dataesr/scientific-paragraphs-categorization huggingface dataset:
As discussed earlier (Sec. 3. 2) the low NO region is the consequence of local extinctions and oxygen scarcity. Along the inner branch (B1), NO profiles from F1 and F1-Inst are in agreement, as noted from Fig. 15a. However, F3 and F6 detections show higher NO concentration which is attributed to the PAH interference and associated unconditional subtraction errors, as discussed in Sec. 3. 2. Along the outer branch (B2), NO concentrations obtained from F3, F1, and F1-Inst are in close agreement, as evident from Figs. 15b  and 16. The NO concentration in B2 (Fig. 15b ) with F6 is consistently lower than that of other detections (F3/F1/F1-Inst). This is again attributed to the PAH subtraction errors. In the low PAH region (near  = 55 ), NO concentration from F6 converges to the rest of the datasets as observed from Fig.
15b. In all the plots (Figs. 15  and 16 ) for  < 60 , the NO profiles of F1 and F1-Inst are almost identical with a mild difference between the flame branches as noticed in radial profiles (Fig. 16 ). As discussed earlier in Sec. 3. 1, NO data beyond  > 60  is susceptible to errors due to intense soot-LII noise. Nevertheless, for relative comparison, we evaluate the NO concentration in sooty regions ( > 60 ), as shown in Fig. 15c. These profiles can be compared with the mean NO obtained from data conditioned on instantaneous B2 branch (plotted in Fig. 17 ). Such evaluation does not require detuned subtraction (detailed subsequently), and thus, serves as a reference to assess the accuracy of different approaches. The comparison of Figs. 15c and 17, shows that both the F1 and F1-Inst lead to appreciable error in sooty regions.
The detuned subtraction approach relies on the assumption that the interference intensity across tuned and detuned acquisitions remains statistically stationary. However, any drift in experiments (laser wavelength and fluence, camera dark-and shot-noise, flame structure) will void this condition. The PAH-LIF interference is less sensitive to the experimental drifts than the soot-LII interference. Soot can be highly intermittent and localized. The soot-LII response to the laser fluence is highly non-linear. Thus, the mean detuned signal across tuned and detuned campaigns can vary, resulting in erroneous NO concentrations. The NO profiles from F1 and F1-Inst in Fig. 15c  diverge when  > 60  despite the same experimental dataset. In the F1 approach, the unmapped mean detuned signal subtraction assumes that the flame is statisti-cally stationary.
The plateau is also evident from the radial profiles in Fig. 16. The NO is spatially biased towards the oxidizer side, instead of being distributed equally on either side of the mean flame-front. This may be due to the availability of nitrogen and oxygen on the oxidizer side of B2. The axial growth of    along B2 saturates at a certain height (see Fig. 14 ). The similar observation is noted at a higher height ( > 60 ) in instantaneous images (Fig. 13 ). We also deduce the mean    conditioned on the B2 branch from instantaneous images to demonstrate the NO growth saturation conclusively. Recall, the mean    field (Fig. 14 ) could not be evaluated for  > 60  due to LII interference. Fortunately, in the B2 branch, both the PAH and LII interferences are not significant (see Figs. 8  and 12 ) with F1 dataset.
The NO concentration along the outer branch (B2) increases up to  = 60 , beyond which the NO growth decelerates, eventually leading to 15% reduction in NO at  = 76. This is due to a possible decrease in flame temperature owing to the radiation from soot particles. The mean    showed nearly identical spatial distribution as of  [7] , although values differed slightly. Nevertheless, the differences between all four detections (F6, F3, F1, F1-Inst) are within the uncertainty (i. e. , ±30%). F1-Inst data is the most reliable due to the narrowband detection and mapped PAH subtraction. This strategy provided access to    even near sooting regions    Spectrum is simulated in synthetic air at an atmospheric pressure using a CORIA LIF model  [7, 23]. (0, 0)-(0, 6) denote the NO fluorescence bands of - excitation.
To facilitate the comparison, axial and radial NO profiles are plotted for all the datasets namely, F6, F3, F1, and F1-Inst. The axial profiles are obtained by spatially averaging the data within c = 0. 45 to 0. 55 region. The deduced profiles are shown in Fig. 15 along with the uncertainty (±30%). The radial profiles are plotted in Fig. 16 at  = 38  and  = 55. These profiles are obtained by averaging the data within  ± 0. 5  region. The profiles in both Figs. 15 and 16 are smoothed (using a Savitzky-Golay filter) for better visualization. The raw (unfiltered) profiles are provided in Appendix D.
