dataesr/scientific-paragraphs-categorization huggingface dataset:
Phase analysis was performed with Oxford Cryosystem Crystallographica Search -Match 2. 1 software with PDF2 2011 database. 3. Results And Discussion 3. 1 Na 3 AlF 6 -NdF 3 ). A solid -state PIXcel detector was used to record XRD patterns. The measurements were carried out at room temperature, with 2θ varying from 10° to 90°.
Gas analysis at the chamber outlet A gas sampling probe is installed at the exit of the combustion chamber and connected to commercial gas analyzers via a heated line to measure the combustion products. A first gas analyzer (SK-electroniks UPAS-FID) comprises a flame ionization detector to measure the total hydrocarbon content (THC), and electrochemical cells for CH 4. A second gas analyzer (Horiba VA-5000) measures CO 2 , CO, NO, and N 2 O based on non-dispersive infrared absorption spectroscopy, O 2 with a paramagnetic cell, and NO 2 with electrochemical cells. All data are monitored and recorded by a computer. To prevent interference from H 2 O, water vapor is removed with a cold trap bath from the gas stream before entering the second analyzer. This gas analyzer measures mole fractions in the dried gases.
Acknowledgments This work has received funding from the  Agence Nationale de la Recherche  (grant PASTEC  ANR16-CE22-0005 ) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 101021538 ). A CC-BY 4. 0 public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions: https://creativeco mmons. org/licenses/by/4. 0/. The authors would like to thank  Erika Jean-Bart ,  Yannick Le Teno ,  Hubert Jubeau , and  Koro Sokhona  for technical assistance with the experimental facility,  Guilhem Lavabre  for discussions on uncertainty quantification, and  Renaud Gablier  for discussions on NO X emissions.
3). Excited radical OH* was chosen for normalization because it is the only measured excited radical that is present in significant quantities in all flames of interest, namely ammonia-methane-air, methane-air, or ammonia-air flames. Therefore, the GPR algorithm features up to five inputs (any combination of NO*/OH*, NH*/OH*, CN*/OH*, CO 2 */OH*, and CH*/OH*) and two outputs (ϕ and X NH3 ). The dataset from  [32]  includes 214 data points, corresponding to wide ranges of equivalence ratio (0. 60 ≤ ϕ ≤ 1. 30), ammonia fraction (0 ≤ X NH3 ≤ 0. 80), and strain rate (80 ≤ a ≤ 300 /s). GPR is well suited to datasets of this dimensionality and size. In addition, GPR is probabilistic by design, meaning that it supposes that the true value is normally distributed about the mean predicted value. Therefore, it provides an estimate of the corresponding standard deviation, which can be used to infer the confidence interval for its predictions.
Note that other types of machine learning regression methods exist, but those tested by the authors (e. g. , ordinary least squares and lasso linear regression or support vector machines regression) did not perform as well as GPR for this application. The GPR algorithm was implemented in scikit-learn  [50] , an open-source library of machine learning tools based on the Python programming language. Application of GPR requires specifying a covariance kernel function, and the scikit-learn library includes numerous options. After multiple tests, the rational quadratic kernel was found to be a suitable option for the present dataset. It is simply parameterized by a length-scale hyperparameter l and a scale mixture hyperparameter α that can be varied and optimized using functions built in scikit-learn to achieve the right balance between underfitting and overfitting.
The rational quadratic kernel k rq is given by: k rq ( x i , x j ) = ( 1 + d ( x i , x j ) 2 2αl 2 ) -α (1) where x i and x j are datapoints and d(x i , x j ) is the Euclidean distance between these datapoints. In GPR, multiple kernels can be combined by addition or by multiplication. Here, to improve numerical stability, a white kernel k w was simply added to the rational quadratic kernel to yield k = k rq + k w. The white kernel is only parametrized by a noise level n as follows: k w ( x i , x j ) = n if x i = x j else 0 (2) Similar performance was found using a Matern kernel instead of a rational quadratic kernel, and both the rational quadratic and Matern kernels slightly overperformed a radial basis kernel. The reader is referred to  [51]  for more details about GPR. To train the GPR algorithm, 75% of the dataset was used, corresponding to 160 data points.
