dataesr/scientific-paragraphs-categorization huggingface dataset:
Cyclic voltammetry (CV) CV measurements were carried out via a VSP potentiostat driven by EC-Lab V10. 40 software from Bio-Logic (Bio-Logic Science Instruments, France). A three-electrode system consisted of a glassy carbon working electrode (GC, disk diameter 2. 0 mm, geometric area 0. 031 cm 2 ), a metallic stainless-steel counter electrode and a saturated calomel reference electrode (SCE) was used. All experiments were performed in 0. 1 M ammonium acetate solutions (pH 3. 5, 7. 4 and 10) as supporting electrolyte in water/acetonitrile (50/50, v/v), and cyclovoltammograms were recorded at a scan rate of 50 mV. s -. DIU was dissolved to reach concentrations of 1 mM. Additional experiments were carried out in the presence of 10 mM of glutathione (GSH). Although not present in the environment, the thiol function of glutathione can be used as a model for nucleophile addition/ trapping of electrophile entities.
The system returned to initial conditions within 0. 5 min and was kept for 1. 5 min to allow column equilibration. Injection volume was set to 2 µL for all samples. Electrospray parameters in the positive ionization mode were set as follows : capillary voltage 0. 5 kV, cone voltage 3 V, source temperature 120 °C, source offset 80 V, desolvation temperature (N2) 350 °C, cone gas flow (N2) 80 L/h, desolvation gas flow (N2) 600 L/h. Leucine Enkephalin at a concentration of 50 pg/µL ([M+H] + at m/z 556. 2771) was used as a lockmass compound on the lockspray channel at the flow rate of 10 μL/min and acquired for 0. 5 sec every 5 sec. MS data were acquired in the range m/z 50 to 800 in the centroid mode. A scan time of 0. 5 sec was using during LC-HRMS and LC-HRMS 2 experiments. LC-HRMS 2 experiments were performed with a collision energy ramping from 10 to 40 V.
Mass voltammetry: EC-MS EC oxidation was carried out using an electrochemical thin-layer cell (µPrepCell TM , Antec, Zoeterwoude, The Netherlands) equipped with a boron-doped diamond working electrode (BDD, Magic Diamond, Antec), a HyREF TM (Pd/H2) pseudo reference electrode and a titanium auxiliary electrode. A solution of DIU (0. 1 mM, in a 20 mM ammonium acetate solution pH 7. 4 in water/acetonitrile (50/50, v/v)) was pumped through the cell at a flow rate of 25 µL/min using a syringe pump (gas tight syringe 549-0538, VWR Collection, France), and the outlet of the cell was connected directly (online) to the ESI source of the mass spectrometer (Xevo G2-XS QToF from Waters). Experiments were performed by linear ramping of the working electrode potential from 0 V to 2 V versus Pd/H2 pseudo reference electrode at the rate of 2 mV/s, using an EmStat portable potentiostat driven by PSTrace software from PalmSens instruments (PalmSens, Randhoeve, The Netherlands).
Acknowledgments RCTT acknowledges the support of both  Université de Nantes ,  CNRS  and  SCAC (Service de Coopération et d'Action Culturelle, French Embassy in Cameroon)  for a travel grant. The authors greatly acknowledge the AMaCC platform ( CEISAM UMR CNRS 6230 ,  University of Nantes ) for mass spectrometry analyses. Quantum mechanical calculations were carried out using HPC resources funded by the  French National Agency for Research  with " Investissements d'Avenir"  ( ANR-11-EQPX-0004 ).
Acknowledgments This work was supported by the  National Key R&D Program of China  (Grant No. 2018YFA0306303 ), the  National Natural Science Foundation of China  (Grants Nos. 11834004 ,  61690224 , and  11761141004 ), the  111 Project of China  (Grant No. B12024 ), the Projects from  Shanghai Science and Technology Commission  (No. 19JC1412200 ),  ISF-NSFC joint research program  (Grant No. 2520/17 ),  CNRS , the  ERDF Operational Program-Burgundy , the  EIPHI Graduate School  (Contract No. ANR-17-EURE-0002 ), the  Associate (CNRS& Weizmann) International ImagiNano Laboratory , and  Israel Science Foundation  (Grant No. 746/15 ). I. A. acknowledges the support as the  Patricia Elman Bildner Professorial Chair. This research was made possible in part by the historic generosity of the  Harold Perlman Family.
