dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence, a complex mix of metabolites can be analyzed in a single run, no precursor ion is selected for individual fragmentation, and fragment ions can be related to their precursors using a mass fragmentation software and high-resolution mass databanks. This technique, coupled with UV analysis, was already applied with success to the identification and quantification of major carotenoids and chlorophylls in Dunaliella salina  [30]. In the present study, we intended to develop a simple and fast UPLC-MS E method for the identification of major phytoplankton pigments and the rapid discrimination of peaks corresponding to minor unidentified metabolites that could correspond to pigments or derivatives. We first established a UPLC-MS E database with 22 standard pigments, representative of major phytoplankton taxa, to record their retention time and the accurate masses of parent and most intense fragment ions.
Identification of Pp Pigments Using the Standard Pigments Database The UPLC-MS E analysis of Pp ethanol extract was achieved in a single 13-min run, and an UPLC-MS E chromatogram in which 35 peaks were discriminated was obtained (Figure  1  and Table  2 ). Comparison of the Rt and mass data with the standard pigments databank allowed the unequivocal identification of eight pigments or derivatives (Table  2  and Figure  2 ): β-car, chl a, chlide a, crypto, zea, pheide a, phein a, and DV chl a. With the exception of DV chl a, all the detected pigments had previously been reported in Pp  [32, 33]. The MS E analysis also excluded the presence of 14 pigments in the Pp ethanol extract (19′ But-fuco, 19′ hexan-fuco, allo, asta, chl b, chl c2, diadino, diato, DV-chl b, fuco, perid, prasino, pyropheide a, and viola).
Identification of Pp Pigments Derivatives Using the Metabolynx Software The Metabolynx software was used to determine if unidentified peaks in the UPLC-MS E chromatogram exhibited accurate masses corresponding to parent ions of pigments metabolites. The masses of the major parent ions detected as peaks 17, 25, 26, and 27 exactly matched with that of hydroxylated chl a and phein a (Table  3 ). Analysis of fragment ions detected at the same Rt confirmed this identification (Table  3 ). The presence of hydroxylated derivatives of chl a and phein a in the Pp ethanol extract suggested a possible hydroxylation of both pigments during the ethanolic extraction, or less probably the presence of both hydroxylated pigments in living Pp cells. Chl a and phein a standards were subjected to the ethanolic extraction and their respective hydroxylated derivatives were detected using the UPLC-MS E analysis (data not shown), confirming that the hydroxylation of both pigments occurred during the extraction.
Tentative Identification of Other Metabolites in the Pp Ethanol Extract Because of the high number of ionizable metabolites extracted by ethanol, numerous ions that did not correspond to pigments or derivatives were detected in the UPLC-MS E chromatogram, some of them co-eluting with parent ions corresponding to pigments. For each unidentified parent ion, the most probable molecular formula was determined using the Masslynx software. A bibliographic analysis was then performed to determine if these structures, their masses and fragmentation pattern had previously been reported. The result of this study indicated that the detected metabolites most probably corresponded to lipids, glycolipids, plastoquinone, gracilamide B, erucamide, and phthalates (Table  4 , Supplementary Figure  S2 ,  [37] [38] [39] [40] [41] ).
Pyropheide a (purity 95%) and pheide a (purity 95%) were purchased from Frontier Scientific, Frankfurt am Main, Germany. Ultra-pure water was obtained using a Milli-Q system (Millipore, Molsheim, France). Reagents were of HPLC grade for extraction and ULC-MS grade (Biosolve, Valkenswaard, Netherlands) for the MS E analysis. Table  4. Putative nature of non-pigment metabolites present in the Pp ethanol extract according to the comparison of MS E data, mass data from the literature  [37] [38] [39] [40] [41] , and most probable formula proposed by the Mass Fragment software.