The 160 data points were randomly assigned by the train_test_split function built in scikit-learn after a seed number was prescribed. Different seed numbers were used to assess the universality of the optimized hyperparameters (see Section 3. 1). The GPR algorithm was extended to also include the possibility to introduce random error into the dataset, which was used to quantify the robustness of the method to the uncertainty of chemiluminescence intensity measurements (see  Section 3. 4). The quality of the regression for any given sets of hyperparameters and inputs was quantified by the coefficient of determination R 2 of the regression, applied to the validation dataset, i. e. , the remaining 54 (25%) data points from Zhu et al. [32]. Once the hyperparameters and inputs were optimized, the performance of the prediction of equivalence ratio and ammonia fraction by the GPR algorithm was quantified using a test dataset that was not used for training or validation.
Experimental setup To collect the test dataset, the chemiluminescence spectra of premixed, turbulent swirl, ammonia-methane-air flames were recorded at atmospheric pressure. These flames were stabilized in a generic, labscale burner (cf. Fig. 2 ) similar to that used and described in detail in  [52] [53] [54]. A perfectly premixed mixture of ammonia, methane, and air was supplied to the plenum of the burner and was then fed in a radial swirler through a converging nozzle. The swirl number was S = 0. 4. The reactants then flowed into a cylindrical combustion chamber made of quartz (70 mm in diameter and 200 mm in length) through an 18-mm diameter nozzle. Different equivalence ratios (0. 80 ≤ ϕ ≤ 1. 20) and ammonia fractions (0 ≤ X NH3 ≤ 0. 60) were tested, and the bulk velocity through the nozzle was set to 5 m/s.
Figs. 4  and 5  are very similar, and only the coefficient of determination of the regression on equivalence ratio is marginally degraded if NO* is excluded. The optimal hyperparameters are different if NO* is excluded: l = 0. 40, α = 0. 29, and n = 2. 9 × 10 -4 for the equivalence ratio and l = 0. 24, α = 0. 74, and n = 1. 0 × 10 -4 for the ammonia fraction. Table  2  shows the corresponding R 2 statistics for the 100 different seed numbers of the training dataset. Based on Figs. 4  and 5  and Tables  1  and 2 , it is concluded that removing NO* from the inputs does not lead to significant degradation of the GPR algorithm's performance, yet it reduces the complexity and cost of the associated measurements. Fig. 6  shows the prediction by the GPR algorithm if the NO* and CO 2 * excited radicals are both excluded, leading to 3 inputs.
Indeed, Fig. 1  shows that trends of CO 2 * with equivalence ratio and ammonia fraction are similar to those of OH*, suggesting that CO 2 * information could be redundant. Again, Fig. 6  is hardly distinguishable from Figs. 4  and 5 , meaning that removing both NO* and CO 2 * from the inputs does not significantly degrade the GPR algorithm's prediction performance. The optimal hyperparameters if NO* and CO 2 * are excluded are l = 0. 39, α = 0. 30, and n = 3. 0 × 10 -4 for the equivalence ratio and l = 0. 27, α = 0. 55, and n = 1. 3 × 10 -4 for the ammonia fraction. Table  3  shows the corresponding R 2 statistics for the 100 different training dataset seed   numbers. A similar excercise was performed by further excluding the remaining excited radicals, one by one. It was found that excluding any of the OH*, NH*, CN*, or CH* radicals leads to significant degradation of the GPR algorithm's prediction performance.
39, α = 0. 30, and n = 3. 0 × 10 -4 for the equivalence ratio and l = 0. 27, α = 0. 55, and n = 1. 3 × 10 -4 for the ammonia fraction. To further assess the robustness of the GPR algorithm, the optimal hyperparameters were identified for 100 different training dataset seed numbers. Statistics are shown in Table  4. It is challenging to qualify if the COVs reported in Table  4  are small or large. However, given the sensitivity of the GPR algorithm to its hyperparameters for the data at hand, these variations were found to be small enough that it can be concluded that the regression performance is not very sensitive to the choice of the training dataset. To illustrate, training with the reference dataset (the one used for Fig. 6  and Table  3 ) using the hyperparameters that are the furthest away from those optimized for this dataset leads to coefficients of regression that remain large, namely R 2 = 0.