This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  Ministere de l'Enseignement Supeŕieur et de la Recherche  (MESR). P. E. M. V. thanks also the  CONACYT  agency for his doctoral financial support n°553260/295449. For the sake of clarity, ACS Omega http://pubs. acs. org/journal/acsodfMini-Review.
S-1a, ESI †). The form factor P(Q) of the nanoparticles is determined from several measurements at a low nanoparticle concentration in water and this curve is used for all samples in ionic liquids to determine structural factors S(Q) using S(Q ) ¼ I(Q)/(F NP Â contrast Â P(Q)). The value of S(Q / 0) therefore compares the studied dispersion to a "reference" dispersion of individual nanoparticles without interparticle interaction. The difference can be due either to interparticle interaction or to a change of the scattering objects (for example formation of small aggregates). Evolution of the size or shape of the nanoparticles aer transfer to the IL can be discarded from the scattered intensity at a large Q. As normalized intensities in water and the ionic liquid are similar, the nanoparticles are the same (shown by the SANS data in Fig.
By using the ZSimpWin 3. 21 software, the values of all above-mentioned parameters were estimated, as can be seen from Table  3. By comparing the Rct value obtained for bare GC electrode, and the value obtained for all modified electrode, one can conclude that the presence of the high conductive CXBi-Fex nanocomposite matrix on the GCE surface led to a significant diminish of the charge transfer resistance (Rct). On the other hand, the Rct value increases with the Fe concentration in the CXBiFex nanocomposite matrix. As previously observed, the nanoparticles are.
By using the ZSimpWin 3. 21 software, the values of all above-mentioned parameters were estimated, as can be seen from Table  3. By comparing the R ct value obtained for bare GC electrode, and the value obtained for all modified electrode, one can conclude that the presence of the high conductive CXBiFe x nanocomposite matrix on the GCE surface led to a significant diminish of the charge transfer resistance (R ct ). On the other hand, the R ct value increases with the Fe concentration in the CXBiFe x nanocomposite matrix. As previously observed, the nanoparticles are primarily found in oxidized states, also with lower crystallinity and poor conductivity  [29]. Such inclusions may also alter the connectivity of the electron conducting matrix, increasing the capacitive response of the system.
Acknowledgments: The authors wish to thank the  Region Grand Est (France)  for the grant " MIPPI-4D ". This research project is supported by  China Scholarship Council (CSC)  (No. 201808440451 ). PX acknowledges funding from the  "Australian Research Council  ( FT170100301 )". This work was granted access to the  HPC resources of the Mesocentre of the University of Strasbourg.
Funding:  China Scholarship Council (CSC)  for  Ke Sun. The  Agence Nationale de la Recherche  ( ANR agency ) is acknowledged for funding through the PhD grant of  Corentin Pigot  ( ANR-17-CE08-0010   DUALITY  project). The  Direction Générale de l'Armement (DGA)/Agence Innovation Defense (AID)  is acknowledged for its financial support through the PhD grant of  Damien Brunel.
Recently synthetized iron complexes have achieved long-lived excited states and stabilities which are comparable, or even superior, to their ruthenium analogues, thus representing an eco-friendly and cheaper alternative to those materials based on rare metals. Most of computational tools which could help unravel the origin of this large efficiency rely on ab-initio methods which are not able, however, to capture the nanosecond time scale underlying these photophysical processes and the influence of their realistic environment. Therefore, it exists an urgent need of developing new low-cost, but still accurate enough, computational methodologies capable to deal with the steady-state and transient spectroscopy of transition metal complexes in solution. Following this idea, here we focus on the comparison between general-purpose transferable force-fields (FFs), directly available from existing databases, and specific quantum mechanical derived FFs (QMD-FFs), obtained in this work through the Joyce procedure.
For the definition of the QMD-FF, among others  [69, [71] [72] [73] [74] [75] [77] [78] [79] , we here resort to the JOYCE protocol  [78, 85, 86] , already successfully employed to study the solvation features of octahedral TMCs  [76, 87] , based on higher-level QM descriptors. Turning to the general-purpose FF, as an example of a transferable black-box approach, the standard General Amber Force Field (GAFF) method  [88]  was adopted whenever possible, by transferring all bonded FF parameters from the proper GAFF database, while retrieving the missing parameters (involving the metal and Boron sites) from supplementary literature databases, as described in section 3. 1. 2. Initially, we have tested the reliability of both FFs to reproduce zero temperature structures using the QM ground state and Xray diffraction (XRD) data geometries for validation.
