dataesr/scientific-paragraphs-categorization huggingface dataset:
Specific Impulses Once the heat of formation of ag iven compound is known, and using its molecular formula, the detonation parameters (for high explosives) and specific impulses (I sp ;f or propellants) can be calculated by using ac omputer code. For all I sp calculations reported in this section, we used the OPHELIE code, which is am odificationo ft he CEC71 program (Chemical Equilibrium Calculations)  [119]  used by NASA to calculate theoretical rocket performance. These simulations are typically carriedo ut with ac ombustion chamber pressureo f7 0atm and an exit pressure of 1atm. Given the properties of the DMTZ salts studied herein, they could potentially be used as solido rl iquid (dissolving the salts in as olvent) propulsion compounds. For DMTZ salts 1-6,a sw ell as for HZT and TZZT,w ecarried out two simulations:o ne with the calculated enthalpies of formation and one with (if available) the experimental valuesf rom bomb calorimetry,f or comparative purposes (Table  12 ).
Computational Details The chemical quantum calculations were carried out by using Gaussian 09 software. [8] We used GaussView 5t op repare the Gaussian job files and analyze the results of the output file. For all calculations, we used al ocal cluster belonging to the Analytical Science Institute (UMR 5280) in Lyon. Each node ran the Linux Red Hat Enterprise (64 bits) operating system and comprised two hexacore Intel Xeon X5650 processors clocked at 2. 66 GHz and 24 GB of memory (DDR3, 1333 MHz). Each Gaussian job was run on as ingle node, with all 12 cores available and 22 GB of memory allocated. Once the method was chosen, the equilibrium geometry (i. e. , ground state) was sought. This geometry is as tationary point corresponding to the global minimum on the potential energy hypersurface and is characterized by the absence of imaginary frequencies.
X-ray Crystallography As uitable crystal was selected and mounted on aG emini kappageometry diffractometer (Agilent Te chnologies, UK, Ltd. ) equipped with an Atlas CCD detector with Cu radiation (l = 1. 5418 ). Intensities were collected at 150 Kb yu sing the CrysalisPro software. [120] eflection indexing, unit-cell parameter refinements, Lorentz polarization corrections, peak integration, and background determination were carried out by using the CrysalisPro software. [120] An analytical absorption correction was applied by using the modeled faces of the crystal. [121] The resulting set of hkl values was used for structure solution and refinement. The structures were solved by direct methods with SIR97  [122]  and least-squares refinement on F2 was achieved with the CRYSTALS software. [123] All non-hydrogen atoms were refined anisotropically.
T he hydrogen atoms were all located in ad ifference map, but those attached to carbon atoms were repositioned geometrically. T he hydrogen atoms were initially refined with soft restraints on the bond lengths and angles to regularize their geometry (CÀHi nt he range 0. 93-0. 98, NÀHi nt he range 0. 86-0. 89, and OÀH = 0. 82 ) and U iso (H) (in the range 1. 2-1. 5 times U eq of the parent atom), after which the positions were refined with riding constraints. The Diamond software  [124]  was used to prepare Figures  2a nd 3.
Acknowledgements This work is part of the Ph. D. thesis of V. F. ,w ho acknowledges financial support by the  CNRS  ( Centre National de la Recherche Scientifique ) and the  CNES (Centre Nationald'Études Spatiales ). V. F. would like to thank  Dr. P. M ignon  for his assistanceinu sing the cluster, D r. E. Jeanneau  for the crystal structure measurements,a nd Dr. F. R. C lemente from the Gaussians upport team for his valuablehelp concerning the volumec alculations.
4 eV with a 0. 4 eV energy step. Core-level spectra were acquired at a pass energy of 23. 5 eV with a 0. 1 eV energy step. All spectra were acquired at 90 • between X-ray source and analyzer and with the use of low energy electrons and low energy argon ions for charge neutralization. After subtraction of the Shirley-type background, the core-level spectra were decomposed into their components with mixed Gaussian-Lorentzian (30:70) shape lines using the CasaXPS software. Quantification calculations were performed using sensitivity factors supplied by PHI. The sample was prepared by casting a concentrated aqueous solution of the material onto silicon substrate followed by drying at 100 • C for 1 h.
Photoelectron survey spectra were acquired using a hemispherical analyzer at pass energy of 117. 4 eV with a 0. 4 eV energy step. Core-level spectra were acquired at a pass energy of 23. 5 eV with a 0. 1 eV energy step. All spectra were acquired at 90° between X-ray source and analyzer and with the use of low energy electrons and low energy argon ions for charge neutralization. After subtraction of the Shirley-type background, the core-level spectra were decomposed into their components with mixed Gaussian-Lorentzian (30:70) shape lines using the CasaXPS software. Quantification calculations were performed using sensitivity factors supplied by PHI. The sample was prepared by casting a concentrated aqueous solution of the material onto silicon substrate followed by drying at 100 °C for 1 h.
