dataesr/scientific-paragraphs-categorization huggingface dataset:
X ray diffraction analysis Orange crystals of compound 1b were prepared by slow evapo ration of solution of 1b in the mixture of benzene and ethanol (v/ v 1/1) at room temperature. Crystals   step, 2q < 58 ), and 3356 independent reflections (R int 0. 0425) were used in further refinement. The structure was solved by direct method and refined by the full matrix least squares technique against F 2 in the anisotropic isotropic approximation. Hydrogen atoms of NH 2 group were located from the Fourier synthesis while all others were placed in calculated positions and refined in the riding model. The refinement converged to wR 2 0. 0950 and GOF 1. 009 for all independent reflections (R 1 0. 0460 was calculated against F for 2595 observed reflections with I > 2s(I)). All calculations were performed using SHELX software package  [45].
Computational details Quantum chemical calculations were carried out by the MOPAC 2009 program package using the PM6 semiempirical method  [46]. CI calculations were performed at optimized geometries, which reached gradient variations less than 0. 01 kcal/mol. The solvent effect was included in geometry optimizations following the "COnductorlike Screening MOdel" (COSMO) implemented in MOPAC 2009. A dielectric constant of ε 40 and a refraction index of solvent (n) such that n 2 2 were used. The CI included eight occupied and eight unoccupied MOs. Method A. To a solution of compound 5 (242 mg, 0. 452 mmol) in 1,4 dioxane (10. 3 mL) hydrazine hydrate (1. 8 mL, 100 wt. %) was added at 90 C. Raney nickel catalyst prepared from 1. 5 g of nickel aluminum alloy was added portionwise to the reaction mass over a period of 30 min.
It is now possible not only to collect powder XRD data of suitable quality for Rietveld refinement, but also to carry out the structure refinements automatically on massive datasets with high stability using modern Rietveld software such as TOPAS academic. 34 An example of this is our recent study of the development of reaction intermediates and coke in a methanol to olefin (MTO) reactor. 25 The variation in the unit cell parameters of the catalyst (which vary significantly depending on the organic molecules inside the cavities), obtained from full profile Rietveld refinement, was used as an indicator to track the development and movement of reaction intermediates and coke through a tube reactor. The data obtained gave the information required to develop a new kinetic model for the process.
25 n this paper we will fully describe the techniques for analysing Z-scan data with high throughput Rietveld methods, verification of the results against high quality single point synchrotron image plate data, and the possible pitfalls inherent in studying data with very small variations. The example used is the methanol to gasoline (MTG) conversion process over the highly crystalline aluminosilicate zeolite catalyst ZSM-5. 35 This catalyst has been used for conversion of methanol to fuels since This journal is c the Owner Societies 2013 Phys. Chem. Chem. Phys. , 2013, 15, 8662--8671 8663 the 1970's and has been applied at an industrial scale. 36 It has a zeolite framework structure with two interlinked 10-T-atom (T = tetrahedrally coordinated Al or Si) ring channel systems; straight channels parallel to the (0 1 0) direction and sinusoidal or zigzag channels parallel to the (1 0 0) direction.
The reaction gases (helium inert gas or helium bubbled through a flask of methanol at a temperature of 20 1C) were flowed upwards through the L-bend into the reactor at a weight hourly space velocity of 5. 7 g MeOH g À1 catalyst h À1 , X-ray diffraction data were collected on a Pixium area detector with a wavelength of 0. 1426 Å (89. 965 KeV). The beam size was 500 mm. The set-up allowed us to move the catalyst bed up through the X-ray beam at a rate of 0. 5 mm s À1 with PXRD patterns being collected at 1 second intervals. The product stream was analysed using a European Spectrometry Systems ecoSys-P Man-Portable mass spectrometer with capillary inlet and heated inlet tubes. The data were scaled and reduced to 2D powder patterns using the program DATASQUEEZE. The instrumental peak function was characterised by a tan-2y broadening parameter as described by O'Brien et al.