The latter effect is also reflected in the simulated optical spectra, for which a sampling performed using QMD-FF is mostly beneficial. However, TD-DFT calculations suggest that no major influence of the solvent shell structuration is observed, this in turn will point to an expected marginal role of For the definition of the QMD-FF, among others  [69, [71] [72] [73] [74] [75] [77] [78] [79] , we here resort to the Joyce protocol  [78, 85, 86] , already successfully employed to study the solvation features of octahedral TMCs  [76, 87] , based on higher-level QM descriptors. Turning to the general-purpose FF, as an example of a transferable black-box approach, the standard General Amber Force Field (GAFF) method  [88]  was adopted whenever possible, by transferring all bonded FF parameters from the proper GAFF database, while retrieving the missing parameters (involving the metal and Boron sites) from supplementary literature databases, as described in Section 3.
GbFF Parametrization The GbFF parameters for the metal complex, the solvent and the counterions have been built without any type of previous parameterization, only by employing tools and parameters which are already available in the literature. For the metal complex, all parameters involving the standard atoms (C, H, N) have been obtained with the antechamber program  [93]  available in the AMBER16 package suite of tools. The remaining parameters related with B and Fe atoms, which are not available in AMBER16 libraries  [94] , were calculated using different methods, aiming to keep the FF implementation as standard as possible. In the case of the B atom, all bonded parameters were obtained from DREIDING force field  [95]. The equilibrium distance of the metal ion with the carbene ligands were calculated averaging the bond distances obtained by using the Z-matrix method as implemented in the Metal Center Parameter Builder (MCPB) Python program  [96].
Following the standard AMBER procedure, we estimated the atomic point-charges of our metal complex by computing the RESP charges calculated at the HF/6-31G* level of theory, as implemented in Gaussian 16 package  [100]. For the description of the acetonitrile (ACN) solvent and the counterion (PF 6 -) we relied on the FF parametrization and atomic charges from references  [101]  and  [102]  respectively. Finally, all FF parameters were transformed into GROMACS file format by using the ACPYPE-AnteChamber PYthon Parser interface program  [103].
MM/MD Simulations The Molecular Dynamics (MD) simulations were conducted within GROMACS package suite of programs  [64]. The MM optimized structure of the Fe complex was solvated into a cubic box containing ∼ 1000 acetonitrile molecules, previously equilibrated through NPT runs, at 1 atm and room temperature (298 K). A number of solvent molecules was also substituted with one PF 6 -ion to assure electroneutrality. The final configuration resulted in a 50 × 50 × 50 Å box containing the Fe complex, its counterion and 979 acetonitrile solvent molecules. A first 100 ps simulation was performed in the NVT ensemble, using a time step of 0. 1 fs with constrained applied to the Fe complex to freeze it to its equilibrium conformation. All velocities were assigned based on a random Maxwell-Boltzmann distribution at 298 K, and the temperature kept constant through the velocities rescale thermostat of Bussi et al.
Contributions: Conceptualization, G. P. , A. F. -M. , A. M. and M. P. ; Joyce implementation, G. P. ; GbFF methodology, V. D. -C. ; MD analysis, G. P. and V. D. -C. ; TD-DFT analysis, V. D. -C. ; writing-original draft preparation, V. D. -C. , G. P. , M. P. ; writing-review and editing, A. F. -M. and A. M. ; All authors have read and agreed to the published version of the manuscript. Funding: V. D. -C. acknowledges financial support through the COMETE project (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie) co-funded by the European Union under the program 'FEDER-FSE Lorraine et Massif des Vosges 2014-2020. M. P. thanks ANR JCJC HELIOSH2 (ANR-17-CE05-0007-01) for financial support.
Acknowledgments: HPC resources from the mésocentre  EXPLOR of Université de Lorraine  (Project  2018CPMXX0602 ) and LPCT local computational resources are also acknowledged. A. F. -M. is grateful to  Generalitat Valenciana  and the  European Social Fund  for a postdoctoral contract ( APOSTD/2019/149 ) and the  Ministerio de Ciencia e Innovación  (project  CTQ2017-87054-C2-2-P ) for financial support.