Acknowledgments: R. B. and S. S. gratefully acknowledge financial support from the  Centre National de la Recherche Scientifique (CNRS) , the  Lille1 University , the  Hauts-de-France region  and the  CPER Photonics for Society. S. S thanks the  Institut Universitaire de France (IUF)  for financial support. G. -H. P. is grateful to financial support from  National Natural Science Foundation of China  (Grants No. 51202019  and  51402284 ), the  Development Plan of Science and Technology of Jilin province  ( 20150520022JH ), and the  Prior Sci-tech Program of Innovation and Entrepreneurship of Oversea Chinese Talent of Jilin province.
They were placed into 96-well plate and imaged using the NightOwl setup. The measurements were done in triplicate. The obtained values of the A/ (A + D) ratio were plotted vs % of integrity and the data were fit with an exponential function (1): y ¼ a þ be cx ð1Þ where a = 0. 14572; b = 0. 1254; c = 0. 01748, y is A/(A + D) and x is FRET pair concentration (wt%). As another model of the complete disintegration of NCs, solution FRET NCs diluted 1000-fold in dioxane was placed into 1 mL Eppendorf® tube and imaged by the NightOwl setup.
Image analysis of the FRET signal in the living mice The ratiometric images were built using a homemade plugin (developed by Romain Vauchelles) under ImageJ that divides the image of the 840 nm channel by that of the 700 nm channel. For each pixel, a pseudocolor scale is used for coding the ratio, while the intensity is defined by the integral intensity recorded for both channels at the corresponding pixel. Image analysis was performed by using the ImageJ software  [51]. The tail vein, liver and tumor were manually delimitated and the mean intensity of the delimitated region was determined for each respective time point and channel for three healthy and three tumor-bearing mice. For both donor and acceptor channels, the signal was corrected for the background using the mice before injection (in the corresponding region of interest).
Acknowledgments This work was supported by  ERC   Consolidator grant  BrightSens  648528 ,  Université de Strasbourg  (IdEX 2015, W15RAT68 and J. G. G. ), by research grants from the  French National Cancer Institute (INCa)  and the  Ligue Contre le Cancer  (J. G. G. ), by institutional funding from  Inserm. RB is supported by  Ministry of Higher Education and Scientific Research of Algeria. BA is supported by  LabEx Chimie des Systèmes Complexes. L. M. is supported by an  INSERM/Région Alsace Ph. D Fellowship. We thank  Ievgen Shulov  helping with synthesis of Cy5. 5LP,  Tsukasa Shibue  ( HHMI/MIT ) for providing the D2A1 cells and  Dominique Bagnard  ( INSERM U1109 ) for providing access to the NightOwl luminograph. We thank the animal facility at INSERM  U1109  for mouse care.
The challenges associated with characterizing the proton surface reactivity of montmorillonite were summarized a decade ago by Bourg et al. 20. A first challenge is that montmorillonite particles undergo significant dissolution during acid-base titration measurements. This can be rendered near-negligible in the pH range from about 4. 5 to 9. 5 through a careful choice of clay pretreatment, storage, and titration procedures  [25] [26] [27] [28] [29]  (see below). Unfortunately, most montmorillonite acid-base titration datasets do not fit the best practices established by Duc et al. [25] [26] [27] [28]. A second challenge is that acid-base titration measurements do not directly measure the net proton surface charge density σ H but rather δσ H , the change in net proton surface charge density relative to its initial value σ H,init (ߜߪ H = ߪ H -ߪ H,init ).
Montmorillonite consists of flake-shaped, 1-nm-thick layers such that the narrow edge surface cannot be a priori treated as an infinite planar surface  9, 20   Bourg et al. 20  showed that the first three challenges outlined above could be resolved using information available a decade ago. The second challenge was resolved by using an SCM to predict both σ H,init (from reported conditions of clay pre-treatment) and σ H. The first challenge required identifying experimental datasets that followed that best practices established by Duc et al. [26] [27] [28]  and that used montmorillonite samples pre-treated by repeated washing in a solution of well-known pH and ionic strength (as required to predict σ H,init ); the available database included only two datasets, those of Baeyens and Bradbury  39  and Duc et al.