26  using NIST SRM660 Lanthanum hexaboride. This dataset was also used to calculate the sample to detector distance (1472. 5 mm) and flat plate detector geometrical corrections used in data reduction. ] [20] [21] [78] [79] [80]  Helium bubbled through methanol at 20 1C was fed over the catalyst at a rate of 20 ml min À1. Diffraction data were collected at a wavelength of 0. 6978 Å on a MAR345 image plate detector and integrated into 2D powder patterns using FIT2D. 81, 82 he reaction was carried out at a temperature of 350 1C in both the quartz tube and capillary reactors, with the catalyst being activated prior to reaction by heating to 350 1C under a flow of helium gas. The temperature in the capillary setup was calibrated using the thermal expansion of silver while the tube reactor temperature was calibrated using a thermocouple.
Chem. Chem. Phys. , 2013, 15, 8662--8671 This journal is c the Owner Societies 2013 patterns in the dataset, see below). The lattice parameters of the structure were refined for each pattern as restricted by symmetry (orthorhombic space group number 62, Pnma) and the broadening of the peaks in each pattern was modelled with the TOPAS Lorentzian strain broadening macro, on the assumption that strain in the crystallites rather than particle size would be influenced by any changes caused by the build-up of reaction intermediates inside the zeolite catalyst during reaction as for SAPO-34 in the MTO process  4, 6  (sample related broadening of powder diffraction peaks being caused principally by crystallite size and strain). The scale, adsorption correction and zero error were linked between the datasets and treated in different ways in order to test the effect of the different methods.
The ability to link parameters between multiple datasets which are refined simultaneously in a simple and intuitive way (they are simply given the same parameter name whenever they occur in the input file) is a major advantage of the TOPAS Rietveld refinement program. This is a key part of the strategy for Rietveld analysis of fast Z-scan high energy powder X-ray diffraction (HEPXRD) data -limiting the number of parameters in the large datasets and also increasing the precision in their determination. In the analysis of the MTG data we experimented with linking of various parameters between the datasets. The main linked parameters were zero point, scale factor, position and broadening of the quartz background peak and adsorption correction. For the main analysis of the complete dataset a 2-theta range of 0.
5 to 6. 951 was used (min d-spacing = 1. 176 Å). When dealing with very large datasets it is necessary to separate the data into batches due to memory limitations (the least squares matrix must be held in the RAM of the processing computer). This means that it is important to know how certain parameters which should be the same in all cases vary between the batches. We, therefore investigated the influence of the zero point on the datasets -studying the difference between refining batches with a fixed zero point or allowing the zero point to refine separately between the batches and hoping that it reaches the same value in all cases. Allowing different values of the zero point correction led to a significant oscillation in this parameter between two values (À0. 00062 and À0. 00053) which influenced the lattice parameters to a very significant degree (a cell volume average of 5355 Å 3 for zero error = À0.
C. Calculation of errors The normal method for determination of errors from Rietveld refinement is through use of a covariance matrix. For the very large datasets involved in experiments of the type described here this is an exceptionally slow process. Fortunately TOPAS supports the ''bootstrap'' method of error determination which is based on repeated resampling of limited areas of the data range. 84 Since the convergence of the Rietveld refinements required is very fast (even for multiple datasets) this method is highly recommended. The current version of TOPAS academic does not, unfortunately, support the output of bootstrap errors to text files (a huge advantage when dealing with large datasets), but we believe this has been remedied in the new version (TOPAS 5) to be released in 2013.
D. Catalytic activity and selectivity Mass spectrometry (MS) responses characteristic for methanol (m/z 31), DME (m/z 46) and propene (m/z 42) versus time on stream are shown in Fig. 8. Response characteristics for butane (m/z 58), butene (m/z 56) and propane (m/z 44) were also measured, but their time dependence did not deviate from that of propene, and they were therefore omitted from the figure for clarity. Two features are apparent from the data in Fig. 8 : first, methanol and DME responses were both non-zero throughout the experiment, showing that the conversion level was less than 100%. Furthermore, the relative fractions of methanol, DME and propene (or any of the other measured products, see above) in the reactor effluent were stable during the experiment, showing that neither the conversion nor the product selectivity changed with time on stream.
