Vinca Prana

Guillaume Fayet

Patricia Rotureau
email: patricia.rotureau@ineris.fr

Carlo Adamo

Development of validated QSPR models for impact sensitivity of nitroaliphatic compounds

Keywords: impact sensitivity, QSPR, nitroaliphatic compounds, quantum chemistry, OECD principles, REACH regulation

The European regulation of chemicals named REACH implies the assessment of a large number of substances based on their hazardous properties. However, the complete characterization of physico-chemical, toxicological and eco-toxicological properties by experimental means is incompatible with the imposed calendar of REACH. Hence, there is a real need in evaluating the capabilities of alternative methods such as Quantitative Structure-Property Relationship (QSPR) models, notably for physico-chemical properties. In the present work, the molecular structures of 50 nitroaliphatic compounds were correlated with their impact sensitivities (h 50% ) using such predictive models. More than 400 molecular descriptors (constitutional, topological, geometrical, quantum chemical) were calculated and linear and multi-linear regressions were performed to find accurate quantitative relationships with experimental impact sensitivities. Considering different sets of descriptors, four predictive models were obtained and two of them were selected for their predictive reliability. To our knowledge, these QSPR models for the impact sensitivity of nitroaliphatic compounds are the first ones being rigorously validated (both internally and externally) with defined applicability domains. They hence follow all OECD principles for regulatory acceptability of QSPRs, allowing possible application in REACH.

Introduction

The European Union regulation REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) [START_REF] Ec | Regulation N°1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the registration, evaluation, authorization and restriction of chemicals (REACH)[END_REF], entered into its registration phase in December 2008. It requires the evaluation of physico-chemical, toxicological and eco-toxicological properties for a tremendous number of chemicals. Indeed, more than 143 000 existing substances have been pre-registered by 65 000 companies and are expected to be registered before 2018. Moreover, all new substances require an early examination to identify possible hazards. Taking into account all the constraints imposed by the regulation, the complete experimental characterization of all substances is not realistic (for reasons of time, costs or ethics in case of tests on animals). Thus, the development of alternative (predictive) methods to evaluate the properties of chemicals was recommended in the framework of REACH. In this context, we developed and validated theoretical models, based on robust computational approaches, which represent a viable alternative to more conventional experiments. In particular, we focused on Quantitative Structure-Property Relationship (QSPR) models, which have been clearly identified in REACH legislation. Besides, models have already been developed for both environmental and physico-chemical properties in the scope of this regulation [START_REF] Papa | QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure[END_REF][START_REF] Papa | On the Use of Local and Global QSPRs for the Prediction of Physico-chemical Properties of Polybrominated Diphenyl Ethers[END_REF][START_REF] Bhhatarai | CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals[END_REF]. To support this development, OECD drawn up the 5 following principles for the validation of QSPR models [START_REF]Guidance document on the validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] models[END_REF]:

1. A defined endpoint (including experimental protocol);

2. An unambiguous algorithm; 3. A defined domain of applicability; 4. Appropriate measures of goodness-of-fit, robustness and predictive power; 5. A mechanistic interpretation, when it's possible. Following these criteria, four QSPR models were developed in the present study for the impact sensitivity of nitroaliphatic compounds. This property, characterizing the tendency of a material to react under the effect of a mechanical impact, is one of the most important to classify explosible substances according to the European regulation related to the Classification, Labelling and Packaging of substances and mixtures (CLP) [START_REF] Ec | and of the Council of 16 December 2008 on classification, labeling and packaging of substances and mixtures, amending and repealing Directives[END_REF]. Many QSPR models have been already developed for this property and, some of them, for nitroaliphatic compounds. The first one was published by Kamlet [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF] in 1976 for trinitroaliphatic compounds and consisted in a simple linear equation based on the oxygen balance that characterizes the oxydability of explosives. Later, Mullay developed another linear model using the molecular electronegativity [START_REF] Mullay | Relationships between impact sensitivity and molecular electronegativity[END_REF][START_REF] Mullay | Relationships between impact sensitivity and molecular electronic structure[END_REF]. More recently, quantum chemical descriptors have been introduced. For example, Politzer [START_REF] Politzer | A relationship between impact sensitivity and the electrostatic potentials at the midpoints of C-NO 2 bonds in nitroaromatics[END_REF][START_REF] Politzer | Relationships between dissociation energies and electrostatic potentials of C-NO 2 bonds: Applications to impact sensitivities[END_REF], Rice [START_REF] Rice | A quantum mechanical investigation of the relation between impact sensitivity and the charge distribution in energetic molecules[END_REF] and Badders [START_REF] Badders | Predicting the impact sensitivities of polynitro compounds using quantum chemical descriptors[END_REF] used the midpoint potential that characterizes the electrostatic potential created by the charges of the C and N atoms at the mid-point of C-NO 2 bonds and Zang [START_REF] Zhang | Review of the establishment of nitro group charge method and its applications[END_REF] exhibited correlations with the nitro group charge. In parallel, Keshavarz [START_REF] Keshavarz | Simple empirical method for prediction of impact sensitivity of selected class of explosives[END_REF][START_REF] Keshavarz | Investigation of the various structure parameters for predicting impact sensitivity of energetic molecules via artificial neural network[END_REF][START_REF] Keshavarz | Novel correlation for predicting impact sensitivity of nitroheterocyclic energetic molecules[END_REF][START_REF] Keshavarz | Prediction of impact sensitivity of nitroaliphatic, nitroaliphatic containing other functional groups and nitrate explosives[END_REF] developed multivariate models using constitutional parameters, such as the ratio of the number of particular molecular fragments on the molecular weight. Lai [START_REF] Lai | New correlations for predicting impact sensitivities of nitro energetic compounds[END_REF] proposed a new model, based on the ones of Keshavarz and Kamlet that included an additional term based on the presence and the number of specific fragments. All these models were characterized by good determination coefficients (R²) but they were not validated (either with respect to an internal or external set of compounds) and their respective applicability domains were not defined. Few models using neural networks [START_REF] Cho | Optimization of neural networks architecture for impact sensitivity of energetic molecules[END_REF][START_REF] Nefati | Prediction of the impact sensitivity by neural networks[END_REF] with different sets of descriptors were also developed for nitro compounds. To our knowledge, the more predictive existing models for the impact sensitivities of nitroaliphatic compounds have been developed by Wang [START_REF] Wang | Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices[END_REF] using a multi-linear regression (R²=0.80, Q² LOO =0.51 and R² EXT =0.93) and partial least square (PLS -R²=0.78, Q² LOO =0.48 and R² EXT =0.97) based on a series of electrotopological descriptors. As indicated, the determination coefficients for the molecules of the validation set were high but a failure in robustness can be noticed probably due to the large number of descriptors [START_REF] Badders | Predicting the impact sensitivities of polynitro compounds using quantum chemical descriptors[END_REF] in models. Moreover, no applicability domains were defined and analyzed. Consequently, none of these models fits all OECD standards and cannot be used, therefore, within REACH regulation. In order to fill this gap, this paper aimed to develop fully validated QSPR models to predict the impact sensitivity of 50 nitroaliphatic compounds respecting all OECD principles including the determination of their applicability domains. About 400 descriptors were calculated, including quantum chemical ones based on density functional theory (DFT) calculations. These latter were performed to have a better chemical interpretation of the developed models as already demonstrated in previous works for nitroaromatic compounds [START_REF] Fayet | On the use of descriptors arising from the conceptual density functional theory for the prediction of chemicals explosibility[END_REF][START_REF] Fayet | Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms[END_REF][START_REF] Fayet | Predicting the thermal stability of nitroaromatic compounds using chemoinformatic tools[END_REF].