> 60 ). The fuel droplet and flame interaction lead to the formation of composite reaction zones. Consequently, NO in the spray flame forms through different mechanisms. The mean    along the inner composite branch (B1) is nearly constant (20 -30 ) with height, suggesting non-thermal routes of NO formation. In B1-P (lean-premixed), nitrous oxide route could be prevalent, while in B1-D (diffusion) prompt route of NO formation is likely. A region of low NO in the vicinity of B1 is attributed to local extinctions, as confirmed by instantaneous NO fields. In the outer diffusion branch B2, the thermal mechanism of NO formation appears to be dominant as evidenced by the NO growth. along 2, increases from 35  at the flame-base to 75  at  = 60. The present work contributes to the spray combustion field on the following three aspects, ) NO measurement and data reduction strategies, ) database enhancement of mean NO concentration, and ) understanding of effects of local flame extinctions, soot radiation, and combustion mode on NO through instantaneous and mean quantitative imaging of NO concentration.
available. The 𝑄 1 ( 6 )Fig. 3. 163 Fig. 3. The broadband (F6) NO-LIF measurements were performed with a different laser than the F3 and F1 detections. The mean fluence of NO lasersheet for F3 and F1 datasets was 0. 05 / 2 , while the fluence in the previous F6 dataset [7], was 0. 02 / 2. Linearity of the NO-LIF signal intensity at the present fluence (0. 05 / 2 ) was confirmed. The fluence in our previous work was limited by the available laser. The higher fluence in the present work enhanced the LIF signal quality compared to the prior measurement [7],.
(  > 60 ). The fuel droplet and flame interaction lead to the formation of composite reaction zones. Consequently, NO in the spray flame forms through different mechanisms. The mean    along the inner composite branch (B1) is nearly constant (20 -30 ) with height, suggesting non-thermal routes of NO formation. In B1-P (lean-premixed), nitrous oxide route could be prevalent, while in B1-D (diffusion) prompt route of NO formation is likely. A region of low NO in the vicinity of B1 is attributed to local extinctions, as confirmed by instantaneous NO fields. In the outer diffusion branch B2, the thermal mechanism of NO formation appears to be dominant as evidenced by the NO growth. along 2, increases from 35  at the flame-base to 75  at  = 60. The present work contributes to the spray combustion field on the following three aspects, ) NO measurement and data reduction strategies, ) database enhancement of mean NO concentration, and ) understanding of effects of local flame extinctions, soot radiation, and combustion mode on NO through instantaneous and mean quantitative imaging of NO concentration.
Modeling and simulation results Modeling and simulations are essential steps to be considered during the design and optimization of the testing chamber. Due to the complexity of the gas transport mechanism, it is compulsory to determine its dynamics inside the testing chamber. This study allows us to determine what is happening at the sensor surface level, in term of gas flow velocity, gas flow direction, gas concentration diffusion, etc. For as followed, we assume that the sensor dynamic range is very large compared to that of the flow (i. e. in relation to the temporal and spatial evolutions of the flow transporting the target gas). Additionally, we assume that the sensor does not have any influence on the nature of the gas flow. To know the velocity as well as the path of the gas flow inside the two chambers, we used the multidisciplinary STAR-CCM+ software  [30] , which is able to simulate complex industrial problems or physical phenomena.
The temperature was raised in steps of 50-100 C up to 450 C and each temperature was held for 30 min. X-ray absorption spectra at Cu K-edge for all the samples were collected in transmission with the exception of the catalyst with Cu/Al ¼ 60/30, mol. ratio, which was collected in uorescence mode. At least 3 spectra for each sample were taken at room temperature, appropriate temperatures and aer reaction at room temperature. CuO reference was measured at room temperature in pellet form. (The data were analysed using the Demeter soware package  11, 12  ). IR spectra were recorded with a Tensor 27 Bruker spectrometer equipped with an MCT detector. Prior to FTIR studies selected mixed metal oxides were pressed into the form of selfsupporting wafers (ca. 5-10 mg cm À2 ) and pretreated in situ in a homemade quartz IR cell at 400 C under vacuum conditions for 1 h.