[60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75]  The symmetry changes seem to be related to filling of the sinusoidal channels of the zeolite framework. Filling of the channels also leads to expansion of the unit cell compared to the activated, empty structure. Larger molecules which occupy the intersections of the channels cause small or negative expansion (Table  1 ). The small expansions (around 0. 01%) we observe during the MTG process suggest that even during an experiment under flowing conditions there occurs some build-up of materials inside the channels of the zeolite catalyst. Despite the fact that it was not possible to obtain useful difference Fourier maps  This journal is c the Owner Societies 2013 from the data we suggest in view of the results summarised in Table  1  that the intermediates are not limited to the channel intersections.
Conclusions This work illustrates the methods for analysis of large time and space resolved datasets by the Rietveld method. The comparisons with single point capillary in situ experiments and the analysis of the effects of variations in the refinement strategy show that the method is robust and the use of trend analysis to illustrate weak features of the dataset shows the value of having a large volume of data. The data on the MTG process over ZSM-5 proved ideal for demonstrating this due to the very small impact of the reaction on the diffraction data, especially with regard to the unit cell parameters. We have also shown that data such as zeolite channel occupancy and crystallite strain can be reliably extracted from these data. In this case these data proved more informative than the unit cell parameters.
Acknowledgements We acknowledge funding from the  Research Council of Norway  through inGAP (contract no. 174893 ) and thank the  ESRF  for granting beamtime at ID15b. We also thank  Shewangizaw Teketel Forsido  and  Stian Svelle  for helpful discussions on the MTG mechanism and  Wouter van Beek  for the loan of the SNBL gas flow meter.
In this article, using the example of the DTA, we present a method to build a lens table for electron spectrometers using the electron optics simulation software SIMION. 44 The lens table built by numerical simulations was verified and fine tuned in a series of measurements. The presented procedure could be valuable for all researchers who own a similar spectrometer,  [41] [42] [43]  or want to build or upgrade their homemade charged particle analyzer.
Unfortunately, these sets do not lie on a smooth curve. As a consequence they could not be effectively used as a starting point for the lens table to be designed. Additionally, experience tells us that for a given operation point, the spectrometer still performs well if the electron kinetic energy E k stays in a range of typically ±6%. This is the reason why the parallel detection over a certain kinetic energy range (etendue) is possible by using a position sensitive detector. In order to proceed, we divided the kinetic energy interval of interest in two E k regions. The first one ranges from 250 eV up to 5400 eV with an increment of 10% per step, and the second one ranges from 250 eV down to 35 eV with a decrement of 10% per step. The numerical simulation package SIMION 8. 0 44 has been selected for the numerical trajectory simulations of the electrons in the spectrometer.
An important improvement of SIMION 8. 0 over previous versions of SIMION 6/7 is that the Lua scripting language is implemented, which provides a modern programming interface that was critical for the present work. A downhill simplex optimization method  49  with systematic iterations is implemented in this framework. This method only uses the evaluation of the merit function but not its derivatives. In our case, this function is evaluated as the ratio: (size of the electron image)/(transmission efficiency). Here, the electron image size is defined as the FWHM of the radial distribution of the monoenergetic electrons on the detector, and the transmission efficiency is defined as the ratio between the number of electron trajectories launched and the number of electron trajectories arriving at the detector.
The flowchart of the code, following this logic, is shown in Fig. 2. When launching the optimization by clicking "start fly'm" button in SIMION 8. 0 software, the Lua code is compiled, the table of the electron kinetic energies is loaded, and the values of voltages are set for the first kinetic energy point. Then the electron kinetic energy is set to the current value to optimize. If an optimum is not reached, a new electrons trajectory bunch is launched with modified voltages. If the optimum is achieved, but it is not the last kinetic energy point, a new optimization is launched for the FIG. 2. Flowchart of the program used to simulate the lens table in SIMION 8. 0. The structure of the code has been arranged to be compatible with the internal framework of SIMION. 47 xt kinetic energy point.