Author Contributions: Conceptualization, G. P. , A. F. -M. , A. M. and M. P. ; Joyce implementation, G. P. ; GbFF methodology, V. D. -C. ; MD analysis, G. P. and V. D. -C. ; TD-DFT analysis, V. D. -C. ; writing-original draft preparation, V. D. -C. , G. P. , M. P. ; writing-review and editing, A. F. -M. and A. M. ; All authors have read and agreed to the published version of the manuscript. Funding: V. D. -C. acknowledges financial support through the COMETE project (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie) co-funded by the European Union under the program 'FEDER-FSE Lorraine et Massif des Vosges 2014-2020. M. P. thanks ANR JCJC HELIOSH2 (ANR-17-CE05-0007-01) for financial support.
35 he experimental script for the acquisition of 2D mass spectra is shown in Scheme 1. All excitation pulses (P 1 , P 2 , and P 3 ) have a frequency range of 357,144. 31-71,669. 99 Hz (corresponding to m/z 301. 08-1500). The frequency list was composed of 458 frequencies with a 624. 71 Hz decrement. For the encoding pulses P 1 and P 2 , the power level (corresponding to the amplitude of the pulses) was set at 106 V pp with a pulse length of 1. 0 μs per frequency. For the excitation pulse P 3 , the power level was set at 80 V pp with a pulse length of 20 μs per frequency (default settings in the Apex Control software, Bruker Daltonik, GmbH, Bremen, Germany). 36 The fragmentation mode used was ECD, using a hollow cathode. 37 The heater was at 1. 3 A, the lens at 20 V, the ECD bias at 2. 0 V, and the irradiation pulse at 0.
03 s. The transients were recorded with 1 Mwords (16 bits) for 1. 4680 s, with a 714,288. 62 Hz sampling frequency. For the broadband 2D mass spectrum, the encoding period t 1 was incremented 4096 times with a 2. 0 μs increment, leading to a 250 kHz Nyquist frequency (mass range: m/z 334. 439-1500 with the high m/z limit set by the frequency range of P 1 ). The longest encoding period was 8. 192 ms. For the narrowband 2D mass spectrum, the encoding period t 1 was incremented 4096 times with an 8. 0 μs increment, leading to a 62. 5 kHz Nyquist frequency (mass range: m/z 415. 00-546. 825). The longest encoding period was 32. 768 ms. For neither 2D mass spectrum was the detection heterodyne. Data Processing. The 2D mass spectra were processed and visualized using the SPIKE software developed independently by the University of Strasbourg and CASC4DE (Illkirch-Graffenstaden, France) in a 64-bit Python 3.
Entropy-based sensor selection method is proposed in  (Liu, Wang, Liu, Zhang, & Peng, 2015)  for prognostics. This method quantifies the trend representing the failure degradation for given sensory dataset and entropy is used to represent the uncertainty within the data. Trigonometric functions and their cumulative transformation have been used to extract monotonic features. The goodness of the features for prognostics has been quantified by analysis of monotonicity and trendability  (Javed, Gouriveau, Zerhouni, & Nectoux, 2015). Monotonicity is the continuous increasing or decreasing nature of the feature and quantified as the sum of positive and negative derivatives. Trendability is basically defined as the correlation of a feature with time. Feature extraction and selection processes affect the data structure.
Simulation Results The model is trained using some selected samples first and then the trained model is used for testing unused samples. In training, all extracted features from the selected samples went through segmentation, selection and fusion steps. In testing, the same selected features in training step are also selected from testing samples and are used in the fusion algorithm to estimate the SoH. In the simulated dataset, SoH degradation has been simulated first. Figure  2  displays the simulated SoH. Then, features that are functions of the simulated SoH degradation have been generated. 20 features have been simulated based on this simulated SoH degradation. Each feature is created based on one or two functions of SoH with some noise. When two functions are used, the first function represents some part of the SoH degradation, whereas the second one represents the remaining part of the SoH degradation.
4343{ E/R(1/T -1/298 K)} Eq. ( 450 The assignment of these absolute uncertainties in k and E/R is our subjective assessment. They are not determined by a rigorous, statistical analysis of the database, which is generally too limited to permit such an analysis. Rather, the uncertainties are based on our knowledge of the techniques, the difficulties of the experimental measurements, the potential for systematic 455 errors, and the number of studies conducted and their agreement or lack thereof. Experience shows that for rate measurements of atomic and free radical reactions in the gas phase, the precision of the measurement, i. e. the reproducibility, is usually good. Thus, for single studies of a particular reaction involving one technique, standard deviations, or even 95% confidence limits, of ±10% or less are frequently reported in the literature.