1 × 50 mm, 1. 7 µm). The system was operated under the following gradient elution program: solution A (0. 01% formic acid in H2O) in solution B (0. 01% formic acid in MeOH) at a flow rate of 400 µL• min -1 as follows: 0-0. 5 min, 70% B; 0. 5-3. 00 min, 70%-100% B; 3. 00-11. 00 min, 100% B; the eluent was adjusted to its initial composition in 2 min. The column and autosampler were maintained at 25 °C and 4 °C, respectively, and the column back pressure was 13,000 psi. Final ESI conditions were: source temperature 120 °C, desolvation temperature 500 °C, cone gas flow 50 L• h -1 , desolvation gas flow 1000 L• h -1 , capillary voltage 2. 5 kV, sampling cone voltage 35 V, and source offset 80 V. The instrument was set to acquire over the m/z range 50-1200 with a scan time equal to 0. 5 s. Data were collected in the positive (ESI + ) electrospray ionization modes using the MS E function in centroid mode, with a 6 V collision energy in function 1 (parent ions experiment) and a collision energy ramp of 20-40 V in function 2 (fragment ions experiment) (frequency of low to high collision switch = 30 Hz).
Software MS E data were recorded in a centroid mode and analyzed using the MassLynx software. In a first step, standard pigments were subjected to MS E analysis and a personal database including the empirical formula of observed ions, Rt, and most diagnostic fragments in function 2 was created using the Notepad text editor. The MassFragment software was used to identify and propose a structure for each pigment ion fragment. The Pp ethanol extract was then subjected to MS E and data obtained (Rt, accurate masses of parent and fragment ions) were compared to the values recorded in the standard pigments database by the Chromalynx software, to confirm the presence or absence of pigments and metabolic derivatives. An additional manual check was performed for each compound identified by Chromalynx, using the Masslynx software.
Conclusions The UPLC-MS E method allowed the rapid and unambiguous identification of phytoplankton pigments in an ethanolic extract, by comparison of the Rt and accurate masses of major and diagnostic parent and fragment ions recorded in a standard pigment database. The quasi-simultaneous detection of parent and fragment ions allowed us to retrace the fragmentation pattern of each pigment. MS E data were, however, insufficient to identify the structure of pigment allomers or isomers, for which additional spectrophotometric data were necessary. Analysis of the unidentified ions using the Metabolynx XS software revealed that some of them corresponded to hydroxylated pigments metabolites produced during the ethanolic extraction. Additionally, UPLC-MS E analysis revealed the presence in the Pp ethanolic extract of ions that could correspond to previously reported metabolites, including fatty acids, glycerides, galactosylglycerides, ceramides, and peptides.
Image Analysis Microscopic micrographs were analyzed using basic functions for image adjustment and processing of Image J, a public domain Java image-processing program (http://imagej. nih. gov/ij/). For cyanophycin quantification, the number of globules per cell and the area of each globule were determined by using the Analyze Particles procedure that calculate area and pixel value statistics of user-defined region of interest (ROI). A nominal radius for the aerial projection of cells (vegetative and akinetes) and CG was calculated, assuming a circular shape. The calculated radius was used to estimate the corresponding volume assuming spherical shape for both cells and CG. The total volume of cellular cyanophycin pool was calculated as a product of globules per cell and the average volume of a cyanophycin globule.
The high mass resolution (M/DM > 5000) allows the separation of ions of very close masses. The analyses were done using the Cs + primary ion source to detect negative ions. For individually observed frame, total ion content (TIC) image and secondary ion images for 12 C, 13 C, and 12 C 14 N were recorded. The data processing and image analyses were performed using the ImageJ software  (Schneider et al. , 2012). The fate of carbon assimilated during akinete differentiation and its potential contribution to akinetes' storage pools was further studied in a pulse chase experiment where 13 C bicarbonate was provided to akinete-induced cultures for the first 2 days of induction and chased with 12 C bicarbonate for additional 20 days. Subsamples were collected from various stages of this experiment fixed and sectioned for further analysis by NanoSIMS.
Nano-scale SIMS Data Processing Ion species 12 C-and 13 C-were used to detect carbon associated with biomass and 13 C/ 12 C ratio was used to assess carbon uptake and assimilation (enrichment over the background), during the labeling period. Isotopic ratio color images were generated by using OpenMIMS, an ImageJ plugin developed by Claude Lechene's Laboratory  (Poczatek et al. , 2009). OpenMIMS allows the visualization of the ratio value in a Hue-Saturation-Intensity (HSI) image. Additional ImageJ plug-ins were used to compute the average 13 C/ 12 C ratios in the cells  (Chew et al. , 2014).