50 (PHREEQC scripts and database are available in the supporting information file). An excellent agreement was found between the two predictions (Figure  3 ), suggesting that Eq. (  4 ) is valid over the entire range of experimental conditions where montmorillonite particles are made of single layers. This condition is met in experiments carried out with montmorillonite particles dispersed in a NaCl or NaClO 4 background electrolyte at ionic strengths lower than I~0. 2  [98] [99] [100] [101]. Figure  3. Comparison of the GCMC simulations of Delhorme et al. 50 (circles and squares) with an SCM that used Eq. (  4 ) (solid lines) at three different ionic strengths in NaCl salt background. Both models used the same site densities and intrinsic pK a values. Activity coefficients of solute species in the SCM were modeled using the extended Debye-Hückel formalism.
Th  present study, therefore, focuses on the dataset from Duc et al. 27  as the best available set of montmorillonite acid-base titration results. An interesting indirect quantification of edge surface charge was pursued by Pecini and Avena  48  , who measured the i. e. p. of montmorillonite loaded with cationic dyes with a strong affinity for the basal surface. Under the assumption that the dyes completely screen the basal surface charge but do not interact with the edge surfaces (such that the measured i. e. p. reflects edge surface properties) and within the well-established theoretical limitations associated with relating an i. e. p. to a point of zero charge  30  , the results of Pecini and Avena  48  suggest that the point of zero charge of clay edge surfaces (the pH value where σ 0,edge + σ H = 0, where σ 0,edge corresponds to the permanent structural charge density "expressed" on the edge surface) is in the pH range 3.
For MX80 montmorillonite, the agreement between experimental and predicted potentiometric titration curves was acceptable if the specific edge surface area was set to 12 m 2 ⋅g - 1 and the relative abundance of [010] and [110] edges was set to 1:1 (Figure  5 ) (the effect of varying the relative abundance of [010] and [110] edges can be seen on Figure  4 , and it can be quantified by using the PHREEQC script files provided in the supporting information). The modeled edge specific surface area was larger than the measured value (Table  S -1, from 6 to 9 m 2 ⋅g -1 , depending on the considered study). However, the values reported in Table S-1 were not measured for the same samples used in the titration experiments. For both titration datasets, the pH values corresponding to ߜߪ H = 0 were correctly predicted at all investigated ionic strengths.
Acknowledgement This work was supported by the  French Radioactive Waste Management Agency (Andra)  in the framework of the  Andra-BRGM scientific partnership  ( CTEC  project). J. A. D. acknowledges funding from L' Institut Carnot  for his visit to the  BRGM  and from the  U. S. Department of Energy  under Contract  DE-AC02-05CH11231  under the auspices of the  Office of Nuclear Energy ,  Used Fuel Disposition program. I. C. B. was supported by the  U. S. Department of Energy  under Contract  DE-AC02-05CH11231  through the  Office of Science, Office of Basic Energy Sciences, Geosciences program.
Al 1. 57 Mg 0. 25 Fe III 0. 09 Fe II 0. 09 )Na 0. 34 O 10 (OH) 2 , respectively 27. Reported montmorillonite structural formulae can vary as a function of sample preparation and calculation method 65. This variability adds a minor source of uncertainty to our model predictions according to Figure 4. The ‫ݔ‬ வୗ୧ିିୗ୧ , ‫ݔ‬ வୗ୧ିୣ ିୗ୧ , ‫ݔ‬ வୗ୧ିୣ ିୗ୧ and ‫ݔ‬ வ୪ି୪ିୗ୧ values were set, respectively, to 0. 125, (Si 4 )(0. 045, 0. 045 and 0 for MX80 montmorillonite and to 0. 125, 0. 003, 0. 112 and 0. 13 for SWy-2 montmorillonite. for the SWy-2 and MX80 reference montmorillonites (the dataset for SWy-2 was made available in Delhorme et al. 50 ). The structural formulae of SWy-2 and MX80 montmorillonites were reported as (Si 3. 87 Al 0. 13 )(Al 1. 52 Mg 0. 25 Fe III 0. 224 Fe II 0. 006 )Na 0. 39 O 10 (OH) 2 and.
As Ra increases, the intensity of convection increases and the centres of convection rolls move upwards. For small Ra, the flow is in the 'pesudo-conductive' regime since heat is mainly transferred by conduction  [49]. As shown in Fig. 3 (a), the isotherms resemble quasi-circles and the centre of rotation is very close to the horizontal line y = 0. For higher Ra, the flow is of boundary layer type  [50]. As shown in Figs. 3(b ) and (c), a buoyant plume originates at the top of inner cylinder and impinges towards the outer cylinder. Results of the mean Nusselt numbers with natural convection are listed in Table  1. For validation, the results obtained with the commercial software FLUENT are also provided. The same number of CVs is used in FLUENT. The results obtained with two different methods are in good agreement with each other.