Context. Studies of the dust distribution, composition, and evolution of protoplanetary disks provide clues for understanding planet formation. However, little is known about the innermost regions of disks where telluric planets are expected to form. Aims. We aim constrain the geometry of the inner disk of the T Tauri star RY Lup by combining spectro-photometric data and interferometric observations in the near-infrared (NIR) collected at the Very Large Telescope Interferometer (VLTI). We use PIONIER data from the ESO archive and GRAVITY data that were obtained in June 2017 with the four 8m telescopes. Methods. We use a parametric disk model and the 3D radiative transfer code MCFOST to reproduce the spectral energy distribution (SED) and match the interferometric observations. MCFOST produces synthetic SEDs and intensity maps at different wavelengths from which we compute the modeled interferometric visibilities and closure phases through Fourier transform.
Results. To match the SED from the blue to the millimetric range, our model requires a stellar luminosity of 2. 5 L , higher than any previously determined values. Such a high value is needed to accommodate the circumstellar extinction caused by the highly inclined disk, which has been neglected in previous studies. While using an effective temperature of 4800 K determined through high-resolution spectroscopy, we derive a stellar radius of 2. 29 R. These revised fundamental parameters, when combined with the mass estimates available (in the range 1. 3-1. 5 M ), lead to an age of 0. 5-2. 0 Ma for RY Lup, in better agreement with the age of the Lupus association than previous determinations. Our disk model (that has a transition disk geometry) nicely reproduces the interferometric GRAVITY data and is in good agreement with the PIONIER ones.
Complementary data VLTI/PIONIER RY Lup was observed in the H-band with the PIONIER instrument of the VLTI  (Le Bouquin et al. 2011). We used two datasets obtained with two different configurations of the auxiliary telescopes (ATs; Table  1 ). The data were reduced with the standard PIONIER pipeline. RY Lup is partially resolved in the H-band with visibilities squared between 0. 4 and 0. 9 (Fig. 1-bottom ). The visibilities quickly decline at the base-lines between zero to a few meters, which is generally interpreted as the contribution of a component that is much more extended than the angular resolution of the interferometer. Such a contribution can be interpreted as scattered light, as described in detail in  Pinte et al. (2008a)  and as applied in  Anthonioz et al. (2015)  who derive a ratio of 0.
A transition disk model for RY Lup We used the radiative transfer code MCFOST to model the SED, the NIR, and sub-mm images, and the NIR H-and K-band interferometric data of RY Lupi. MCFOST is a 3D continuum radiative transfer code based on the Monte Carlo method (see  Pinte et al. 2006 Pinte et al. , 2009 , for a complete description and benchmarking). MCFOST includes multiple anisotropic scattering when-Fig. 2. Spectral energy distributions corresponding to our models of RY Lup: M50 for an inclination of 50 • with r in = 0. 12 au (black solid line) and r in = 0. 17 au (red dashed line), and M70 for an inclination of 70 • (black dotted-line) compared with the photometric measurements (orange circles; Table  2 ), the X-Shooter spectrum (blue crosses), and the Spitzer/IRS spectrum (red plus).
e. , 60 mas for the UTs and 250 mas for the ATs). For model M50, we needed to slightly adjust the inner disk radius to fine tune the adjustment of the visibility curves, while maintaining a proper match to the SED. An inclined disk (with an inclination of ∼50 • ) and an inner dust rim R in at 0. 12 au correctly fits the SED and the NIR interferometric observations (Fig. 3 ). We note that reducing the inner radius to 0. 12 au entails an increase in the maximum dust temperature at the inner disk up to 1700 K, slightly beyond to adopted sublimation tempera-ture of 1500 K, but still reasonable. The fit of the high-quality GRAVITY visibilities is excellent. Our model is also in agreement with the PIONIER data, considering the larger error bars of these datasets, even if the difference between the data and the model is larger.
Such a difference could come from the variability of the source because the geometry of the inner disk is expected to be very similar in both the H-and K-bands. Contemporary interferometric datasets are needed to investigate this in more detail. We also verified that our M50 model leads to closure phase signals that are consistent with ∼ 0 • , as observed by both GRAVITY and PIONIER (Fig. 3 ). This is the case because even if the central part of the disk model is not centro-symmetric, its compactness (1 mas or less) is such that it is only partially resolved by GRAVITY as shown by the squared visibilities higher than 0. 55, even at the longest baselines. The parameters of our final model M50 are given in Table  3. For the M70 model, we failed to correctly reproduce the GRAVITY visibilities  (Fig 4) , even when changing the inner rim position which is about three times further away from the star than for the M50 model due to the higher luminosity.