At first, linear regressions were performed and a first validated model was proposed. Then several multi-linear regressions (MLR) were developed to reach better performances using different sets of descriptors. The first one considered only constitutional descriptors that only need the 2D structures of molecules. The second one was extended to all types of descriptors and the last one focused only on quantum chemical descriptors that could provide better chemical insights. Also all models were validated by a series of internal and external validation methods. To our knowledge, they represent the first models dedicated to energetic properties proposing a complete validation process including cross validations to characterize robustness, Y-randomization techniques to avoid chance correlations and external validation to estimate predictive power of models and taking into account the applicability domain.

Computational details 2.1. Experimental dataset

The considered dataset contained 50 experimental impact sensitivity values of nitroaliphatic compounds (see Table S1 in supporting information for structures) that have been extracted from a single reference [START_REF] Storm | Sensitivity relationships in energetic materials[END_REF] to ensure that they were obtained using a single protocol (by drop weight impact tests in the work of Storm [START_REF] Storm | Sensitivity relationships in energetic materials[END_REF]), as required by the first OECD point. The impact sensitivity, noted h 50% , is a quantity which measures (in centimeters) the height from which the fall of a weight of a given mass on a sample causes a reaction with a 50% probability level. Substances with low h 50% values are the most sensitive since they need less energy to react. This impact test requires relatively small quantities of material (ac700 mg) but does not exhibit good resolution or reproducibility. It is then used to separate qualitatively explosives into classes of similar handling hazards [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF]. Also, the test of sensitivity to impact is a regulatory test within the frameworks of CLP [START_REF] Ec | and of the Council of 16 December 2008 on classification, labeling and packaging of substances and mixtures, amending and repealing Directives[END_REF] or Transport of Dangerous Goods (TDG) [START_REF]Recommendations on the transport of dangerous goods: Manual of tests and criteria[END_REF]. To allow an external validation of models, the data set was then divided into a training set, containing two thirds of the molecules of the data set and a validation set constituted by the remaining molecules. This splitting was carried out to guarantee similar distributions of the h 50% values in both sets [START_REF] Katritzky | QSPR modeling of flash points: An update[END_REF]. In practice, compounds were classified by increasing order of h 50% and one molecule out of three molecules was selected to constitute the validation set. This partition enables both sets to be of sufficient size with similar distributions to allow a robust development and validation of models. Moreover, no bias in representativeness was identified when inspecting the chemical structures of both sets. The so defined training set of 34 compounds was used for the development and the internal validation of models, while the validation set of 16 compounds was considered for external validation to evaluate their predictive power. The repartition of data between training and validation sets as well as the corresponding experimental values are reported in Table 1.

Molecular structures calculation

A preliminary conformation analysis was carried out with Scigress software [START_REF]Scigress, version 2.2.0, Fujitsu limited[END_REF] using the Conflex algorithm [START_REF] Goto | An efficient algorithm for searching low energy conformers of cyclic and acyclic molecules[END_REF][START_REF] Goto | Corner flapping -a simple and fast algorithm for exhaustive generation of ring conformations[END_REF] and the MM3 force field [START_REF] Allinger | Molecular mechanics. The MM3 forcefield for hydrocarbons. 1[END_REF]. This analysis generated automatically all possible conformations of molecules and their energy. The most stable conformer (the one with the smaller energy) of each molecule was used as molecular structure. Then it was optimizated with Gaussian03 software [START_REF] Frisch | Gaussian Inc[END_REF] using DFT calculations with the PBE0 [START_REF] Adamo | Toward reliable density functional methods without adjustable parameters: The PBE0 model[END_REF] functional and the 6-31+G(d,p) basis set. Vibrational frequencies were computed at same level of calculation to ensure that all final stable conformations exhibited no imaginary frequency.

Molecular descriptors calculation

Each molecular structure was then characterized by a series of descriptors. According to Todeschini and Consonni [START_REF] Todeschini | Handbook of molecular descriptors[END_REF], the "molecular descriptor is the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment". Different kinds of calculated descriptors can be then considered [START_REF] Karelson | Molecular Descriptors in QSAR/QSPR[END_REF]:

• Constitutional descriptors, such as the number of specific atoms, functional groups or bonds; • Topological descriptors, which are calculated from atomic connectivity in the molecule and give information about size, composition and branching degree (e.g. Weiner, Balaban or Randic indices); • Geometric descriptors, related to the 3D-structure, such as distances, angles, molecular volume or surface area; • Quantum chemical descriptors, like atomic charges, electronic and binding information, molecular orbital energies or reactivity indices. Most of the descriptors considered in the present paper were calculated using the CodessaPro software [START_REF] Codessapro | [END_REF]. Others were extracted from examination of chemical structures or from the works of Kamlet [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF], Mullay [START_REF] Mullay | Relationships between impact sensitivity and molecular electronic structure[END_REF], Badders [START_REF] Badders | Predicting the impact sensitivities of polynitro compounds using quantum chemical descriptors[END_REF] and Keshavarz [START_REF] Keshavarz | Novel correlation for predicting impact sensitivity of nitroheterocyclic energetic molecules[END_REF]: oxygen balance (OB 100 ), molecular electronegativity (χ mol ), the ratio of the number of atoms (C, H, O or N) on molecular weight (n C /mw, n H /mw, n O /mw, n N /mw) and the number of specific fragments (as n CNC ). Moreover, descriptors issuing from conceptual DFT [START_REF] Chermette | Chemical reactivity indexes in density functional theory[END_REF][START_REF] Geerlings | Conceptual density functional theory[END_REF], already successfully used in QSPR models for the prediction of the heat of decomposition of nitroaromatic compounds [START_REF] Nefati | Prediction of the impact sensitivity by neural networks[END_REF][START_REF] Fayet | On the use of descriptors arising from the conceptual density functional theory for the prediction of chemicals explosibility[END_REF][START_REF] Fayet | Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms[END_REF][START_REF] Fayet | Predicting the thermal stability of nitroaromatic compounds using chemoinformatic tools[END_REF], were considered. More details about descriptors are available in [START_REF] Codessapro | [END_REF] or in supporting information (Tables S2 andS3).