Acknowledgements This work was funded by the  French Government program  " Investissements d'Avenir" (LABEX INTERACTIFS , reference  ANR-11-LABX-0017-01 ) (to  Jian Wu ), a grant from the  French district Poitou-Charentes  (to P. Traoré ) and financial support from the  Spanish Ministerio de Cienciay Tecnologí a (MCYT)  under Research Project No. FIS2011-25161  and  Junta de Andalucí  a under research projects  P10-FQM-5735  and  P09-FQM-4584  (to A. T. Pé rez).
Other flow reactor studies include investigations by Alzueta et al. [13] , Ing et al. [14] , Rasmussen et al. [15]  and Aranda et al. [16]. Alzueta et al. [13]  studied the oxidation of methanol with and without NO in a flow reactor, performing the experiments at atmospheric pressure in the temperature range 700-1500 K. Ing et al. [14]  reported experimental data for methanol in a flow reactor under pyrolysis and oxidation conditions. Methanol pyrolysis was studied at 1073 K over a pressure range of 1-10 atm while oxidation experiments were conducted over a temperature range of 873-1073 K and a pressure range of 1-5 atm. Rasmussen et al. [15]  also studied the oxidation of methanol in a flow reactor with and without the presence of NO in a temperature range of 650-1350 K. Recently Aranda et al.
Shock tube (ST): Two ST facilities have been used in this study. The high-pressure ST used is an updated version of that described in detail by Darcy et al. [25]. It has an inner diameter of 6. 35 cm, a driver   The low-pressure ST facility has been described in detail previously by Smith et al. [26]. Briefly, it has a barrel-shaped driver section 62 cm long. Polycarbonate diaphragms are used to separate the driver section from the 6. 22 m long driven section. This facility has a 10. 24 cm internal diameter and is equipped with a Kistler 603B pressure transducer located in the end-wall used to measure ignition delay times, Fig. 3. This facility was used to study the 2 atm, φ = 2. 0 methanol/'air' data only. oxygenates were analyzed on a Poraplot U column (methanizer and FID). Helium was used as carrier gas in the GC analyses.
Ignition delay times, RCM and ST A comprehensive matrix of ignition delay times are measured using both an RCM and two ST's, which have already been described. The range of experiments cover different pressures (2-50 atm), temperatures (850-1650 K), equivalence ratios (φ = 0. 5-2. 0) and dilutions (fuel in air conditions down to 1. 5 mole % methanol concentration). The complete list of mixtures studied is presented in Table  2. In this section the experimental data will be presented and compared to the predictions of the model developed in this work (Mech15. 34) and the model of Li et al. [39]. All simulations are performed using CHEMKIN-PRO  [40]. The two definitions show no difference in the measured ignition delay times as shown. Nonetheless in order to accurately compare the model simulations to the data the different ignition delay time definitions were considered.
The data from Kumar and Sung  [7]  were ultimately qualitative as a comprehensive set of non-reactive profiles were not supplied in order to model that data. In the current study a comprehensive set of CHEMKIN format input files are provided for the simulation of the RCM data measured. In Fig. 12  ignition delay times at temperatures below 1000 K and at three pressures are presented and compared to the predictions of the two models. At a nominal pressure of 10 atm the two models over-predict ignition delay times. At pressures of 30 and 40 atm Mech15. 34 shows, generally good agreement with the experimental data. However the model of Li et al. [39]  tends to continue to over-predict ignition delay times at the elevated pressures. Another unique aspect to the ignition delay time measurements presented here is the measurement of ignition delay times at a pressure of 50 atm.
Conclusions and Future Work The data contained herein covers ignition delay times measured in two STs and in an RCM and species measurements for methanol oxidation performed in a JSR. The ignition delay times measured cover a range of pressures (2-50 atm), temperatures (820-1650 K), equivalence ratios (0. 5, 1. 0 and 2. 0) and dilutions (1. 5% to 21. 9% methanol, including fuel/'air' mixtures). These ignition delay times cover the widest range of conditions for any ignition delay time study of methanol to date. Of particular novelty are the high pressure (50 atm) and low-temperature (<1000 K) measurements which are significant as they cover important validation conditions for the detailed chemical kinetic model (Mech15. 34). The JSR species measurements provide further validation for the developed model (Mech15.
CH 3 OH = 5. 70%, O 2 = 8. 55% and Ar = 87. 75%) and mixture 7 (CH 3 OH = 12. 28%, O 2 = 18. 43% and N 2 = 69. 29%) are compared. At 50 atm the ignition delay times decrease with increasing reactant concentration (ignition delay times of mixture 7 < ignition delay times of mixture 9). In general both models are capable of predicting both datasets in Fig. 10 (b).