VI. SUMMARY AND CONCLUSIONS In this paper we discuss the importance of designing an accurate lens table for charged particle energy analyzers. Indeed, the focusing properties of electron trajectories can be controlled when a large number of degrees of freedom is available in the transfer lens. The drawback of this flexibility is the complexity and the significant duration of time necessary to optimize the voltages applied to the lens electrodes for every measurement. We show that by using the scripting capabilities of the simulation software SIMION 8. 0 it is possible to generate good quality lens tables for a broad range of operation conditions (electron kinetic energies and pass energies) and that these lens tables represent a valuable starting point for a fine tuning of the spectrometer in the laboratory conditions.
With the coordinates from the X-ray structure determination of Tonogaki et al. [14]  as a starting point, atomic coordinates and anisotropic displacement parameters for all atoms were refined against F 2 for all data, using SHELXL97 software  [15] , together with four scale factors and the extinction parameter of the model implemented in SHELXL97. The recommended weighting scheme was used; refined parameters do not change within 3 esd if statistical weights are used instead, in which case the goodness of fit is 2. 07 (see Table  1 ). Crystal data, detailed information on the diffraction experiment, data reduction and structure refinement are collected in Table  1. The experimental molecular conformation and atom labels are shown in Fig. 1.
Calculations Periodic calculations (PC) were performed with CRYSTAL09 program  [17]  using the B3LYP hybrid method with the 6-31G ⁄⁄ basis set. Shrinking factors equal to 4 were used for both, the Pack-Monkhorst and Gilat nets. Default criteria were used as regards accuracy and SCF convergence. Atomic positions were optimized keeping the cell parameters at their experimental values (see Table  1 ). Default optimization criteria were also used in this case. Vibrational frequencies at the C point  [18]  were then calculated for the optimized geometry. Calculations on the isolated molecule were performed with Gaussian 03  [19] , using the B3LYP hybrid method with the 6-31G ⁄⁄ basis set and default convergence criteria for SCF and geometry optimization. The choice of the level of theory, in particular for periodic calculations, deserves some specific considerations.
Methods differ among themselves in the model employed, but also in the way vibrational information is taken into account in the estimation of the residual ADPs. In the present work a periodic model is used and residual ADPs are estimated as DU ij ðH n ; modelÞ U ij ðH n ; modelÞ À U ij ðH n ; TLSðmodelÞÞ ð2Þ Although probably not evident at first sight, the procedure outlined above should be equivalent to the so called TLS + ONIOM method  [23]. As pointed out in the introduction, however, improvement with respect to that proposal could in principle be expected if vibrational information obtained from periodic calculations was used. We will borrow that acronym and refer to the present implementation as TLS + CRY. Using the program CRYSTAL09  [17]  it is possible, in principle, to characterize the complete set of normal modes (phonons) of a periodic model system.
H11 H12 DU  X i<j U EXP ij ðHÞ À U EST ij ðHÞ r EXP ij ðHÞ ! 2 2 4 3 5 1 2 is also included in Table  4. In the expression above r EXP ij ðHÞ is the experimental estimated uncertainty (standard deviation) of U EXP ij ðHÞ. THMA14  [27]  as implemented in the WinGX  [28]  package of crystallographic programs was used for the TLS fits. Out of the 36 individual ADPs, 33 are reproduced within three times the experimental uncertainty, 22 of them being within twice the uncertainty. The most poorly predicted parameters are U 22 (H11), which is overestimated by 46 Â 10 À4 Å 2 (six and a half times the uncertainty), and U 22 (H2) and U 33 (H5), which are underestimated by 44 Â 10 À4 Å 2 (about four times the uncertainty) and 25 Â 10 À4 Å 2 (three times the uncertainty), respectively. The comparison between an estimated MSDT and its experimental (reference) counterpart can also be performed in terms of the eigenvalues and eigenvectors of their difference when expressed in a cartesian axial system.