Model building

All models were developed on the training set using the Best Multi Linear Regression (BMLR) approach [START_REF] Karelson | Molecular Descriptors in QSAR/QSPR[END_REF] as implemented in CodessaPro program [START_REF] Codessapro | [END_REF]. This approach gave the most correlated model at each rank (i.e. for each number of descriptors) and the final model was chosen as the best compromise between correlation and number of descriptors as explained in previous works [START_REF] Fayet | Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms[END_REF][START_REF] Katritzky | QSPR modeling of flash points: An update[END_REF].

Performances of models

To evaluate the performances of models, a series of internal and external validations were computed. The goodness of fit was measured by the root mean square error (RMSE) between predicted and experimental values:

( ) 1 ² 1 - - - = ∑ = p n y y RMSE n i i i (1)
With i y ˆ the calculated value of the property, i y the experimental one, y the mean of experimental values, n the number of molecules and p the number of descriptors.

Correlation was also characterized through the determination coefficient (R²):

( ) ( ) ∑ ∑ = = - - - = n i i n i i i y y y y R 1 1 ² ² 1 ² (2)
Moreover, a Student's t-test validation at a confidence level of 95% was computed to confirm the pertinence of each descriptor. The descriptors with the highest t-test values are the most relevant in the model.

The Q² coefficient issued from both leave-one-out (LOO) and leave-many-out (LMO) cross validations measured the robustness of the model, i.e. the dependence of the fitting on particular molecules or groups of molecules of the training set.

(

( ) ∑ ∑ = = - - - = n i i n i i i i y y y y Q 1 1 / ² ² 1 ² )
With i i y / ˆ the predicted value of the i th molecule from the model refitted without using this i th molecule. Robust models are expected to present a low difference between Q² and R² coefficients. It has to be noticed that cross validation does not measure the predictivity of models because the set of molecules excluded at each step of the cross validation procedure and then used to calculate Q 2 has already been used for the building of the model [START_REF] Gramatica | Principles of QSAR models validation: internal and external[END_REF][START_REF] Tropsha | Best Practices for QSAR Model Development, Validation and Exploitation[END_REF].

To ensure that models did not correspond to chance correlations, a Y-scrambling test was realized. Random permutations of experimental property values were performed (500 iterations) and new models were recalculated [START_REF] Lindgren | Model validation by permutation tests: applications to variable selection[END_REF][START_REF] Rücker | y-Randomization and Its Variants in QSPR/QSAR[END_REF]. To evaluate the impact of randomization, average and standard deviation in R² random coefficients were calculated (R 2 YS and SD YS ). Low R 2 YS values are expected to avoid chance correlation. To go further, Rücker proposed that the difference between R² of the original model and R 2 YS should be roughly ≥ 2.3 SD YS to ensure a statistical significance at a 1% level and ≥ 3 SD YS at a 0.1% level [START_REF] Rücker | y-Randomization and Its Variants in QSPR/QSAR[END_REF].

The predictive power of models was measured on the validation set based on the R² EXT coefficient, which measures the correlation between predicted and experimental values for the molecules of the validation set and the root mean standard error of predictions (RMSE EXT ). Moreover, the Q² EXT coefficient (Eq. 4) was calculated as proposed by Tropsha [START_REF] Tropsha | The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models[END_REF] and the OECD guidance document [START_REF]Guidance document on the validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] models[END_REF].

( )

( ) ∑ ∑ = = - - - = ext ext n i TR i n i i i EXT y y y y Q 1 1 ² ² 1 ² (4)
With i y ˆ the predicted value of the property, i y the experimental value of the property, TR y the mean experimental value in the training set and n ext the number of molecules in the validation set.

A QSPR model only offers reliable predictions for compounds similar to those belonging to the training data set. In this study, the applicability domain (AD) [START_REF] Netzeva | Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52[END_REF]46], required by the 3 rd OECD point, was determined based on the descriptors included in the model. Euclidean distance method available in Ambit discovery software [47] was used with a 95% threshold, i.e. the domain was calculated to contain 95% of the molecules of the training set. Then, the predictivity inside the applicability domain was also calculated based only on the molecules of the validation set that belonged to this domain (R² IN , RMSE IN and Q 2 IN ).

Results

Linear regressions

Firstly, linear regressions were performed on the entire data set to evaluate the global performances of descriptors not only with h 50% but also with logh 50% as commonly proposed in previous existing models [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF][START_REF] Mullay | Relationships between impact sensitivity and molecular electronic structure[END_REF][START_REF] Badders | Predicting the impact sensitivities of polynitro compounds using quantum chemical descriptors[END_REF][START_REF] Keshavarz | Simple empirical method for prediction of impact sensitivity of selected class of explosives[END_REF][START_REF] Keshavarz | Investigation of the various structure parameters for predicting impact sensitivity of energetic molecules via artificial neural network[END_REF][START_REF] Keshavarz | Novel correlation for predicting impact sensitivity of nitroheterocyclic energetic molecules[END_REF]. The best correlations were obtained for logh 50% . Indeed, the highest correlation considering h 50% reached R 2 =0.57 (obtained with the relative number of aromatic bonds) whereas the ten most correlated descriptors, as shown in Table 2, ranged from R 2 =0.73 to 0.78 with logh 50% .

To illustrate this point, oxygen balance (OB 100 ), presenting one of the most correlated descriptors with both h 50% and logh 50% was represented as function of the experimental impact sensitivity in Figure 1. This descriptor was defined by Kamlet [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF] as OB 100 = 100 (2n O -n H -2n C -2n COO )/mw (5) and used as a criterion to evaluate explosibility capacity of CHNO compounds [START_REF]Recommendations on the transport of dangerous goods: Manual of tests and criteria[END_REF]. It is similar to the oxygen balance (OB) defined in the UN regulation related to the transport of dangerous goods (TDG) as a criterion for explosive compounds [START_REF]Recommendations on the transport of dangerous goods: Manual of tests and criteria[END_REF]. From the plot of Figure 1, it appears that the logarithmic fitting is significantly better than the linear one (R²=0.78 and R²=0.51, respectively). The same behaviour was noticed for almost all the others descriptors when used alone. Therefore, only logh 50% was considered in the following of the study. Moreover, the pertinence of including external descriptors that were not calculated using CodessaPro software was confirmed as they represent four out of the ten most correlated descriptors: OB 100 , n C /mw, OB and n H /mw (see Table 2). Also, some quantum descriptors stood out: the ratio of the zero point vibrational energy on the number of atoms and the ratio of the number of occupied electronic levels on the number of atoms.