Test of the GPR algorithm with turbulent swirl flames So far, the GPR algorithm was only trained and validated with laminar flame data and it is important to test if it generalizes to new data, not included in the database from Zhu et al. [32]. This is also an opportunity to challenge predictions of equivalence ratio and ammonia fraction for more practical flames; in this case the premixed, turbulent swirl, ammonia-methane-air flames described in Section 2. Measurements in the laminar flames  [32]  and in the turbulent swirl flames were made with different spectrometers, featuring different spectral resolutions and sensitivities. Therefore, a 1-point calibration procedure was required to ensure that the turbulent swirl flame data were compatible with the GPR algorithm trained with laminar flame data.
Given the different spans of equivalence ratio and ammonia fraction considered in the laminar flames and in the turbulent swirl flames datasets, it is risky to compare the coefficients of regression directly. However, Fig. 8  also includes ±0. 05 (dashed) and ±0. 10 (dotted) lines that help quantify prediction errors. Both for the equivalence ratio and for the ammonia fraction, prediction errors are well below ±0. 05 for the vast majority of data points, and errors are always below ±0. 10. In addition, the true equivalence ratios and ammonia fractions are always located within the confidence intervals of the GPR algorithm's predictions (green vertical bars in Fig. 8 ) that could be estimated thanks to its probabilistic nature (cf. supplementary material for more details). Note that this arguably good performance was achieved for wide ranges of equivalence ratio (0.
Robustness of the GPR algorithm to measurement errors The start-of-the-art spectroscopic hardware and the well-behaved nature of the steady laminar flames considered by Zhu et al. [32]  led to measurement uncertainties that are likely to be well below what would be achieved in the field for more practical flames. This is why a less costly and more robust bench spectrometer and turbulent swirl flames were also considered in the present study. To further quantify the robustness of the GPR algorithm to measurement errors, the training and validation procedures were repeated after a random input error was introduced into the laminar flame data. The corresponding coefficients of determination for the predictions of equivalence ratio and ammonia fraction with the turbulent swirl flames' test dataset are shown in Fig.
9  (green diamonds) as a function of the input error. An input error of X% means that the NH*/OH*, CN*/OH*, and CH*/OH* ratios of the training and validation datasets were each multiplied by a different random number between 1 and 1 ± X/100. Due to the random nature of this error, each case was repeated many times, until convergence of the mean and rms coefficient of determination was achieved, and the variations are reflected with error bars. Hyperparameters were re-optimized for each test. Fig. 9  shows that equivalence ratio predictions are fairly robust to measurement errors because the mean coefficient of determination remains above 0. 90 even for input errors of ±25%, which is arguably larger than typical chemiluminescence measurement errors. Interestingly, the mean coefficient of determination is slightly higher if an input error between ±1.
25% and ±15% is added compared to no input error at all. This behavior was not thoroughly investigated but is consistent with previous findings showing that noise injection can lead machine learning algorithms to be more robust to generalization  [55]. In an extension of this work, retraining on a more diverse dataset could therefore potentially modify this behavior. Fig. 9  shows that ammonia fraction predictions are also robust to measurement errors because the mean coefficient of determination only drops below 0. 90 for ±25% of input errors or more. The mean coefficient of determination is slightly larger if an input error between ±1. 25% and ±5% is added compared to no input error at all. To quantify the robustness of GPR to measurement errors that could also be present in the test dataset, the same exercise was repeated but with random error added to the turbulent swirl flame data in addition to the laminar flame data.
The results are shown in Fig. 9  as purple stars. For both the equivalence ratio and ammonia fraction predictions, the mean coefficient of determination decreases when the input error is increased above some threshold. Also consistent with expectations, for any given input error, the mean coefficient of determination is smaller when an error is added to the test dataset than when no error is added to this dataset. For equivalence ratio and ammonia fraction predictions, the mean coefficient of determination drops below 0. 90 for more than ±15% of input error. Given the magnitude of measurement errors typically found in chemiluminescence measurements, the GPR can arguably be labeled as robust to errors in the turbulent swirl flame measurements.
