dataesr/scientific-paragraphs-categorization huggingface dataset:
Analytical conditions The method was developed and adapted from previous published data  [28]. UPLC-MS/MS analysis was performed using an Acquity UPLC H-Class System coupled to a Xevo TQD tandem mass spectrometer (Waters©). HSS T3 analytical column (2. 1 x 50 mm; 1. 8 µm) was used to chromatographically separate molecules. Multiple ion monitoring chromatograms were acquired and analysed using Masslynx 4. 1 software (Waters ©). Frozen samples were thawed at room temperature  (25 min). IS solution was spiked on filter (25 µL) and acetic acid (1 %-15 mL) were added, mixed for 20 min and extracted by solid-phase extraction (SPE) Atoll XC 100/3, using the following procedure: (i) conditioning the cartridge with 2 mL of methanol, followed by 2 mL of desionised water; (ii) loading the samples prepared; (iii) drying for 25 min under 10mbar; and (iv) eluting with 1800 µL of methanol and evaporating for 60 min at 45°C.
Wipe samples were performed with cellulose filters in delimited rubber surface. Different wiping papers have been studied in previous studies  [34] , and according to these experiments, the best recovery values were achieved with whatman paper. Our experimental procedure was close previously published data  [23, 36]  including optimal performance with FA. However, conversely to previous data using aquous pH3 solution  [37] , water/methanol  [38]  , ethyl acetate/methanol  [24]  or FA/acetonitrile  [23] , we have chosen to exclude the use of organic solvent to limit inhalation exposure to lab technician, explaining, in part, low global recovery % of our analytes (Table  4 ). However, our limits of detection remain very interesting compared to the previous published data  [36, 38]  (see above paragraph).
Imprecision and inaccuracy QC samples at different concentrations were used to determine within-run and between-run precision and accuracy were determined with QC samples, as described in the experimental section. Data for within-run and between-run precision and accuracy of the method are presented in Table  4. Within-run imprecision ranged between 8. 3 and 16. 9 % (LLOQ), 7. 6 and 11. 7 %, 11. 4 and 14. 9 %, 6. 3 and 11. 8 %; 4. 5 and 13. 7 %; 7. 3 and 16. 5 % (LLOQ) for 5FU, DOXO, EPI, CP, IF and GEM respectively. Between-run precision ranged between 11. 3 to 18. 1 % (LLOQ); 6. 6 to 10. 6 %; 10. 6 to 14. 2 %; 6. 9 to 19. 9 % (LLOQ); 4. 7 to 16. 4 % (LLOQ) and 7. 5 to 15. 1 % (LLOQ) and the range of accuracy was -6. 7 to -0. 7 %; -10. 9 to 1. 1 %; -9. 0 to 2. 9 %; -7. 7 to 1. 1 %; -2. 5 to 4.
All measurements were made with Metrohm 713 pH meters and a [N2225][NTf2] salt bridge. 1  for solutions that were used by different groups). The differences between the experimentally obtained pH and the expected buffer pH value (according to the label) are plotted. The uncertainty bars are the consistency standard deviations of the ladders. Within the series, the results are ordered from high to low pH of the buffer, and anchor points (pH 7. 00 in all cases) are omitted from the graph. Orange lines represent the target uncertainty (k = 1). The consistency standard deviations of the obtained pH ladders are given in Table  4. Here, the standard deviation is smaller than the LJP cancellation assumption (approximately 0. 1 pH units) between different solvents. Data from all obtained pH ladders were combined into Figure  5.
The results from the first experimental realization  [16]  and unpublished validation results of the setup used in  [20]  are added for comparison. Both were performed using a saturated aqueous KCl salt bridge. In addition, some unpublished results from 2015 were added and measured with the same instrument as in FC. Data from all obtained pH ladders were combined into Figure  5. The results from the first experimental realization  [16]  and unpublished validation results of the setup used in  [20]  are added for comparison. Both were performed using a saturated aqueous KCl salt bridge. In addition, some unpublished results from 2015 were added and measured with the same instrument as in FC. ID and UT in 2020, but with different cells. All measurements were made with Metrohm 713 pH meters and a [N2225][NTf2] salt bridge.