Finally, regarding the high correlation of OB 100 with logh 50% , a first linear model (see Table S4).was developed by considering a complete validation method (including external validation): logh 50% =1.714 -0.205 OB 100 (6) It has to be noticed that this model is similar to the equation proposed by Kamlet [7] even if this last was only developed from C(NO 2 ) 3 compounds: logh 50% =1.753 -0.233 OB 100 (7) If Kamlet's equation presented better performance in terms of correlation than this new model (R²=0.94 and R²=0.72, respectively), it was not validated, in particular on an external set of molecules. On the contrary, the new model (Eq. 6) was developed according to all OECD validation principles. It presented good robustness (Q² LOO =0.69, Q² 10CV =0.69, Q² 5CV =0.70) and it did not result from chance correlation as checked by Y-scrambling (R² YS =0.03, SD YS =0.04). Concerning predictivity, the model presented particularly high R² EXT value (0.92) which is surprisingly largely superior to the R 2 one. Such behavior could be related to the small size of the validation set. Nevertheless, the distribution of property values was quite homogeneous in the validation set as shown on Figure 2. Another explanation could be a structural bias between the training and the validation sets. To explore this hypothesis, a series of 10 random partitions has been set up and models were developed for each of them (Table S5). Seven out of the ten randomized models presented same behaviour (R² EXT >R 2 ). The average values of R² EXT remained higher than R 2 ones but none of the randomized partition allowed to reach R² EXT =0.92 of Equation 6. Moreover, when inspecting the chemical structures of validation sets for each 10 randomized partition, no particular molecule was included in the three partitions presenting R² EXT >R 2 (partitions 1,5,7) and absent in the other seven ones. So, no structural feature was identified to explain this behaviour. Furthermore, high standard deviations were observed for both R 2 EXT (0.08) and R 2 (0.04) which can be explained from high experimental uncertainties. Nevertheless, this linear model presented already quite good performances and could be used for first evaluation of the property from a single constitutional descriptor which can be easily calculated (i.e. without any software). In a next step, multi-linear regressions were computed to achieve models that could improve prediction.

Multi-linear regressions

Three models with performances summarized in Table 3 were developed with the BMLR method. The first one was built only from constitutional descriptors which do not require the 3D molecular structures (i.e. without any quantum chemical calculations). Then, all descriptors (about 400) were taken into account for determining a second model, while a third one was defined on the basis of only quantum descriptors.

Model with constitutional descriptors

From 66 constitutional descriptors, a three-parameter model (see Table S6) was found to be the best compromise between correlation and number of descriptors among the six equations sorted out by the BMLR method: logh 50% = -2.53 n N /n atom + 0.07 n single -0.25 n NO2 + 1.94 (8) with n N /n atom the relative number of N atoms (t-test= -2.2), n single the number of single bonds (t-test=7.9) and n NO2 the number of NO 2 groups (t-test=-8.4). From a chemical point of view, the occurrence of NO 2 can be explained because the primary cleavage of the C-NO 2 bond is known to be the main mechanism of decomposition [START_REF] Dewar | Ground-state of molecules.65. Thermolysis of molecules containing NO 2 groups[END_REF][START_REF] Nazin | Thermal-decomposition of aliphatic nitrocompounds[END_REF][START_REF] Nazin | Thermal decomposition of some polynitrocompounds in gas phase[END_REF][START_REF] Korolev | Differentiation of the molecular structures of nitro compounds as the basis for simulation of their thermal destruction processes[END_REF]. The relative number of N atoms and the number of single bonds are not so straightforwardly interpretable. The predicted impact sensitivity using equation 8 as function of experimental values was plotted in Figure 3. The model was characterized by good correlation (R²=0.88, RMSE=0.17) and robustness (Q² LOO =Q² 5CV =0.85 and Q² 10CV =0.84). The Y-scrambling method validated the model because of low values of R² for the models obtained after randomisation (R 2 YS =0.09, SD YS =0.07). As seen in Figure 4, the determination coefficient of each new model (R 2 random) can be plotted versus the correlation between original and randomized y-values (R²Yrandom/Yexp). Besides, the criterion of Rücker [START_REF] Rücker | y-Randomization and Its Variants in QSPR/QSAR[END_REF] was respected: R² -R 2 YS (0.79) is higher than 3 SD YS (0.22). The predictive power was high (R² EXT =0.81, RMSE EXT =0.22, Q² EXT =0.81), in particular in the applicability domain (R² IN =0.78 and RMSE IN =0.23) from which only one molecule (Methylene-bis-N,N'-(2,2,2-trinitroacetamide)) was excluded in the validation set. This model was fully validated and all descriptors involved in equation 8 can be easily calculated from the 2D-structures of the molecules. Moreover, performances from this multivariate analysis were improved compared to the ones of OB 100 -based linear model (Eq.6) since it presented higher R 2 than R 2 EXT values.

Model with all descriptors

In this section, a model for impact sensitivity was developed by taking into account the whole set of 400 calculated descriptors to find out if a better model including chemically interpretable descriptors could be obtained. A model with four descriptors was derived (see Figure 5 and Table S7): logh 50% = -0.018 OB + 4.07 P Qmax-Qmin + 28.5 Q² NO2,max + 4.80 N O,max -0.438 [START_REF] Mullay | Relationships between impact sensitivity and molecular electronic structure[END_REF] with OB the oxygen balance as defined in the TDG regulation [START_REF]Recommendations on the transport of dangerous goods: Manual of tests and criteria[END_REF], P Qmax-Qmin the polarity parameter [START_REF] Osmialowski | Quantum chemical parameters in correlation analysis of gasliquid chromatographic retention indices of amines[END_REF][START_REF] Katritzky | QSPR modeling of flash points: An update[END_REF] defined by the difference between the maximum and minimum charges in the molecule (t-test=7.1), Q² NO2,max the squared of the maximum NO 2 charges obtained by natural population analysis [START_REF] Reed | Intermolecular interactions from a natural bond orbital, donor-acceptor viewpoint[END_REF] (t-test=5.4) and N O,max the maximum nucleophilic reactivity index for a O atom (t-test=2.9). The first observation is that the OB descriptor which has already been exhibited as one of the most correlated descriptors in linear analysis (see Table 2) is the most important descriptor in the equation (t-test=-14.8). The last three ones are quantum chemical descriptors. N O,max and Q 2 NO2,max are related to the electronic and reactivity properties of nitro groups, which are critical in the decomposition process of nitro compounds. N O,max was computed from Codessa software whereas Q 2 NO2,max was calculated externally, inspired by the works of Zang [START_REF] Zhang | Review of the establishment of nitro group charge method and its applications[END_REF] and Cao [START_REF] Cao | Two dominant factors influencing the impact sensitivities of nitrobenzenes and saturated nitro compounds[END_REF]. Besides, the electronic properties of nitro groups also strongly influence the last parameter, P Qmax-Qmin . As shown in Figure 5, equation presented good correlation (R²=0.93, RMSE=0.13) and robustness (Q² LOO =Q² 5CV =0.90 and Q² 10CV =0.89). The model did not result from chance correlation, since the models issuing from Y-randomization presented low correlations (R² YS =0.12, SD YS =0.08). The validation set allowed performing an external validation that demonstrated its good predictive power: R² EXT =0.88, RMSE EXT =0. [START_REF] Lai | New correlations for predicting impact sensitivities of nitro energetic compounds[END_REF]