Acknowledgements The authors acknowledge the  Federal Ministry of Education and Research (BMBF)  for funding in the frame " Material Innovations " within the research project "Efficient DeNOx-strategies for lean-operated combustion engines" ( BMBF-PTJ FKz 13XP5042A ). This work was partially funded by the  Excellence Initiative of the German Federal and State Governments in the frame of the Center for Automotive Catalytic Systems Aachen (ACA)  at  RWTH Aachen University  by the Grant No. 2015/18/E/ ST4/00191  from the  National Science Centre, Poland  and by  EU structural funding in Operational Programme Research, Development and Education , project No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000853  " IET-ER ". The authors acknowledge the  Diamond Light Source  (project  SP14834 ) for the provision of beamtime on the beamlines  B18 , and  Diego Gianolio  for assistance in performing the XAFS measurements.
The corresponding ASR values are 2. 76 •cm 2 and 4. 80 •cm 2. In this case, the above mentioned fluctuations of the stack voltage cause significant different ASR values. Therefore, this method is not suitable for the reliable determination of the ASR. An alternative method is the enlargement of the interval. For the so-called global ASR the interval is spanned over the entire j-V curve so that the error of the interval method is reduced as best as possible. This method leads to an ASR of 2. 88 •cm  2. Another method which is very similar to the global ASR is the linear regression which is mathematically fitted to the j-V-curve. The corresponding algorithms are already integrated in most of the established software programs, e. g. "Microsoft Excel" or "OriginLab". The ASR can be calculated with the corresponding linear equation, which has a result in this case of 3.
Hence, a high reproducibility between jV-characterization and EIS is obtained by consequent application of the above mentioned pre-normative test procedures "Test Module 03: Current-voltage characteristics"  18  and "Test Module 04: Electrochemical impedance spectroscopy"  19  of the European project SOCTESQA. Operation under constant current. -Figure 12  presents the electrolysis voltages of three stacks (#10, #11, #12) tested in different laboratories recorded during 500 h of operation. The stacks were operated in SOEC mode at 750°C with a fuel gas mixture of 80 vol. % H 2 O + 20 vol. % H 2 and air at a current density of -0. 5 A/cm 2. The data acquisition period was 300 s. Figure  12  shows that the applied electrical current value of stack #11 and stack #12 is very reproducible and stable at -0.
LSMN n= 2 study The LSMN n= 2 material exhibits a Ruddlesden-Popper (RP) structure with 2 layers of octahedra in the perovskite-like stack and tetragonal space group I4/mmm (No. 139). A total of 24 sets of ~3 g of corresponding material, were synthesized as described in the Experimental Section. For each synthesis, the lattice parameters (a=b, c) and the unit-cell volume were determined from the XRD data using structure refinement based on the Full Pattern Matching -Le Bail method. The comparison of the results allowed the selection of the samples to be mixed (or discarded), obtaining a final homogeneous stock of around 40 g of pure phase material (see Supplementary Information; Figure  S1  and Table  S1 ). The structure of the as-obtained LSMN n= 2 powder was successfully refined as a single phase using Rietveld method based on X-ray diffraction data and pseudo-Voigt peak shape function with reasonable reliability factors of Rp= 4.
FULL PAPER intrinsic parameters (surface features,  [63]  mechanical stresses and strains,  [64]  wetting angles,  [65]  defects such as vacancies and dislocations  [66, 67]  ), affecting the formation (nucleation) and growth of the Ni particles,  [33]  it was considered in this study only the main effects of reduction temperature (T) and time (tr). The influence of T and tr on the Ni particle size was examined using a precise image analysis of the scanning electron micrographs (FE-SEM) based on about 100 particles to obtain a characteristic frequency histogram in each selected (T, tr) conditions. The grouped particle size data were analyzed using as likelihood fitting method a lognormal distribution [Eq. 2] as reported in the literature  [68] [69] [70]   (2) In this case, A is the area of the size distribution, w the scale parameter defining the width of the size distribution (multiplicative standard deviation) and D 0 the median diameter.
The average particle size (D ̅ p ) of the lognormal distribution and its standard deviation (σ ln ) were determined using the following equations: The results of the 14 lognormal distributions are shown in Supplementary Figure  S8  and they were used to determine the statistical significance of the different studied variables (T and tr) on the average Ni particle size (D ̅ p ). A statistical ANOVA analysis was performed through the Generalized Linear Models available in the STATGRAPHICS Centurion XVII package with a confidence level of 95 % and a Square Sum Type III analysis, allowing the measurement of the contribution of each variable. The results are presented in Table  2 , showing that p-value was lower than 0. 05 for both cases, which means that tr and T have a statistically significant influence on the average Ni particle size in perfect agreement with Gao et al.