Discussion The quality of pH H 2 O abs measurement is largely determined by the pH H 2 O abs system design. The influence of different cell designs on the reliability of the measurements was estimated in steps. All the cells tested are suitable for unified acidity measurements, as is evident from Figure  5 , where the majority of results lie within the target uncertainty or show significant overlapping uncertainty. The standard deviation of all the measured ∆∆pH values is 0. 04. This validation highlights that the method is, to a large extent, independent of the user and the used equipment, including the cell geometry. The method is robust enough for a wider adoption of the concept. Most important is that all the cells are symmetrical, even if not perfectly. In addition to cell symmetry, the quality (consistency standard deviation) of the fitted ladder data depends also on the electrodes and instrument.
Unified acidity values (pH H 2 O abs values) can be measured with various instruments and cell designs. All used experimental setups were found suitable for pH H 2 O abs measurements. The method is robust enough for a wider adoption of the concept, and the results from different setups are comparable. Symmetry of the measurement cell is important and helps to improve the accuracy of the measurement results, hence the pH in a Faraday cage and an analog filtering at 5 Hz was carried out to limit the electromagnetic noise and obtain a smooth signal. The samples were thermostated at (25 ± 0. 05) • C using a cryothermostat LAUDA ECO RE 2025 GN. The data were collected every 5 s. The most stable data over a 15 min (around 200 points) period were used for subsequent data treatment. PTB: A Keysight B2987A Electrometer/High Resistance Meter with Quick IV Measurement Software was used for measurements.
The sampling interval was 10 s. The data were collected for 1 h and the average of points 1800 s to 3600 s was used. A LAUDA Proline RP845 was used to maintain temperature at 25. 0 • C. A Metrohm 6. 0750. 100 Ag/AgCl 3 M KCl reference electrode was used for the glass electrode calibration experiments. A Faraday cage was not used. Metrohm EtOH-trodes, combined glass electrodes, were used for unified pH measurements. Only the signal from glass electrode part was used. TUBITAK UME: A Metrohm 916 Ti-touch potentiometric titration system with a differential amplifier (Metrohm 6. 5104. 030) was used for the potentiometric measurements. The data were collected at intervals of 15 min for 2 or 3 h and the last two datasets were used. A Labo C200-H13 circulated thermostatic water bath was used to provide a stable temperature at 25.
Funding: This research was funded from the  EMPIR programme  (project  17FUN09  " UnipHied ", www. uniphied. eu)co-financed by the Participating States and from the  European Union 's  Horizon 2020 research and innovation programme. The work at  UT  was supported by the  Estonian Research Council  grant ( PRG690 ) and by  EU  through the  European Regional Development Fund  under project  TK141  " Advanced materials and high-technology devices for energy recuperation systems " ( 2014-2020. 4. 01. 15-0011 ). FC. ID, whose work was developed in  Centro de Química Estrutural, thanks Fundação para a Ciência e Tecnologia  for funding under project  UID/QUI/00100/2020. The APC was funded by  MDPI.
, 2014)  with R version 3. 5. 2. (R Core Team, 2018) in Rstudio software (version 1. 1. 456). Denominator degrees of freedom were approximated by using the Satterthwaite procedure in the lmer Test package  (Kuznetsova et al. , 2017)  and species-specific temporal slope estimates were inferred using the phia package (de Rosario-Martinez, 2015). To assess the relationships between bacterial abundance and process performance parameters (sulfate, thiosulfate, and MT concentrations), we used linear regression analysis. For this analysis of variance, we used the average values of technical triplicates because we had only a single measurement of the process performance parameters for each sample.
Analytical techniques A detailed description of the methods and techniques mentioned in Section 2. 7 can be found in  (Kiragosyan et al. , 2020)  Biomass quantification was based on the amount of organically bound nitrogen oxidized to nitrate by digestion with peroxodisulfate (LCK238 and LCK338, Hach Lange, the Netherlands). Before analysis, the cell pellet was washed twice with nitrogen-free medium at 20,238 x g for 5 min to remove any residual urea and ammonia. Sulfide was measured as total HS -using a methylene-blue method in a cuvette test (LCK653, Hach Lange, USA). To assess whether the sulfur balance is closed, total S measurements were performed using inductively coupled plasma -optical emission spectrometry (ICP-OES, model, manufacturer location of manufacturer). The concentration of measured sulfur was compared with the sum of sulfate and thiosulfate (data and a full description can be found in Appendix A (in Supplementary material)).