Model with quantum chemical descriptors

As three out of the four parameters exhibited from previous equation (Eq.9) were quantum chemical descriptors, a new model using only this class of descriptors was developed (see Table S8). To this end, 172 quantum chemical descriptors were employed and a four parameter model was obtained: logh 50% = -1.6 N occ /n atoms -1.4Q O,min -0.013 HDSA1 -16.5 BO N,max + 31 [START_REF] Politzer | A relationship between impact sensitivity and the electrostatic potentials at the midpoints of C-NO 2 bonds in nitroaromatics[END_REF] with N occ /n atoms the number of occupied electronic levels divided by the number of atoms (t-test=-13.9), Q O,min the minimal net atomic charge for a O atom (t-test=-4.3), HDSA1 the hydrogen bonding donor ability of the molecule (t-test=-5.1) and BO N,max the maximal bond order for a N atom (t-test=-3.9). It can be noticed that a different set of quantum chemical descriptors was selected (compared to Eq.9) which could be explained by the important intercorrelation between most of these descriptors in particular those characterizing the distribution of charges in the molecules. Among these descriptors, oxygen and nitrogen atoms, that constitute the nitro groups, stand out in this model: the first because of its charge, the second for its bond order. These descriptors are related to the presence of the nitro groups in the molecule and its stability. This model (Eq. 10) presented also high performances in terms of correlation (R²=0.90, RMSE=0.16), robustness (Q² LOO =0.87, Q² 10CV =0.87 and Q² 5CV =0.88) and predictivity (R² EXT =0.73, RMSE EXT =0.29, Q² EXT =0.68) as seen in Figure 6. Moreover Y-scrambling test ensured against chance correlation (R 2 YS =0.12 and SD YS =0.08). For this model, the whole validation set was included into the applicability domain and the predictive power was R² IN =0.73 and RMSE IN =0.30. Finally, this model was less powerful than the two first ones and did not improve the mechanism meaning.

Discussion

In this paper, four predictive QSPR models were developed and successfully validated according to the OECD principles for the validation of QSAR/QSPR models. A first linear model (Eq. 6) based on oxygen balance presented sufficient performances for use as a first evaluation method. Then, three models (Eqs. 8-10) presenting better performances were obtained using multi-linear regressions. The three models had comparable performances in terms of fitting (with R²≥0.88 and RMSE≤0.17 =0.30) demonstrating the pertinence of associating both constitutional and quantum chemical descriptors in the same model. Table 4 allows comparison of performances between the two new models presenting the better performances (Eqs. 8-9) and those extracted from literature. The new MLR models present better correlation than the model of Mullay [START_REF] Mullay | Relationships between impact sensitivity and molecular electronegativity[END_REF] (R²=0.82) and the two ones of Keshavarz [START_REF] Keshavarz | Simple empirical method for prediction of impact sensitivity of selected class of explosives[END_REF][START_REF] Keshavarz | Prediction of impact sensitivity of nitroaliphatic, nitroaliphatic containing other functional groups and nitrate explosives[END_REF] (R²=0.77 and 0.85 respectively). Only Kamlet's model [START_REF] Kamlet | The relationship of impact sensitivity with structure of organic high explosives. I. Polynitroaliphatic explosives[END_REF] is better in fitting (R²=0.94 vs R²≤0.93 in this study) but without internal or external validation and limited to C(NO 2 ) 3 compounds. In order to have a better comparison with our models, the predictivity of the Kamlet's and Mullay's models was computed using the molecules of the data set not used in the definitions of their models (see definition in Table S1). The computed predictivities for both models (R² EXT =0.74 and 0.70 for the model of Kamlet and Mullay, respectively) were significantly lower than those obtained for the models developed in this study (R² EXT ≥0.81). It should be noticed that Keshavarz's models [START_REF] Keshavarz | Simple empirical method for prediction of impact sensitivity of selected class of explosives[END_REF][START_REF] Keshavarz | Prediction of impact sensitivity of nitroaliphatic, nitroaliphatic containing other functional groups and nitrate explosives[END_REF] were not tested for predictivity because they were developed on the same data set. The two new models were also compared with Wang's models (MLR and PLS) for nitroaliphatic compounds [START_REF] Wang | Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices[END_REF], which were externally validated. The goodness-of-fit of the new MLR models (Eqs. 8-9) were higher than those of Wang's models (R²≥0.88 vs. R²=0.80 and 0.78 for MLR and PLS respectively). Moreover, internal validation exhibited a high robustness for the two new MLR models (Q² LOO ≥0.85) whereas Wang's models felt (Q² LOO =0.51 and 0.48 for MLR and PLS, respectively) due to potential over-parameterization (since 13 descriptors are used in the work of Wang vs. 3 and 4 in this study for Eq.8 and 9 respectively). Furthermore, if the predictivity of Wang's models was also remarkable (R² EXT =0.93 for MLR and 0.97 for PLS) as the one of the best new MLR model (R² EXT =0.88, for Eq. 9), Wang did not define any applicability domain. Besides, the model using only constitutional descriptors remained attractive since it associated good predictivity (R² EXT =0.81) with low computer times.

Conclusion

A data set of 50 nitroaliphatic compounds was used to correlate their impact sensitivities to their molecular structures characterized by more than 400 descriptors including quantum chemical ones. Two efficient QSPR models (when considering only constitutional descriptors or all types of descriptors) were built in agreement with all OECD validation principles of QSAR/QSPR models for regulatory use:

1) The endpoint is well defined as the impact sensitivity which characterizes one aspect of explosibility property of chemicals. All experimental values, extracted from a unique database [START_REF] Storm | Sensitivity relationships in energetic materials[END_REF], were performed based on the same protocol (drop weight impact test). 2) Algorithms are unambiguous: they consist in multi-linear equations with completely defined descriptors, calculated from DFT optimized structures at PBE0/6-31+G(d,p) in the case of the model using quantum chemical descriptors. 3) Models are applicable to nitroaliphatics. The applicability domains were built by interpolation to include 95% of the molecules of the training set based on the descriptors included in the models. 4) The performances of the models were estimated by various validation tests:

-goodness-of-fit was characterized for the molecules of the training set (R², RMSE); -robustness was evaluated by LOO and LMO cross validations (Q² LOO , Q² 10CV , Q² 5CV ); -chance correlation was checked by Y-scrambling tests (R 2 YS , SD YS ); -predictive power was defined using an external validation set (R² EXT , RMSE EXT , Q² EXT ), taking also into account the applicability domain (R² IN , RMSE IN , Q² IN ). 5) Some hints were provided concerning the mechanistic interpretation of models.