Acknowledgments We want to thank  Claudia Menzel  for sample preparation for the TEM,  Alexandrine Pannard  for helpful advice on samples preparation for NanoSIMS analyses,  Björn Watzer ,  Rebeca Perez , and  Karl Forchhammer  for protocols and discussions. The thin sections for the NanoSIMS analyses were prepared by the electronic microscope plateform MRIC/TEM/BIOSIT at the  University of Rennes (Villejean). Work in Tübingen was supported by  Deutsche Forschungsgemeinschaft, DFG  ( GRK1708 ,  SFB766 ). AS was supported by  Israel Water Authority  and research grants awarded by the  Israel Science Foundation  (ISF grant No. 319/12 ). MB was supported by a research grant from the  University of Rennes  1 for international collaborations. We deeply appreciate knowledge sharing with European experts and researchers via the  European Cooperation in Science and Technology, COST Action   ES 1105.
ACKNOWLEDGMENTS We thank the  Chlamydomonas Genetics Center (USA)  for providing the CC-125 Chlamydomonas line, and  Dr. Kiyoshi Ohyama  for identification of the sterols. This research was supported by a grant from the  Global Frontier Program  ( 2011-0031345 ) of the Republic of  Korea  awarded to  Youngsook Lee , from  Gyeongbuk Sea  Grant Program (20090218) funded by the  Ministry of Oceans and Fisheries ,  Korea  awarded to  Jae-Ung Hwang , by a  Grant-in-Aid for Scientific Research  ( 21570034 ) from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan  awarded to  Ikuo Nishida , and by a grant from the  French ANR  ( DIESALG ,  ANR-12-BIME-0001-02 ) awarded to  Yonghua Li-Beisson.
This means for instance that ILs with long alkyl side chains on either the cation or the anion have the surface region dominated by these tails, which are oriented away from the bulk. However, if the alkyl chains are short or functionalized different effects may occur. On a first set of experimental determinations, we have measured liquid density and surface tension data in temperature ranges up to 473 K and 532 K, respectively, for 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl) amide ILs, [C n C 1 im][Ntf 2 ] with n = 2-10, 12, and 14. 23  The aim was to test the validity of the above-mentioned theories. Figure  6  shows that the surface tension decreases with increasing temperature and with the increase of the alkyl side chain length along the [C n C 1 im][Ntf 2 ] family. The decrease is more significant from [C 2 C 1 im][Ntf 2 ] to [C 5 C 1 im][Ntf 2 ], less so in the [C 6 C 1 im][Ntf 2 ] to [C 12 C 1 im][Ntf 2 ] range, and shows a noticeable drop from [C 12 C 1 im][Ntf 2 ] to [C 14 C 1 im][Ntf 2 ].
Acknowledgements Financial support provided by  Fundação para a Ciência e Tecnologia (FCT)  through projects  FCT-ANR/ CTM-NAN/0135/2012 ,  PTDC/QUI-QUI/117340/2010  and  UID/QUI/00100/2013. K. S. and A. A. F. acknowledge the postdoctoral grants  SFRH/BPD/94291/2013  and  SFRH/ BPD/94299/2013 , respectively.
The stability with time of the values measured by the pipes indicates that the sludge structure is steady despite the passage through the pump. An electromagnetic flow meter device is used to measure the flow rates in the range 3. 3 to 9. 2 L/min. Two differential pressure transducers are set on each pipe to monitor pressure drops across the pipes. An online data acquisition system is used to record different parameters as a function of time, such as the temperature, the torque and shear rate for the HRI system as well as pressure drops and volumetric flow rates for the PF system. Since the pipes are note regulated in temperature, it has been verified that the variation  in temperature is not significant between the entrance and the exit of the pipes (circulation times in pipes of only a few seconds).
8 in the case of our helical ribbon and reactor, to see that our experimental value is in reasonable agreement. In the case of the k p constant, the correlation proposed by Kappel et al. [34]  gives for our helical ribbon and reactor a calculated value of 182 which is also in good agreement with the experimental value. Once bioreactor rheograms sð _ c MO Þ have been drawn, modelling is performed for each TSS concentration using the HB model. HB parameters are determined using an iteration calculation (MatlabÓ program) that has been described in details in a previous paper  [6].