Acknowledgments This work has been performed within the cooperation framework of  Wetsus, European Centre of Excellence for Sustainable Water Technology  (wetsus. nl), Wageningen University and Research  (wur. nl) and Paqell B. V. (paqell. com). Wetsus is co-funded by the  Netherlands' Ministry of Economic Affairs and Ministry of Infrastructure and Environment , the  European Union's Regional Development Fund , the  Province of Fryslân  and the  Northern Netherlands Provinces. Wetsus is also a coordinator of the  WaterSEED  project, which received funding from  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement No. 665874. The research of  Peer H. A. Timmers  and  Dimitry Sorokin  was supported by the  Soehngen Institute of Anaerobic Microbiology (SIAM)   Gravitation grant  ( 024.
Then, the stained samples are washed with distilled water and dried for 48 h. After staining, samples become hard and this makes possible to cut the surface at room temperature to obtain a smooth surface using a diamond knife. Before observation, samples are coated with a thin layer of conductive carbon using a sputter coater. The stained phase appeared bright and the less stained phase appeared dark in SEM  [41]. Finally, ImageJ software was used to measure the volume average radius of the dispersed morphology or the characteristic length l c for the co-continuous morphology. The measurements of the characteristic length was made by drawing lines along the length and width of the SEM micrograph and by counting the number of intersections (N) between the two phases. The characteristic length (l c ) is then deduced from the line length divided by N/2.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  under Grant No. 21722307 ,  21673233 ,  21873016 , and  21688102 , the  National Key R&D Program of China  (No. 2018YFA0306503 ), the  Strategic Priority Research Program  ( XDB17010200 ) of the  Chinese Academy of Sciences , the  Fundamental Research Funds for the Central Universities  (No. DUT18ZD202 ), and  LiaoNing Revitalization Talents Program  ( XLYC1907190 ).
2-A temperature dependent correlation for the relative permittivity of liquids for a wide temperature range In  [16] , Neau et al. presented the correlation developed by Neau and Raspo  [35]  for the representation of the relative permittivity of polar compounds with respect to temperature: 𝜀 𝑟 (𝑇) = 𝐴 0 + 𝐴 1 𝑇 + 𝐴 2 𝑇 2 + 𝐴 4 𝑇 ⁄ + 𝐴 5 𝐿𝑛(𝑇) (1) However, the parameters were fitted on "data" generated from CRC Handbook correlations valid only for temperatures lower than 400K. In the present paper, we considered a large database including temperatures up to 823K for water, 525K for methanol and 513K for ethanol (see Table  1 ). The objective was to obtain simultaneously similar results to those given by literature correlations in the temperature range where they are defined and an accurate representation of the relative permittivity in the high temperature domain; it is worth recalling that the comparison between experimental and calculated data should be performed in the limits of experimental uncertainties (about 1%-3% for temperatures larger than 400 K).
(  2 )) for water and methanol. In order to check, as for Eq. (  1 ), the capability of Eqs. (  2 ) and (3) to describe the behavior of relative permittivity at high temperatures, parameters Ai of these two equations were fitted on the database of Table  1. Resulting curves are presented in Fig. 2  together with those provided by our new correlation. The parameter fitting obviously improves the representation of relative permittivity; however, an incorrect variation is still observed for very high temperatures (T>650K for water, and T>520K for alcohols). As a conclusion, results presented herewith evidence: first, that the use of literature models, CRC and ZZC, should be restricted to modelings in their validity domain; second, that the new correlation is the only one suitable for an accurate prediction of the relative permittivity, not only in the range of validity of literature models, but also in the high temperature domain.