Indeed, several descriptors (notably of quantum chemical type) were identified as related to NO 2 groups that are critical in the chemical mechanism involved in the decomposition process of nitro compounds [START_REF] Dewar | Ground-state of molecules.65. Thermolysis of molecules containing NO 2 groups[END_REF][START_REF] Nazin | Thermal-decomposition of aliphatic nitrocompounds[END_REF][START_REF] Nazin | Thermal decomposition of some polynitrocompounds in gas phase[END_REF][START_REF] Korolev | Differentiation of the molecular structures of nitro compounds as the basis for simulation of their thermal destruction processes[END_REF].

Finally, among the four models developed in this study for nitroaliphatic compounds, the two selected ones are to the best of our knowledge the first QSPR models that can accurately predict the impact sensitivity of energetic compounds following all OECD principles needed for regulatory use. One of them does not need any advanced quantum chemical calculations and is then easy to use and implement whereas the other including quantum chemical descriptors achieved more reliable predictions. These models could now be used as efficient alternative methods to experimental characterization to gather impact sensitivity values for nitroaliphatic compounds for which experimental values are unavailable or even for compounds that are not yet synthesized (as a preliminary screening test).

  ) and robustness (Q 2 LOO ≥0.85) as shown on

Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .

 123456 Figure 1. Experimental impact sensitivity (h 50% in cm) vs Oxygen Balance (OB 100 ). Linear and logarithmic fittings are also indicated in blue full line and red dotted line respectively

  , Q² EXT =0.86. Only one molecule of the validation set (1,1,1,6,6,6-hexanitro-3-hexene) was out of the applicability domain. Excluding this molecule, the predictive power remained high with R² IN =0.88 and RMSE IN = 0.19.

Table 3 .

 3 The first model with only constitutional descriptors (Eq. 8) presented already remarkable predictive power (R 2 IN =0.78 and RMSE IN =0.23) and the advantage to be easy to use for new predictions, since no advanced calculations were needed to compute the descriptors in these models. Even more accurate predictions (R 2 IN =0.88 and RMSE IN =0.19) were obtained by including quantum chemical descriptors. Besides, the analysis of the only quantum chemical descriptors led to lower reliability in prediction (R 2 IN =0.73 and RMSE IN

Table 1 : Experimental and predicted values for h 50% (from Eqs. 6, 8, 9, 10) Molecules h 50% exp logh 50% exp Eq. 6 Eq. 8 Eq. 9 Eq. 10

 1

		Training Set
	mol1	1,1,1,3,5,5,5-heptanitropentane	8	0.90	1.02 0.75 0.84	0.79
	mol2	1,1,1,6,6,6-hexanitro-3-hexyne	7	0.85	1.25 0.88 0.90	0.82
	mol3	3,3,4,4-tetranitrohexane	80	1.90	2.18 1.91 1.89	2.10
	mol4	2,2,4,4,6,6-hexanitroheptane	29	1.46	1.71 1.55 1.41	1.57
	mol5	2,2,4,6,6-pentanitroheptane	56	1.75	2.03 1.85 1.91	1.66
	mol6	2,2,2-Trinitroethyl-carbamate	18	1.26	1.35 1.43 1.30	1.28
	mol7	2,2-Dinitro-1,3-propane-diol	110	2.04	1.71 1.98 2.16	2.05
	mol8	Methyl-2,2,2,-trinitro-ethyl carbonate	28	1.45	1.71 1.74 1.56	1.71
	mol9	4,4,4-trinitrobutyramide	40	1.60	1.71 1.62 1.80	1.73
	mol10 Bis-(2,2,2trinitroethyl)-carbonate	16	1.20	0.87 1.06 1.02	1.04
	mol11 Bis-(trinitroethoxy)-methane	17	1.23	1.05 1.22 1.24	1.27
	mol12 N,N'-Bis-(2,2,2-trinitroethyl)-urea	17	1.23	1.18 1.07 1.19	1.18
	mol13 Ethyl-2,2,2-trinitro-ethylcarbonate	81	1.91	1.63 2.00 1.88	2.04
	mol14 Bis-(trinitroethyl)-oxalate	15	1.18	1.12 1.16 1.03	0.87
	mol15 Bis-(trinitroethyl)-oxamide	13	1.11	1.22 1.18 1.18	1.30
	mol16 N-trinitroehyl-4,4,4-trinitrobutyramide	18	1.26	1.34 1.24 1.21	1.28
	mol17 1,5-bis-(trinitroethyl)-biuret	24	1.38	1.28 1.28 1.19	1.19
	mol18 N-(t-butyl)-trinitro-acetamide	110	2.04	2.37 2.08 2.13	2.12
	mol19 1,1,1,7,7,7-hexanitroheptanone-4	34	1.53	1.50 1.40 1.51	1.57
	mol20 2,2-dinitropropyl-trinitrobutyrate	151	2.18	1.77 1.78 2.01	1.89
	mol21 2,2,2-trinitroethyl-4,4-dinitrovalerate	70	1.85	1.77 1.78 1.88	1.86
	mol22 Bis-(2,2-dinitropropyl)-carbonate	300	2.48	1.84 2.17 2.33	2.30
	mol23 Bis-(trinitropropyl)-urea	23	1.36	1.61 1.59 1.24	1.41
	mol24 Bis(trinitroethyl)fumarate	14	1.15	1.44 1.42 1.38	1.24
	mol25 Trinitroethyl-bis-(trinitroethoxy)-acetate	6	0.78	1.11 1.02 1.03	1.07
	mol26 4,4,4-trinitrobutyric anhydride	30	1.48	1.43 1.58 1.55	1.59
	mol27 2,2,2 trinitroethyl-4,4-dinitrohexanoate	138	2.14	1.99 2.02 2.07	2.03
	mol28 Nitroisobutyl-4,4,4-trinitrobutyrate	279	2.45	2.35 2.33 2.42	2.39
	mol29	Tetrakis-(2,2,2-trinitro-ethyl)-orthocarbonate	7	0.85	0.87 0.66 0.86	1.06
	mol30 Methylene-bis-(4,4,4-trinitrobutyramide)	113	2.05	1.80 1.93 1.92	1.90
	mol31 Ethylene-bis-(4,4,4-trinitrobutyrate)	120	2.08	1.89 2.13 1.90	1.99
	mol32	N,N'-Bis-(2,2-dinitro-propyl)-4,4,4-trinitro-butyramide	72	1.86	1.88 1.94 1.83	1.86
	mol33	2,2-dinitropropane-1,3-1,3-diol-bis-(4,4,4-trinitro butyrate)	50	1.70	1.64 1.79 1.71	1.58
	mol34	Bis-(2,2,2trinitroethyl)-4,4,6,6,8,8-hexanitro-undecanedioate	32	1.51	1.51 1.60 1.70	1.43
		Validation Set
	mol35 1,1,1,3-tetranitrobutane	33	1.52	1.54 1.39 1.40	1.52
	mol36 1,1,1,6,6,6-hexanitro-3-hexene	17	1.23	1.37 1.06 1.04	1.09
	mol37	Methylene-bis-N,N'-(2,2,2-trinitroacetamide)	9	0.95	0.99 0.91 0.73	0.68
	mol38 5,5,5-trinitropentanone-2	125	2.10	1.99 1.83 2.21	2.09
	mol39 N-(2-propyl)-trinitroacetamide	112	2.05	2.06 1.82 1.83	1.89
	mol40 2,2,2-Trinitroethyl-4,4,4-trinitrobutyrate	18	1.26	1.29 1.23 1.43	1.34
	mol41 Trinitroethyl-2,2-dinitropropylcarbonate	15	1.18	1.31 1.62 1.49	1.60
	mol42 Tris-(2,2,2-trinitroethyl)-orthoformate	7	0.85	0.93 0.93 0.88	1.06
	mol43 Methylene-bis-(trinitroethyl)-carbamate	27	1.43	1.45 1.61 1.25	1.13

