVis  (Metsalu et al. , 2015)  to investigate the possibility of differentiating the 4 locations with MVOCs and PLFAs biomarkers. Relationships between PLFA and MVOC biomarkers were tested using correlation analysis carried out with the 'corrplot' R package  (Wei and Simko, 2017). Spearman coefficient was computed to estimate a rank-based measure of the association. The significance of each correlation was evaluated using 'cor. mtest' function and the non-significant correlations were removed from the analyses. Correlations were represented in correlograms. Canonical correspondence analyses (CCA) were performed in order to identify multivariate relationship between weather data (wind speed, temperature and relative humidity) and biomarker concentrations (MVOCs and PLFAs).
Only biomarkers measured in both summer and winter sampling times were used with the weather data of the different locations during these periods. The analyses were performed using the CCA function available in the 'vegan' R package  (Oksanen et al. , 2017). CCA biplots were made using symmetric scaling, both biomarker and weather scores were scaled symmetrically by square root of eigenvalues to preserve the features of biplots without focusing on one set of scores.
Experimental results The available experimental data obtained from quasi-static and dynamic tests were used as database to identify and compare the response of the considered models below. Figure  1  shows the pronounced strain rate dependence on the material: we observed evolution of the engineering stress with increasing strain velocity till a pure elastic response. The technique described in  [23]  was used to identify the time-independent equilibrium hyperelastic response, graphically shown in Figure  2. Relaxation tests were intended to determine the relaxation kernel, including large characteristic times. Observations in Figures  3  and 4  confirm the separability assumption: the phenomenological behavior of the considered material is only time dependent none deformation dependent  [2, 24, 25].
Results and discussion The applied procedure to identify material's parameters in an integral-based constitutive viscoelastic relationship relies on a stress decomoposition into a long-term response and an instantaneous part intended to deal with strain-rate dependence. In the present work, identification has been conducted with the "Evaluate" module of the finite element solver Abaqus 6. 14  [29]  and we wont report identification results. It is to underline that for the monotonic tests, at high strain values, a third order Ogden model  [4]  presents more accurate results for the pure hyperelastic response. To compare moderate strains, a simple Mooney-Rivlin leads to sufficient results (Tab. 1). On the comparison results, we firstly present results for the monotonic tension response, as shows Figure  8.
This observation is clearly revealed for the curve at 10% of deformation. Fosdick & Yu and Simo models are seen to slightly underestimate the experimental data, for low and high strains. At the beginning of relaxation, it is observed that the Simo model approximates better the experimental data (Tab. 2). The comparison of the resulting complex shear modulus is evaluated in term of shear storage modulus and loss factor for all the models under consideration. We firstly report on the results concerning the shear storage modulus, which are shown in Figures  11  and 12. Elastomers show a frequency dependent dynamic behavior. Increasing frequency leads to increasing the shear storage modulus in our frequency range. All investigated models exhibit this dependence. At 10% of predeformation, both Christensen and Fosdick and Yu models estimate the experimental data with good accuracy, while Simo model overestimates this modulus.
Acknowledgements Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur et de la Recherche ,  Région Nord-Pas de Calais  and  FEDER  are acknowledged for supporting and funding partially this work. K. Sadouki  et al. DOI: 10. 4236/ampc. 2018. 83008118 Advances in Materials Physics and Chemistry.
Pervaporation The transport and separation properties of the CAP-RIL membranes formed were evaluated during a vacuum pervaporation (VPV) process. The experimental setup has been presented and described in detail elsewhere  [31, 32]. Prior to measurement, the setup was running for 1h to reach a stationary state at a given concentration and temperature. During the experiments, the membrane was in contact with feed solution containing 7-14 wt % of water in aqueous propan-2-ol mixture. All measurements were realized at 35 • C. The compositions of feed and permeate solutions were evaluated using gas chromatography (Varian 3300 gas chromatograph with a TCD detector, Varian, Inc. , Walnut Creek, CA, USA)  [31, 32]. Borwin software (version 1. 21. 07, JMBS, Grenoble, France) was employed for the data acquisition and processing.
Acknowledgments: This project received the founding from the  Polish National Science Centre  (grant agreement No. DEC-2015/18/M/ST5/00635 ) and was supported by  Hubert Curien's Partnership Program "Polonium"  ( 35501/2016 ). Edyta Rynkowska  acknowledges the financial support of the  French Government  and the  French Embassy in Poland  (scholarship  848642E ,  878205J ).
Oxygen concentrations were changed by a nitrogen-air distributor. DO sensor calibration and the characterization  [35]  was done using the 8-channel potentiostat (1030A Electrochemical Analyzer, from CH-Instruments). The pH sensor calibration was done in different pH solutions and a commercial pH-meter was used for the correlation of the obtained potentials (GLP22, from Crison). The calibration of the pH and µISE sensors were done using home-made data acquisition system set-up with four multichannel microelectrodes to measure open circuit potential, connected and controlled by a personal computer. For all calibrations was used a magnetic stirrer to quickly homogenize their concentrations. For the experimental validation, the measured Na + , K + ions of different sample of mouse urine were correlated using an atomic spectrometer (929 Solar AA Spectrometer, ATI Unicam, from Prince Technologies B.
Acknowledgements This work was partially done with the support of the Spanish government funded projects  DPI2011-28262-C04  and  DPI2015-65401-C3-3-R (MINECO/FEDER ,  EU)  and  BFU2007-62119  (MINECO). Also, the authors acknowledge the financial support from the  European Union 's  Horizon 2020 research and innovation programe entitled MicroMole and HEARTEN  grant agreement No  653626  and No 643694 respectively. The authors also acknowledge ICTS "NANBIOSIS", more specifically by the  SU8  Unit of the  CIBER in Bioengineering, Biomaterials & Nanomedicne (CIBER-BBN ) at the  IMB-CNM (CSIC). This work has also made use of the  Spanish ICTS Network  MICRONANOFABS partially supported by  MEINCOM.