It is also relevant to compare the performance of GPR to that of linear regression using the same chemiluminescent inputs, namely OH*, NH*, CN*, and CH*. A least square linear approach applied to the laminar flame dataset yields the following equations to predict the equivalence ratio and the ammonia fraction:  ϕ = 0. Fig. 12  shows the equivalence ratios and ammonia fractions predicted using Eqs. (  3 ) and (  4 ). The scatter and regression coefficients are much worse than those obtained with the GPR algorithm in Fig. 8 , for both the laminar flames and the turbulent swirl flames. For the latter, discrepancies are up to 0. 15 for the equivalence ratio and the ammonia fraction. Examination of Fig. 1  indicates that the problem at hand is intrinsically non-linear, implying that the poor prediction performance of a linear regression should not come as a surprise.
Therefore, it is useful to assess the performance of higher order polynomial fits. With scikitlearn's toolbox, a n th order polynomial fit is most conveniently achieved by taking the linear regression of inputs that were previously extended to include their exponents (from 1 to n) as well as their cross products featuring an order smaller than or equal to n. For example, with NH*/ OH*, CN*/OH*, and CH*/OH* as the three "original" inputs, a 2nd order polynomial fit would require 10 coefficients to fit the following inputs:  [  For a fair comparison with GPR that features optimum hyperparameters that are a priori unknown, the optimal order of the polynomial should be treated as a hyperparameter too, and it should be identified through the same train/validation exercise and datasets. For the herein data, a 4th order polynomial (35 coefficients) yielded the best regression coefficients on the validation datapoints, specifically R 2 = 0.
983 and 0. 986 for the equivalence ratio and ammonia fraction, respectively. Fig. 13  shows the equivalence ratios and ammonia fractions predicted with 4th order polynomial fits for the laminar flames   (validation datapoints in red) and turbulent swirl flames (test datapoints in green). Fig. 13  should be compared to Fig. 8  which shows the performance of GPR trained with the exact same dataset. Overall, while the prediction performance of the 4th order polynomial fits is good (prediction errors are within 5% for most datapoints), regression coefficients are smaller for the 4th order polynomial fits (R 2 = 0. 873 and 0. 946) than for the GPR (R 2 = 0. 938 and 0. 968). More important, inherent in the method, predictions made using the 4th order polynomial fits cannot be provided with a confidence interval while such a confidence interval is an output of GPR.
In particular, it is necessary to solve the issue of HDESs safety. Furthermore, the HDESs stability has to be carefully evaluated using the adequate and precise methodology. Our analysis indicated that it is necessary to have an improved, standard protocol for determination of solubility of HDESs in water. In this way, it will be possible to create a database of water-stable HDESs that can be used in water treatment processes. When all of the mentioned issues will be covered, it will be necessary to expand the research from laboratory to industrial scale and solve all the technological issues associated with the usage of HDESs in a large scale. With that being said, a lot of time will most likely pass before HDESs will be considered as real alternative for the conventional solvents. In our opinion, HDESs will probably be more suitable for processing rather small volumes of wastewater with high pollution load or containing specific hazardous pollutants and followed by a biological treatment process for removal of traces of HDESs.
Acknowledgements The authors gratefully acknowledge financial support from  BPI France  and  Région Auvergne-Rhone-Alpes  (project  PSPC Régions REPOS ). The authors would like to thank  Dr. Winnie Raynaud  ( INSA-Lyon ) for her help for the characterization of repolymerized silicone oil (SEC analysis and volatile content). The authors are also grateful to  Dr. Louis Vovelle  and  Joséphine Munsch  for stimulating discussions on circular economy and particularly on circularity of silicones.
Excess enthalpies A high sensitivity heat conduction isothermal titration microcalorimeter from Thermometric AB/TA (now Waters TM ), together with a prototype water bath from Lund University, Sweden, was used to measure the partial excess molar enthalpies of mixtures of TBF in TBH, at atmospheric pressure and at 298. 15 AE 0. 01 K. 46  A 71/2 digit Agilent nanovoltmeter (model 34420A) is connected to the calorimetric channel and interfaced with the computer, where data acquisition and the syringe pump control are performed through a modified version of the program LABTERMO. 46, 47 The system was electrically calibrated by release of controlled amounts of heat through a 'homemade' insertion heater with accurately known resistance, as well as chemically by use of the test reaction of dilution of a 10% propan-1-ol solution in water.