LIST OF TABLES Table 1. Database for relative permittivity: number of data points (N), interval of experimental temperatures (T-range in Kelvin) and references (Ref). Table  2. New correlation: fitted parameter values of Eq. (  1 ) for relative permittivity. Table  3. Parameter values of the literature correlations for relative permittivity, together with their temperature range of validity. NR: Eq. (  1 ) with parameters given in  [16] , CRC: Eq. (  2 ) with parameters of  [24] , ZZC: Eq. (  3 ) with parameters of  [25, 30]  for water and ethanol and  [29]  for methanol. Table  4. Mean percent relative deviations on relative permittivity obtained with various correlations for data described in Table  1. Interval of experimental temperatures (T-range in Kelvin, with, in parenthesis, according to Table  3 , the correlation concerned by this range of validity), number of data points (N) and name of the correlation: NR: Eq.
Electrochemical Sensing of Mercury SWV was used to investigate the electrochemical response of the ZnO/Hg 2+ -IIP sensor towards mercury in solution at different concentrations. Sensor response was recorded, via the characteristic peak current, for concentrations between 10 -12 and 10 -3 M (Figure  10 ). The sensor limit of detection (LOD) was determined from the lowest concentration that gave a visible signal  [81, 82] , instead of the method based on the signal to noise ratio of 3, as usually done. Here, the obtained LOD of 1 pM is much lower than the WHO maximum permissible limit in drinking water of (1 μg. L -1 ). The dissociation constant Kd is an important parameter that determines the degree of affinity between the polymer and the template. Different analytical models were tested to fit the experimental data of the electrochemical measurements.
Compared to partially premixed combustion (or combustion of non-homogeneous reactants in general), fully premixed and diffusion flames represent only two asymptotic limits of combustion modes. However, the deep knowledge accumulated over the years on these two elementary and archetypal flame prototypes is such that they remain the cornerstone and reference building blocks of most combustion modelling proposals. Therefore, from a general point of view, being able to distinguish between premixed and non-premixed modes of combustion thanks to a flame index appears as a quite appealing but challenging task that still concentrates many research efforts. Indeed, the availability of such an index is not only appealing to proceed with the analysis of either experimental or computational data issued from DNS (or highly resolved LES) databases.
It is also an essential ingredient to elaborate advanced flamelet-based multiregime combustion models on the basis of single regime tabulated flamelet databases. In the present study, a new definition of the premixedness index ζ PF is proposed for partially premixed combustion. It is based on a weighted form of the cross-scalar dissipation rate of the mixture fraction Y ξ and progress variable Y c , i. e. , quantities that have been previously identified as relevant parameters to describe partially premixed combustion regimes. The relevance of the corresponding index is assessed through a detailed computational procedure that includes three successive validation subsets: counterflow flames (including premixed, rich partially-premixed, and diffusion flames), (ii) stabilized triple flames for three distinct values of the inlet mixture fraction gradient, and finally (iii) unsteady flame kernel developments in nonhomogeneous mixtures of fresh reactants, which are characterized by various initial levels of the segregation rate between the fuel and oxidizer.
The proposed premixedness index ζ PF and its counterpart ζ DF = 1 -ζ PF are used as the weighting coefficients between tabulated premixed flamelets (TPF) and tabulated diffusion flamelets (TDF) data, which have been parameterized as functions of Y ξ and Y c. It is noteworthy that, in contrast to some previous proposals of the literature, the present flame index does not require the consideration of any other quantities in addition to those already used to parameterize the flamelets databases, i. e. , Y ξ and Y c. The validation procedure makes use of steady and unsteady processes with a priori and a posteriori analyses. In both cases, the comparisons between the results obtained with the proposed flame partitioning and detailed chemistry (DC) computations lead to a satisfactory level of agreement and, from a general viewpoint, the level of agreement is better than the one obtained with either premixed or diffusion flamelet-based models.
The use of such tabulations in turbulent conditions requires further work since the joint PDF of the corresponding quantities must be approximated, and this may require to consider not only a closed set of modelled transport equations for their filtered values, but also many additional cross-correlations between subgrid scalar fluctuations. However, the corresponding modelling issues are outside the scope of the present work, which is focused on the assessment of a flame index partitioning in partially premixed but laminar conditions. To conclude, it should be emphasized that the elementary building blocks mentioned above, i. e. , TPF, TDF, and TPPF, share some common limitations and there exist several directions for further improvements. For instance, since the tabulated data are based on onedimensional computations, the spatial variations of mixture fraction and progress variable are necessarily aligned in either one-dimensional TDF or one-dimensional TPPF building blocks, whereas they are not systematically oriented along the same direction in practical situations.