Otherwise, a humidity correction can also be foreseen  [28]. Humidity might be construed as an additional source; accordingly, adding a source term should compensate for the different values of humidity. This is not the aim of the actual study but this offers potential for future work. To reach this goal, we acquired data with synthetic mixtures. The acquisition was divided into four steps. The first step was the preparation of the mixture. Thanks to the dilution of calibrated gas mixtures (produced by supplier), we obtained different samples. Then, we injected the mixture in an analytic cell of 200 mL in which the sensors were disposed. The gas flowed into the cell with a continuous flow rate of 500 mL/min. An exhalation contains between 500 mL and 2 L of air, as in the case of forced exhalation.
In addition to its rapid acquisition, the peak height varied according to the gas concentrations and, consequently, provided information about the mixture composition. Thus, for each sample of gas mixture, we acquired data in the air buffer alone (V 0 ) and in mixtures diluted in the air buffer (V g ). V 0 , depending also on the temperature sensor, was also taken on the peaks heights. We calculated the resistance of sensitive using the equation given by the constructor  [34] : R g = R L 4. 55 V g -1 (1) with R L a load resistance in the electronic editing. Data were acquired several times and we kept the mean values. The error bars were computed from several measurements, between 2 and 4 measurements, in the same configuration for a given temperature with the same sensor. At temperature T i , we noted the measure x i (output of the MOX sensor), defined as the ratio of resistances (2): x i = R 0 R g T i (2).
Model coefficients were estimated using experimental data with different mixtures, which required a long time and a high degree of accuracy. Instead of this supervised approach, future work will consider approaches based on blind source separation  [46] [47] [48]  for estimating the coefficients of the model without knowing exactly the concentrations of the mixtures. This will extend the preliminary results presented in Madrolle et al. [42, 49]  to other source separation methods and to sources with low concentrations. In addition, we will increase the complexity of the mixture by adding gases, to be closer to the complexity of human breath. The goal is to apply the proposed method directly to human breath. Also, humidity, naturally present in human breath, might be taken into account as an additional source, defined by a non-integer power of the gas concentration and included in a second order polynomial model.
Due to this slowing down in the exchange of Cu I coordination to the phosphorous atoms of the ligand, we observed a decrease in deshielding effect of the Csp 3 -H proton (Figure  SI-8 ). The experimental charge density was refined using the Hansen & Coppens multipolar atom model with program MoPro  [30]. X-H distances were constrained to standard neutron diffraction values. [31] The C1-H1 distance was set to d=1. 099 Å. The Cu atom was modelled up to hexadecapole level. Using this model, the X-ray structure of 2 with toluene solvate (2') resulted in a very low R-factor of 1. 890%. (Table  2 ). The experimental Laplacian density (Figure  7 ) map and the Cu I ꞏꞏꞏH bond critical point properties of complex 2' were obtained from high-resolution X-ray diffraction data and compared with the theoretical ones.
Full-sphere data collection was used with ω scans. Programs used: Data collection and data reduction, CrysAlisPro 1. 171. 39. 12b  [32]  and absorption correction SADABS-2014/5. [33] Structure Solution and Refinement: Crystal structure solution was achieved using the VLD procedure implemented in SIR2014  [34]. Spherical model refinement was done using the program ShelXle  [35]. All nonhydrogen atoms were refined including anisotropic displacement parameters. Copper(I) chloride, copper(II) chloride, copper(I) tetrakis(acetonitrile)triflate, copper(I) tetrakis(acetonitrile)tetrafluoroborate, diphenyl-phospine chloride (99% purity), diisopropylphosphine chloride (98% purity) were purchased from Merck (Sigma-Aldrich) and used without further purification. Unless otherwise stated, all other reagents were purchased from commercial sources and used without further purification.
For CuCl 2 , 0. 8 mL of MeOH were used instead. All obtained complexes can be isolated by recrystallization in air from the concentrated reaction mixture by addition of tBuOMe or hexane or by slow diffusion of diethyl ether. For the systematic studies, due to the low solubility of some compounds in CDCl 3 , all NMR data were obtained in CD 2 Cl 2 purchased from Merck (Aldrich). [HTIM(PPh 2 ) 3 Cu(NCMe)]BF 4 (1). Yellowish crystalline solid obtained in 89 % by recrystallization from CH 2 Cl 2 :tBuOMe. [HTIM(PPh 2 ) 3 Cu]Cl (3). Yellow crystalline solid was obtained in 70% yield after recrystallization from CH 2 Cl 2 :hexane. 1 H NMR (400 Hz, CD 2 Cl 2 ) δ 11. 20 (q, 1H, 4 J H-P = 10. 3), 7. 62-7. 58 (m, 6H), 7. 49-7. 48 (m, 3H), 7. 32-7. 29 (m, 3H), 7. 18-7. 15 (m, 6H), 7. 03-6. 98 (m, 6H), 6.
Acknowledgements We would like to thank the  European Commission  for the ERC-CG-2014-648304 (J. Ll. -F) project. We also thank the financial support from  ICIQ Foundation and CELLEX Foundation  through the CELLEX-ICIQ high throughput experimentation platform and the  Starting Career Program  is gratefully acknowledged. We also thank  CERCA Programme (Generalitat de Catalunya)  for financial support and  MINECO (Severo Ochoa Excellence Accreditation   2014-2018 ;  SEV-2013-0319 ) and  MINECO  project  CTQ2014-52525-P.