Acknowledgements This work was partly developed within the scope of the project  CICECO-Aveiro Institute of Materials  ( UIDB/50011/2020 ,  UIDP/ 50011/2020  and  LA/P/0006/2020 ),  CIQUP ,  Centro de Investigaça ˜o em Quı ´mica da Universidade do Porto  ( UIDB/00081/ 2020 );  IMS, Institute of Molecular Sciences  ( LA/P/0056/2020 ), and  Centro de Quı ´mica Estrutural, Institute of Molecular Sciences , ( UIDB/00100/2020/LA/P/0056/2020 ), financed by national funds through the  FCT/MEC  (PIDDAC). AIMCLF is financed by national funds through the  FCT-I. P. , in the framework of the execution of the program contract provided in paragraphs 4, 5, and 6 of art. 23 of Law no. 57/2016 of 29 August, as amended by Law no. 57/2017 of 19 July.
Acknowledgement. We thank  Albert Silvi  ( FMI ) and  Aku Helin  ( FMI ) for conducting comparison tests with different tube materials,  Thierry Léonardis  ( IMT ) for conducting the intercomparison and for his technical support and  Laurence Depelchin  ( IMT ) for performing the particulate filter experiments for terpenes. Anja Claude  and  Stefan Reiman  are thanked for providing sodium thiosulfate impregnated filters and MnO2 nets as O3-scrubbers. Financial support. This research within the project  19ENV06  MetClimVOC has received funding from the  European Metrology Programme for Innovation and Research (EMPIR)  cofinanced by the  Participating States  and from the  European Union 's  Horizon 2020 research and innovation programme.
However, because the BE of these proposed inorganic Br species are similar,  [30- 31]  it is difficult to distinguish them via XPS analysis. While the complexity associated with the different possible inorganic species makes fully understanding the chemistry and quantifying the speciation after PSM-1 challenging. The XPS data could be fit to quantify the amount of organic and inorganic Br; the results indicate that there is a contribution from ~ 54 % organic species (C-Br contributions) and a 46 % inorganic Br species (Cr-Br, NH3 + Br -) (See Table  S1 ). Since XPS is a surface sensitive technique, and the MOF crystal size is ~ 34 nm, XPS results were expected to resemble bulk characterization techniques. Despite this, the ligand modification in PSM-1 was also quantified from X-ray Absorption Spectroscopy (XAS), which provides more information on the bulk sample (see Supplementary information).
The quantity of each Br species could be determined after a linear fitting of the three reference samples. The data indicates that there is 46 % organic Br (C-Br bonds), 31. 6 % Cr-Br, and 22. 4 % NH3 + Br -; these values are close to those obtained from XPS analysis (Figure  S3 ). It is noted that, for every ligand modification with BrAcBr in NH2-Cr-BDC, one H + and Br -is released, which then has an opportunity to protonate unreacted NH2-BDC ligands and/or the anion could bind to the Cr 3+ cluster via exchange with OH -/NO3 -. Knowing this, EXAFS (Extended X-ray Absorption Fine Structure) data was then collected on BrAc-NH-Cr-BDC to extract structural information on the atoms in close proximity to Br (Figure  1 d ). To further explore the complexes formed during the functionalization of the MOF with BrAcBr, Kohn-Sham density functional methods (DFT) were employed.
Acknowledgement This work was supported by the  Swiss National Science Foundation  under grant numbers  200021_188536  and  PYAPP2_160581. We thank  GAZNAT  for the support of J. E. We thank  Dr. Natalia Gasilova  for her help with Microwave digestion of samples and  Dr. Olga Trukhina  for the help with IC measurements. https://doi. org/10. 26434/chemrxiv-2023-rb7x0 ORCID: https://orcid. org/0000-0003-4496-1594 Content not peer-reviewed by ChemRxiv. License:  CC BY-NC 4. 0.
Data from all spatial locations of nine turbulent flames in the Cambridge/Sandia swirl database are combined to study how the choice of scalar variables in conditional moment closure (CMC) type approaches affect the conditional spatial fluctuations of reactive scalars. In order to investigate the influence of swirl and stratification, two additional data-sets have been constructed. Principal component analysis (PCA) is applied to help identify the number of scalar variables and the most appropriate choices to describe the composition space. Two PCA scaling methods have been adopted, namely Pareto and Auto-scaling. Regardless of the data-set investigated and the scaling method used, the results suggest that a single principal component correlated with temperature accounted for the largest variance.