However, the use of two-dimensional laminar partially premixed flames as the elementary building block would raise so complicated issues that this strategy is not considered as relevant for future developments. In this respect, it is noteworthy that the definition of a blending function able to account for the possible influence of a misalignment between the gradients of Y ξ and Y c would be extremely beneficial. This special point will be discussed in more details in the next section of this work. In addition to this, it should be emphasized that, over the years, a large amount of knowledge has been accumulated to develop and construct TPF and TDF databases that reproduce the burning rates and chemical species formation with a remarkable level of accuracy  [8, 9, 14, 16, 17]. A large number of these databases have been gathered for various operating conditions and there is therefore some undeniable interest in finding an appropriated partitioning function to take advantage of their immediate availability.
A proposal for a new flame index Before embarking on the introduction of such a proposal, it seems worthwhile to remind that flamelet databases are generally parameterized as functions of Y ξ and Y c. Therefore, to keep pace with a reduced number of variables, an optimal choice would consist in expressing the flame index as a function of these two variables only. In this respect, it is also noteworthy that the orientation between the mixture fraction and progress variable gradients, as well as their associated amplitudes, have been early identified as relevant parameters to characterize combustion of non-homogeneous reactants  [27, 28]. From this point of view, the mixture fraction gradient is aligned with the progress variable gradient across a local one-dimensional diffusion flame. Therefore, to assess the possible relevance of tabulated diffusion flamelets (TDF), the flame index should evaluate the orientation between the direction of mixture fraction gradient n Y ξ = ∇Y ξ /|∇Y ξ | and the one of the progress variable gradient n Y c = ∇Y c /|∇Y c |.
Flamelet databases In the forthcoming sections of the manuscript we analyze the relevance of the model proposed above. This requires the use of flamelet manifolds, which are generated by using the Cantera software  [35]. Methane-air combustion is described with the San Diego mechanism  [36]. Non-unity Lewis number effects are not addressed just for the sake of simplicity and to make easier the comparisons between tabulated flame computations and detailed chemistry results. The flamelet manifolds are standardly parameterized by means of two reference scalar variables  [13]. On the one hand, the fresh mixture composition variations are described using a mixture fraction variable Y ξ  [11]  and the definition of Bilger  [37]  has been retained to proceed with the construction of the tabulated databases.
5 • (Y CO + Y CO 2 ). The proposed flame index partitioning framework is independent of the progress variable definition and other definitions can be chosen according to the input fuel or stiffness of chemical source terms. In this respect, it must be emphasized that the above definition of the progress variable has been retained just for the sake of simplicity and consistency with previous analyses performed on the same topic. Tabulated data are described with two hundred points along each direction Y ξ and Y c. The tabulated premixed flamelets (TPF) database corresponds to one-dimensional unstrained freely-propagating premixed flames, while the tabulated diffusion flamelets (TDF) database is constructed on the basis of one-dimensional strained counterflow diffusion flames. Figure  1  gathers temperature, progress variable source term, and CO mass fraction contours, which are issued from premixed and diffusion flamelet prototypes.
In the premixed flame plots, the black lines are associated to flammability limits, beyond which tabulated characteristics are interpolated. In the diffusion flame plots, the black line corresponds to the strained counterflow diffusion flame featuring the smallest strain-rate. For smaller strain-rate values, interpolation with the mixing line is performed. The premixed flame database is deduced from the detailed solution of freely propagating one-dimensional laminar premixed flames obtained for various values of the mixture fraction Y ξ within the flammability limits  [7]. Using the San Diego mechanism  [36]  and for standard pressure and temperature, the laminar flame speed S 0 L of a stoichiometric mixture is 0. 285 m/s, while the thermal flame thickness is δ 0 L = 0. 5 mm. Any flamelet property such as composition, temperature or production rate ω Y c , is represented as a function of the progress variable Y c , see the left side of Figure  1.