At lower temperatures where longer test times are required, near-constant pressure profiles over the longer test times were achieved by using driver inserts. Further details on driver insert design can be found in Hong et al. [16]. Ignition delay time data were acquired at both the sidewall and endwall locations. In experiments with strong energy release at ignition, blast waves forming immediately adjacent to the endwall can move and perturb the normally stagnant test gas mixtures observed at the side wall location. In experiments with very short ignition delay times (e. g. sub 50-100 s) this perturbation can artificially shorten the observed ignition delay time. In these cases endwall data is preferred  [17].
NUI Galway rapid compression machine The NUI Galway RCM has been described in detail by Darcy et al. [18]. Briefly, it is a duplicate of the original NUI Galway RCM  [18]  which is characteristically different from most other RCMs in that it has a twin-opposed piston configuration as described previously  [18]. A typical pressure trace obtained from this facility is included Fig. 9(a). It outlines the ignition delay definition used in all experiments. The adiabatic compression/expansion program of GasEq  [9]  is used to calculate the compressed gas temperature and pressure. The ignition delay time, was defined as the time from the end of compression to the maximum rate of pressure rise. End of compression is defined as the first local maximum in pressure. For both the NUI Galway high-pressure shock tube and RCM experiments, propene gas was obtained from Sigma Aldrich at > 99% purity.
The reactants are brought to the EOC conditions in approximately 30-50 ms, depending on the volumetric compression ratio and the pneumatic driving pressure chosen. Fuel and oxidizer are premixed in large stainless steel mixing tanks to facilitate performing many experiments during one day. Two mixing tanks are used: one is used to hold a reactive mixture, and is approximately 17 L in volume; the other is approximately 16 L and used to hold a non-reactive mixture, whose utility will be discussed below. The reactants (C 3 H 6 /O 2 /N 2 /Ar) are filled at room temperature to the calculated partial pressure required to achieve the mole fractions outlined in Table  3. Note that the 10 atm data used diluent mixtures consisting of 50% N 2 /50% Ar; the 40 atm data used a diluent of pure N 2. During filling of the mixing tanks, the pressure is monitored by a static pressure transducer.
Texas A&M flames Flame speed was measured using the stainless steel, constant-volume bomb at Texas A&M University. The design and details of this vessel are explained in depth in Krejci et al. [31]. In brief, the vessel has an internal diameter of 32 cm and a length of 28 cm. Given this relatively large inner diameter, flames can be measured under near-constant-pressure conditions to a diameter of 12. 7 cm. The vessel was filled with research grade Propene (99. 5%) and Primary Standard Air using the partial pressure method using a 0-1000 Torr pressure transducer. All experiments were conducted at room temperature (296 K ±2 K). The gaseous mixture was ignited by a central spark ignition system. Experiments were conducted over a wide range of equivalence ratios (φ) from 0. 7 to 1. 7. Experimental data were collected using a high-speed camera (Photron Fastcam SA1.
1) at 3,000 fps using a Z-type Schlieren setup with a Hg lamp light source  [31]. The images were processed using a MATLAB-based edge-detection program. From this program, un-stretched laminar flame speeds and Markstein Lengths were calculated using the well-known linear Markstein relation. Burned flame speeds were converted to unburned flame speeds using the density ratio calculated from the equilibrium flame chemistry. The un-stretched laminar flame speed was also calculated using the appropriate nonlinear model as shown by Chen  [26]. No appreciable difference in flame speed (within the experimental uncertainty) was found between the nonlinear and the linear methods. Experiments proved to be repeatable with a calculated uncertainty based on systematic error and random uncertainty as outlined by Krejci et al.
Ignition delay time results Ignition delay time measurements from six different shock tubes and two different rapid compression machines (RCMs) are presented. Ignition delay times are displayed on Arrhenius-type plots, with the ignition delay on a log scale plotted as a function of reciprocal reflected-shock temperature in the case of the shock tube data and compressed temperature for the RCM. The experimental data are compared to the predictions of the current mechanism and in some cases the previous mechanism (AramcoMech 1. 3  [38] ). The solid symbols represent data obtained from NUI Galway, vertically half-filled symbols from Stanford, open symbols from TAMU, crossed open symbol from RPI, open symbols with a vertical line from UConn and horizontally half-filled symbols from KAUST. In general, there is good agreement among the predictions of the current mechanism and the experimental measurements.