2. 030(2) Å). Two issues can be remarked. In the first place, the observed difference between the residual MSDT of the amino hydrogen atoms raises a warning against the use of an only set of ADPs for each chemical type of hydrogen, since individuals within a given type are usually in different environments in the crystal. It should be mentioned that the statistical frequency of this situation in molecular crystals is surely at the origin of the large rms deviations of the amino hydrogen atom parameters in SHADE2 data base. Both experimental and theoretical values for DU CC (H11) would be closely reproduced by the SHADE2 web server  [8]  if the N1-6C-N2 fragment was used, but in that case DU CC (H12) would be severely overestimated. Moreover, the situation would be even worse if the minimal N1-6C fragment was used, and this fact concerns the second issue we would like to address here: the internal motion of the amino nitrogen relative to the rest of the molecule.
Related literature For background to and the biological activity of quinoline derivatives, see:  Sasaki et al. (1998) ;  Reux et al. (2009) ;  Morimoto et al. (1991) ;  Markees et al. (1970). For related structures, see:  Loh et al. (2010a,b). For a description of the Cambridge Structural Database, see  : Allen, (2002).
Experimental Crystal data (i) Àx þ 1; Ày þ 1; Àz; (ii) x þ 1; y; z; (iii) Àx þ 1; Ày þ 1; Àz þ 1. Data collection: CrysAlis PRO (Agilent, 2011); cell refinement: CrysAlis PRO; data reduction: CrysAlis PRO; program(s) used to solve structure: SIR2002  (Burla et al. , 2003) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: HG5339).
Computing details Data collection: CrysAlis PRO (Agilent, 2011); cell refinement: CrysAlis PRO (Agilent, 2011); data reduction: CrysAlis PRO (Agilent, 2011); program(s) used to solve structure: SIR2002  (Burla et al. , 2003) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
This work was supported by the Unitede Recherche de Chimie de l'Environnement et Moleculaire Structurale, CHEMS, UniversiteConstantine 1, Algeria and Laboratoire de Chimie de Coordination Toulouse, France. Thanks are due to the Ministere de l'Enseignement Superieur et de la Recherche Scientifique -Algerie (via the PNR project) for financial support.
(i) Àx; y þ 3 2 ; Àz þ 1 2 ; (ii) Àx þ 1; y À 1 2 ; Àz þ 1 2 ; (iii) x; Ày þ 3 2 ; z þ 1 2. Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT-Plus  (Bruker, 2009) ; data reduction: SAINT-Plus; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: IM2432).
Computing details Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT-Plus  (Bruker, 2009) ; data reduction: SAINT-Plus  (Bruker, 2009) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and publCIF  (Westrip, 2010). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max < 0. 001 Δρ max = 0. 33 e Å -3 Δρ min = -0. 25 e Å -3 Special details Geometry. All s. u. 's (except the s. u. in the dihedral angle between two l. s. planes) are estimated using the full covariance matrix. The cell s. u. 's are taken into account individually in the estimation of s. u. 's in distances, angles and torsion angles; correlations between s.
Acknowledgments E. A. A. P. and T. S. have received funding from the  European Research Council  under the  European Communitys Seventh Framework Program  ( FP7/20072013 )/ ERC  grant Agreement No. 207916. C. R. T and J. R. V. acknowledge financial support from  Generalitat de Catalunya and Ministerio de Economa y Competitividad  through grants  SGR2009-01225  and  MAT2010-15225 , respectively.
Magnetic measurements: The magnetic susceptibility measurements were carried out with a Quantum Design SQUID magnetometer MPMS-XL. This magnetometer works between 1. 8 and 400 K for dc applied fields ranging from -7 to 7 T. Measurements were performed on polycrystalline samples. Alternating current susceptibility measurements have been measured with an oscillating ac field of 3 Oe and ac frequencies ranging from 1 to 1500 Hz. The magnetic data were corrected for the sample holder. Mössbauer spectroscopy: The Mössbauer spectra were acquired using a conventional spectrometer in the constantacceleration mode equipped with a 57 Co source (3. 7 GBq) in a rhodium matrix. Isomer shifts are given relative to α-Fe at room temperature. The sample was inserted inside an Oxford Instruments Mössbauer-Spectromag 4000 Cryostat, which has a splitpair superconducting magnet system for applied fields up to 5 T, with the field of the sample oriented perpendicular to the γ-ray direction.