Acknowledgements Ultan Burke  and  Alexander Heufer  would like to acknowledge the financial support from the  Irish Research Council (IRC). Wayne Metcalfe  and  Sinead Burke  would like to acknowledge the financial support of  Saudi Aramco. Thanks to  Dr. Chih-Jen Sung ,  Dr. Kamal Kumar ,  Prof. Ravi Fernandes ,  Dr. Changyoul Lee  and  Dr. Kieran Somers  for the useful discussions and help during this project.
(b) mixture 9 ( 9 CH 3 OH = 5. 70%, O 2 = 8. 55% and Ar = 87. 75%) and mixture 7 (CH 3 OH = 12. 28%, O 2 = 18. 43% and N 2 = 69. 29%) are compared. At 50 atm the ignition delay times decrease with increasing reactant concentration (ignition delay times of mixture 7 < ignition delay times of mixture 9). In general both models are capable of predicting both datasets in Fig. 10 (b).
ACKNOWLEDGEMENTS We thank  A. Chubukov ,  R. Fernandes ,  Y. Matsuda ,  I. Mazin ,  T. Shibauchi  and  L. Taillefer  for useful discussions. Funding: This work was supported by the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences, Materials Science and Engineering Division. Ames Laboratory  is operated for the U. S. DOE by  Iowa State University  under contract  DE-AC02-07CH11358. We thank the  SIRIUS team ,  O. Cavani ,  B. Boizot ,  V. Metayer , and  J. Losco  for running electron irradiation at École Polytechnique [supported by  EMIR  ( Réseau national d'accélérateurs pour les Etudes des Matériaux sous Irradiation) network , proposal  11-11-0121 ]. V. M. acknowledges the support from the  Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by  UT-Battelle, LLC , for the  U.
Preparation of Na12 NaOH. based on the method of Blessing. 19 The structures were solved by direct methods and refined by full-matrix least-squares using the SHELX-TL package. 20 In the three reported structures, there is a discrepancy between the formulae determined by elemental analysis and that deduced from the crystallographic atom list because of the difficulty in locating all the disordered water molecules and alkali ions. Disordered water molecules and counter-ions were thus refined with partial occupancy factors. The hydrogen atoms were theoretically located on the basis of the conformation of the supporting atoms. The crystallographic data are gathered in Table  1. CCDC numbers: 1431904 (SiW9CuAle), 1431905 (SbW9CuAle) and 1431906 (P2W15CuAle). Magnetic measurements. Magnetic measurements on powder were carried out with a Quantum Design SQUID Magnetometer with an applied field of 1000 G using powder samples pressed in pellets to avoid preferential orientation of the crystallites.
The independence of the susceptibility value with regard to the applied field was checked at room temperature. The susceptibility data were corrected from the diamagnetic contributions as deduced by using Pascal's constant tables. The susceptibility times temperature as a function of temperature curves have been simulated using MAGPACK. 21 ectrochemistry. The solution were deaerated thoroughly for at least 30 minutes with pure argon and kept under a positive pressure of this gas during experiments. Electrochemical measurements were carried out on a PARSTAT 2273 potentiostat at room temperature under argon atmosphere. A three-electrode system was used with a glassy carbon (GC, Tokai Japan) electrode as a working electrode, a platinum wire as auxiliary electrode and the reference electrode was the saturated calomel electrode, which was electrically connected to the solution by a junction bridge filled with electrolyte.
74 cm -1. The magnetic behavior of SiW9CuAle has been interpreted considering the POM {SiW9Cu3} fragment and the {Cu6Ale4} cluster as magnetically isolated. In {SiW9Cu3}, the copper centers form an isosceles triangle, necessitating the introduction of an additive exchange parameter compared to the model used for the {SbW9Cu3} magnetic unit. The coupling scheme associated to SiW9CuAle is represented on Figures  3d  and 4c , and leads to the following Hamiltonian: The best fit to the experimental MT data in the 300 -2 K temperature range has been obtained for J1 = -255 cm -1 , J2 = -55. 0 cm -1 , J3 = +1. 56 cm -1 and J4 = -0. 11 cm -1 (TIP = 110 10 -6 cm 3. mol - 1 per Cu II center, g = 2. 19 and R = 8. 6 10 -6 ). 29 Considering the {SiW9Cu3} fragment, the dx²-y² orbitals of the Cua and Cub atoms interact via the p-orbital of the deprotonated O(C) atom of the BP ligand, with a low dihedral angle of 12.