There are some inconsistencies in the flame speed extrapolation using spherical flames with different chamber shapes and sizes especially when the mixture Lewis number is much less or larger than unity  [42]. Conservative estimates of radiation-induced uncertainty in spherically expanding flames  [27]  predict a maximum inhibition of 3. 7% for the PU measurements and 6. 1% for the TAMU measurements. Radiation heat loss has a stronger effect in TAMU flames due to the large flame size utilized. However, the use of a large flame size in TAMU reduces extrapolation uncertainty due to the low stretch rate. Data from Davis et al. [5]  were obtained using counterflow flames without nonlinear extrapolation. Data without appropriate non-linear extrapolation may lead to higher flame speeds. Heat flux burners were used for measurements from VUB, Lund, and LRPG.
These burners typically report lower uncertainty than spherical and counterflow flames due to the reduced stretch effects. However, the flat flames may introduce uncertainties due to the flame instability and reduced sensitivity of temperature gradient to flow velocity, especially on the rich side. Mass Flow Controllers (MFCs) also influence the overall uncertainties, the relative errors in mass flows increases when smaller flows are produced  [30]. Under fuel-lean conditions the data from PU, VUB, and TAMU, LRPG, and Lund show good agreement with each other and with the mechanism predictions. However, under fuelrich conditions only the VUB and TAMU measurements agree with each other and the model prediction. PU data shows good agreement with the data of Davis et al. [5]  but are faster than the VUB and TAMU data.
Conclusions New ignition delay time measurements were carried out in shock tubes and rapid compression machines at higher pressures and lower temperatures than previously published in the literature. The results presented in this study greatly expand the ignition delay time database available for mechanism validation for propene oxidation. Further, this is the first study of its kind to cross-validate ignition delay time measurements among six different shock tube and two different RCM facilities. Particularly for shock tubes, there is good agreement among measurements, which suggests that experimental shock tube IDTs can be wellreproduced even considering experimental method-and facility-specific variations. The current IDT measurements did not show any evidence of NTC-like behavior under the conditions studied.
New laminar flame speed measurements were obtained over a range of equivalence ratios, dilutions, and pressures from three different facilities using different methods. Measurements presented in this study considerably extend the pressure range of the propene laminar burning rate database up to 20 atm. There is good agreement among the facilities for fuel-lean mixtures; however, there are notable discrepancies under-fuel rich conditions, highlighting the need for further refinement of laminar flame speed experimental methods and a subsequent reduction in the lab-to-lab measurement uncertainties. A new chemical kinetic mechanism has been validated against the new ignition data and is able to reproduce experimental measurements across a range of temperatures, pressures, and mixtures. Reactions shown to be sensitive in the prediction of propene ignition delay time include reactions between propene and molecular oxygen and propene and hydroxyl radicals.
Acknowledgements The Rensselaer group was supported by the  U. S. Air Force Office of Scientific Research  (Grant No. FA9550-11-1-0261 ) with  Dr. Chiping Li  as technical monitor. Work at the  University of Connecticut  and at Princeton University was supported as part of the  Combustion Energy Frontier Research Center , an  Energy Frontier Research Center  funded by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Science , under Award Number  DE-SC0001198. The work at  Stanford University  was supported by the  Air Force Office of Scientific Research  under the AFRL Integrated Product Team, with  Dr. Chiping Li  as contract monitor.
The time or frequency domain, covered by the relaxation or the complex modulus, respectively, tends to cover several orders of magnitude. Hence, it is difficult to cover experimentally the complete viscoelastic domain, and based on the time-temperature superposition principle, the so-called master curves of relaxation and complex modulus are constructed. The method is explained, for example, in  Brinson (2008) ,  Williams et al. (1955). The objective here is to propose a viscoelastic constitutive model with prestrain sensitivity capable of predicting data from relaxation and DMA experiments. The main features of the experimental procedure are summarized in Fig. 2. In both cases, strains are imposed, stresses are measured, and the moduli are computed from the measured data. The data will be first collected in the format of a master curve encompassing several orders of magnitudes in the time or frequency range and used after for identification of the model.