Characterization Each synthesized powder (~3 g per synthesis) was characterized by X-ray diffraction (XRD) at room temperature (RT) using a BrukerD8-ADVANCE powder diffractometer operated in Bragg-Brentano geometry, equipped with a Lineal LynxEye detector and a beam of CuKα1,2 radiation (λ= 1. 5418 Å). The diffractometer was operated over the angular range 2θ= 2 -70° for qualitative analysis and 2θ= 2 -90° for Rietveld analysis with a measurement step of 0. 020353° (2θ). The X-ray diffraction data were processed using JANA 2006 software package. [93] The elemental analysis was confirmed by X-Ray Fluorescence spectroscopy (XRF) in an S2 Ranger Bruker spectrometer equipped with a Pd X-ray tube.
Finally, the last zone is the analysis one, integrated by a gas chromatograph (GC, SRI instruments 8610C) using He (grade 5. 0, CRYOGAS) as mobile phase, equipped with two packed columns (molecular sieve 13X 6 in and hayesep D 6 in), a thermal conductivity detector (TCD) and controlled by PeakSimple 4. 44 free software. Operating conditions: Around 100 mg of catalyst (LSMN n=2) was diluted in SiC (SiC:LSMN n= 2 10:1 weight ratio) and introduced in the reactor as fixed-bed between catalyst-free SiC and two pieces of quartz wool. Prior to the behavior test, the catalyst sample was reduced in situ (55 mL (STP) min -1 of 3 mol% H2/N2 mixture, Cryogas) according to the results obtained in the reduction study. The catalytic behavior was measured during 8 h at the same reducing temperature under atmospheric pressure.
Acknowledgements The authors acknowledge the financial support of the  Colombian Administrative Department of Science, Technology and Innovation COLCIENCIAS  (Project #  110265842833  " Symmetrical high temperature Fuel Cell operating with Colombian natural gas " (contract #  038-2015 ) and  S. Vecino-Mantilla´s Ph. D. scholarship (call # 647 )) and of the  Spanish National Research Council CSIC  (Project #  COOPA20112 ). The authors are also grateful to  UIS' X-Ray Laboratory (Parque Tecnológico Guatiguará)  for XRD measurements,  UPV´s Electronic Microscopy Laboratory  for the FESEM analysis, and finally to  Margarita Vecino-Mantilla ,  Carolina Cárdenas-Velandia ,  Santiago Páez-Duque ,  Ivan Suárez-Acelas  (UIS),  María Fabuel  ( UPV ) and  Olivier Gardoll  ( UCCS ) for their contribution to materials synthesis and characterization.
Acknowledgements The authors are indebted to the participants, the  Mutuelle des Salariés Agricoles (MSA)  occupational doctors, and  Marion Hulin  and  Franck Vibert  that managed the field survey. This publication arises from the project FERMA supported by The  French National Research Program for Environmental and Occupational Health of ANSES  ( EST-07-71 ) and by  MSA Region Auvergne. This manuscript was reviewed by  Error!Bookmark not defined. , Center for Occupational and Environmental Health (COEH) ,  University of California, Davis.
Formation enthalpies and entropies of both hydrogenation and dehydrogenation were calculated using Van't Hoff method  [6, 30, 37, 38]. The HVPA-200 Sievert's apparatus was also used to study the hydrogenation kinetics of the composites. Hydrogen absorption isotherms were performed at various temperatures (50, 75, 100, 150, 200, 300, 325 and 350 °C) under 30 bar of H2. Before the isotherms, the powders were degassed under vacuum at temperatures between 200 and 300 °C (the lowest temperature possible based on the TPD data to ensure the complete dehydrogenation of the sample). The apparent activation energy of the hydrogenation process was determined using the Johnson-Mehl-Avrami-Kolmogorov model as described in the ESI (eqns. S1 and S2)  [11, 13, 15, 39]. Finally, in order to study the reversibility of the formed storage systems, dehydrogenation/ hydrogenation cycles were performed under the following temperature and pressure conditions: dehydrogenation under vacuum at 200 °C and hydrogenation under 30 bars of H2 at 100 °C.
Acknowledgements This work was supported by the  French National Research Agency (Agence Nationale de la Recherche , Projet  ANR-15-CE29-0023-01 ). The authors are thankful for the scientific services of IRCELYON, particularly  Y. Aizac  for XRD analyses,  C. Lorentz  for NMR analyses,  L. Cardenaz  for XPS analyses and  M. Aouine  for TEM analyses. They also thank  CNRS  for support and  Dr Yannick Coppel  at  LCC  for solid state NMR measurements.