Table 2 :

 2 Descriptors presenting the best linear correlations with logh 50% for the whole data set

	R²	Descriptors
	0.78	Relative number of aromatic bonds
	0.78	Oxygen balance (OB 100 ) according to Kamlet [7]
	0.77	Ratio of number of C atoms on molecular weight
	0.77	Relative number of single bonds
	0.76	Oxygen balance (OB) according to regulation [27]
	0.76	Ratio of zero point vibrational energy on the number of atoms
	0.76	Ratio of number of occupied electronic levels on the number of atoms
	0.76	Relative molecular weight
	0.75	Relative number of H atoms
	0.73	Ratio of number of H atoms on molecular weight

Table 3 :

 3 Performances of QSPR models developed for logh 50% of nitroaliphatic compounds

	in this study
			Eq. 6	Eq. 8	Eq. 9	Eq. 10
	R²	0.72	0.88	0.93	0.90
	RMSE	0.15	0.17	0.13	0.16
	Q² LOO	0.69	0.85	0.90	0.87
	Q² 10CV	0.69	0.84	0.89	0.87
	Q² 5CV	0.70	0.85	0.90	0.88
	R 2	YS	0.03	0.09	0.12	0.12
	SD YS	0.04	0.07	0.08	0.08
	R² EXT	0.92	0.81	0.88	0.73
	RMSE EXT	0.15	0.22	0.19	0.29
	Q² EXT	0.89	0.81	0.86	0.68
	R² IN	0.92	0.78	0.88	0.73
	RMSE IN	0.15	0.23	0.19	0.30
	Q² IN	0.89	0.78	0.87	0.68

Table 4 : Performances of the two more reliable QSPR models developed for logh 50% of nitroaliphatic compounds in this study and extracted from literature Models N mol /N desc R² RMSE Q² LOO R² EXT RMSE EXT

 4 determined based on the molecules of the dataset that were not used for the development of models in the original work (see supporting information) b only applicable for C(NO 2 ) 3 compounds

	Eq. 8 (constitutional descriptors)	50/3	0.88 0.17	0.85 0.81	0.22
	Eq. 9 (all descriptors)	50/4	0.93 0.13	0.90 0.88	0.19
	Kamlet [7] b	28/1	0.94 N.A.	N.A. 0.74 a	N.A.
	Mullay [8]	41/1	0.82 N.A.	N.A. 0.70 a	N.A.
	Keshavarz, 2005 [18]	58/5	0.85 0.20	N.A.	N.A	N.A.
	Keshavarz, 2007 [15]	58/4	0.77 0.19	N.A.	N.A	N.A.
	Wang [22]	43/13	0.80 0.20	0.51 0.93	0.19

a

Acknowledgements

This work was performed using HPC resources from GENCI-CCRT (Grant 2011-t2011086639). The authors are grateful to Dr Enrico Mombelli (INERIS) for precious discussions.


Alpha-Oumar Diallo

Alexander B Morgan

Christophe Len

Guy Marlair
email: guy.marlair@ineris.fr

An innovative experimental approach aiming to understand and quantify the actual fire hazards of ionic liquids

The aim of the study is to produce advanced knowledge on the thermal and combustion hazard profiles of ionic liquids based on an original multiscale combined experimental approach. Experimental tools have been implemented and used to a) obtain actual measurements of theoretical heats of combustion of imidazolium-based and phosphoniumbased ionic liquids by use of a bomb calorimetry; b) provide access to fundamental flammability properties of these chemicals through the use of Pyrolysis Combustion Flow Calorimetry c) determine actual behaviour of ionic liquids in fire conditions, from learnings obtained by a series of combustion tests performed on 12 ionic liquids by use of the INERIS Fire Propagation Apparatus. Results so far confirm that the combustibility potential as well as the fire behaviour must be assessed on a case by case approach and is often dictated by ionic liquid chemical structure. The study also illustrates how the data obtained by our innovative procedure allows for consistent fire safety engineering studies serving the green use of ionic liquids in a contextual way. The work has opened a new perspective of collaborative work towards the development of a dedicated and pertinent methodology aiming at characterizing the comprehensive physicochemical hazards profile of ionic liquids.

Introduction

Ionic liquids (ILs) are advanced chemicals promised by many people to have a brilliant future in a number of strategic applications that might provide a greener future in chemistry and energy related technological developments. [1][2][3][4][5][6][7][8][9][10] In particular, key emerging uses in the sector of energy and environmental technologies are regularly reported in the literature. [11][12][13] One of main advantage systematically claimed for ILs is their improved operational safety in comparison to conventional solvents. This is based on their negligible vapour pressure and most often misleadingly reported "non flammability", not only in manufacturer commercial literature, but also in scientific journals. Indeed, the flammability (or non flammability property) of a given material must always be related to specific conditions prevailing during testing (see ISO 13943 fire safety vocabulary). When considering liquids, flammability is assessed according to flash point values, various threshold values being considered in different regulatory frameworks to rate a liquid as "flammable" or "non flammable" in the context of those regulations. For instance, in the Classification Labelling and Packaging regulation, [START_REF]Parliament and of the Council of 16 December 2008 on Classification, labelling and packaging of substances and mixtures, mending and repealing[END_REF] flammable liquids are those having a flash point below or equal 60°C whereas in the Globally Harmonized System, [START_REF]Globally Harmonized System of Classification and Labelling of Chemicals[END_REF] values as high as 93°C (e.g. in 4 th category) and even higher according to regulatory frameworks considered, would still qualify same liquids as flammable liquids. Subsequently, the only true definition of a flammable liquid is a liquid which is capable of burning with flames. This is why, reporting on ILs as non flammable liquids per se may be misleading, even if somewhat true in the appropriate regulatory context.