The samples were pressed into pellets of 13 mm of diameter, whereas for Cu 2 O and CuO references cellulose was used as binder and diluting. The Extended X-ray Absorption Fine Structure (EXAFS) parts of the spectra were collected with a variable sampling step in energy with Dk ¼ 0. 05 ÅÀ1 up to 15 ÅÀ1. For each sample, three consecutive EXAFS spectra have been collected and been averaged for data analysis. The extraction of c(k) function has been done using Athena and EXAFS data analysis has been performed with Artemis soware. 27 Fourier transform of EXAFS function c(k) into R space with k 3 weighted factor and Kaiser-Bessel window function has been performed in 2 to 15 ÅÀ1 , yielding a function l c(R)l ( ÅÀ4 ). The input les, which contain the information like crystal structure, lattice parameters and space group, given to Artemis were taken either from ATOMS or ICSD (Inorganic Crystal Structure Database).
The SAXS instrument is directly attached windowless behind the sample chamber. The small-angle scattering was recorded using an area sensitive Ar gas-lled multiwire proportional counter (200 Â 200 mm 2 ). These raw data were corrected for dark current, dead time, and sensitivity of the detector as well as by the incoming photon-ux, sample transmission, scattering background, and geometrical effects like the projection of the detector plane on the sphere with radius equal to the sample-detector distance. The relative sensitivity of each detector pixel was determined by collecting the isotropic uorescence of an appropriate metallic foil (Zn foil at the Cu-K edge) and subtracting the small-angle scattering of this foil measured below the Zn-K absorption edge. The scattering background coming from all components in the beamline was measured before each sample run by moving the sample out of the beam.
The sample transmission was measured directly before each scattering measurement using an X-ray sensitive diode (Hamamatsu S2387-1010R). This scattering and sample transmission sequence was repeated at any of the four chosen X-ray energies (8597, 8933, 8957 and 8975 eV) for ASAXS. The whole sample and energy sequence was three times repeated by statistical reasons. The data reduction was done by an instrument specialized data reduction soware "SASREDTOOL" to get the nal scattering curves. For the data analysis SASt 30 was used to get the structural model. It turned out the partial structure could be modelled by a formfactor of homogeneous spheres and a Voigt behaviour of the scattering peaks that are arising from the regular framework structure. Further analysis of SAXS and ASAXS and some necessary theory like the calculation and analysis of the resonant scattering functions are described in the ESI.
Acknowledgements The authors thank  Dang-Lanh Hoang  for technical assistance and fruitful discussions,  Andreas Martin  for discussions,  Matthias Schneider  for performing and discussing the XRD investigations. We thank the  Leibniz Institute for Catalysis, Rostock, Germany  for the support and the possibility to perform the TPR and the XPS investigations. We thank the staff of the  ESRF , especially of  BM23  and the staff of  HZB/BESSY II  for technical assistance and providing beamtime.
§ XRD pattern was simulated with "Database of Zeolite Structures": http://asia. iza-structure. org/SC/pow_pat. php?STC¼AFI&ID¼AFI_0"; downloaded at 2018-09-21. This journal is © The Royal Society of Chemistry 2019 RSC Adv. , 2019, 9, 6429-6437 | 6433.
Acknowledgments The authors would like to acknowledge the  National Council for Scientific Research of Lebanon (CNRS-L) , the  University of Montpellier (UM)  and the  'Agence Universitaire de la Francophonie' (AUF)  for granting a doctoral fellowship to  Rouba Alrammouz. The author  Roland Habchi  would like to thank the  scientific research program  of the  Lebanese University  for the research grant.
7 eV). The acquisition was operated at a pass energy of 20 eV for the individual spectral lines and 50 eV for the extended spectra. The analysis of the spectra has been performed with the Spectral Data Processor v2. 3 software using Voigt functions and usual sensitivity factors. The textural characterization was performed using the conventional nitrogen adsorption/desorption method, with a Micromeritics ASAP 2010 automatic equipment. The surface areas were calculated using the BET method in the relative pressure, P/P 0 , region 0. 065-0. 2, while the pore sizes were determined by the BJH method from the nitrogen desorption branch. Prior to nitrogen adsorption, the oxide samples were degassed at 300 °C for 12 h. The reducibility of the catalysts was studied by temperature programmed reduction under hydrogen (H 2 -TPR).