We must also keep in mind that ILs cover a very wide range of chemicals, numbering in millions if not more [START_REF] Kirchner | Topics in Current Chemistry[END_REF] and that some ILs may be tuned as to be combustible by design. [START_REF] Smiglak | [END_REF] Additionally, authors of this manuscript have reported elsewhere why, in the case of ILs, the measurement of flash point do not reflect their actual flammability potential. 18 Indeed, other limits of the flash point criterion is bound to the fact that in apparatus developed to measure flashpoint, the flammability is implicitly related to the flaming combustion of a mixture or the vapour phase of the studied liquid and air, which is not necessarily the phenomenon observed with ILs. At last, whatever is the retained method to rate a material as a "non flammable" material, this does not mean that it should be considered as "non combustible" and consequently does not imply that the material in question is 100% safe to use near heat or fire sources. As it was reported by Smiglak, [START_REF] Smiglak | [END_REF] decomposition products from the thermal decomposition of ILs may be highly and purposely combustible, and indeed, others have seen this as well. [19][20][21][22] Furthermore, due to the variety of chemical structures available with ILs, noxious emissions resulting from free burning of these materials could reveal far more different and possibly more exotic as those generated from combustion of existing organic solvents that ILs seek to replace. Clearly there is much more to be studied on these materials and most likely, different tests will be needed to assess the fire hazard of ILs in laboratory and industrial settings. Just as new technologies entering the workplace and home have resulted in new fire standards, very likely ILs will require different fire tests to certify them as safe or of acceptable risk in laboratory and industrial fire risk scenarios.

The present work is a continuation of our previous research to (i) quantify the heat release of ILs; (ii) provide theoretical and experimental data that can quantify the flammability of ILs in all its aspects (ease of ignition, mass burning rate, heat release rate, fire-induced toxicity data...); (iii) provide first guidance to fire safety engineers about how to handle this material in their assessments. Through the use of heat of combustion measurements, pyrolysis combustion flow calorimetry, and the fire propagation apparatus, we show that not all ILs have the same levels of heat release/flammability even though they may have similar predecomposition thermal properties.

Materials and methods

Ionic liquids

Samples of Imidazolium-Based Ionic Liquids (IMBILs) associated with different counteranions were kindly supplied by BASF, respectively under the generic brand commercial names Basionics. Samples of a second family of Phosphonium-based Ionic Liquids (PBILs) associated with different counter-anions were also made available to the laboratory for testing, due to courtesy of the CYTEC Company. Designations of materials and information on their technical grades are given in table 1. Schematic representations of all the studied ILs are in addition shown in figure 1. Experimental tests were performed on the products as received, without any further purification step.

Oxygen Bomb calorimetry

The theoretical energy of combustion of ILs was measured in an oxygen bomb calorimeter (Model 1108P, Oxygen Combustion Bomb, Parr Instrument Co., Moline, Illinois) following ASTM D240 protocol. [START_REF]ASTM D240-09 Standard Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter[END_REF] Prior to sample testing, the bomb calorimeter was calibrated by combusting ten tests of approximately 1 g of standard benzoic acid (NBS Thermochemical Standard, 39g) which has a known heat of combustion of 26.454 MJ/kg. Preliminary testing has revealed that the ignition did not result in effective combustion in the case of all ILs samples by use of the standard ignition procedure in the bomb calorimetry. Thus, in order to achieve actual and effective ignition of ILs during the process of combustion in oxygen, pharmaceutical paraffin oil, which heat of combustion was firstly measured, was mixed in predetermined quantity with the samples. A weighed sample of approximately 0.21 to 0.35 g of IL is placed in a platinum crucible and assembled in the bomb calorimeter. The vessel is then pressurized with pure oxygen to 3.0 MPa for the test and placed inside a bath containing 2 litres of water in an insulated jacket. A motorized stirrer is placed inside the water bath to circulate the water around the bomb creating a uniform temperature. The equilibrium rise of water temperature due to combustion in the bomb calorimeter is recorded using a precision thermistor (Omega Model 1417E). The amount energy released by paraffin oil was subtracted from the total energy released in the bomb to compensate for the added charge.

Ignition correction e 1 is made for the heat contribution from burning of nitrogen trapped in the bomb to form nitric acid. It was assumed that 10 calories is a good correction for e 1 . A correction value e 2 for the combustion of sulphur leading to sulphur trioxide forming sulphuric acid instead of sulphur dioxide is also applied. This adjustment is equal to 13.7 calories per percentage of sulphur in the sample mass. Finally, for the fuse wire an adjustment e3 equal to 15 calories is applied. The gross heat of combustion or high heating value (HHV) is then calculated as: (1

)
where W is the energy equivalent of the calorimeter obtained from the calibration; ΔT is the temperature rise; HHV paraffin , m paraffin and m IL are the gross heat of combustion, weight of paraffin and IL, respectively. Three replicates are performed for each sample, and the typical relative error was less than 0.20%.

Microscale Combustion Calorimetry

The microscale combustion calorimeter also known as Pyrolysis Combustion Flow Calorimeter (PCFC) [START_REF] Walters | [END_REF] is a new small-scale instrument and standardized method (ASTM D7309-07) [START_REF] Astm D | 7309-07 Standard Test Method for Determining Flammability Characteristics of Plastics and Other Solid Materials Using Microscale Combustion Calorimetry[END_REF] for measuring at small scale the heat release from combustible materials via oxygen consumption calorimetry. Samples in the range of 5-50 mg in size are sufficient, making it a potent technique for quantification of material flammability without consuming large amounts of material. Indeed, with the PCFC, one can obtain fundamental heat release data for a material (originally for plastics) as a function of its chemical structure, and can study the heat release rate vs. temperature, as well as the actual heat of combustion behaviour for a wide range of material flammability studies, as related in many works. [START_REF] Yang | [END_REF][27][28][29][30] The samples provided by CYTEC were tested with the microscale combustion calorimeter using a heating rate of 1°C per second, from 100 to 700°C using Method A of ASTM D7309-07. The working principle of the instrument is illustrated in figure 2. The sample is pyrolyzed under nitrogen atmosphere initially, and the gases from pyrolysis zone are pushed into a 900°C furnace, where they are combusted in the presence of oxygen. Each sample was run in triplicate as per the standard to evaluate reproducibility of the flammability measurements.

Fire Propagation Apparatus (Tewarson Calorimetry)

The fire behaviour of ILs was performed using the Tewarson calorimeter also covered under the name of Fire Propagation Apparatus (FPA) by different standards, namely NFPA 287, [START_REF]Standard Test Methods for Measurement of Flammability of Materials in Cleanrooms Using a Fire Propagation Apparatus (FPA)[END_REF] ASTM E2058, [START_REF] Astm E | -03 Standard Test Methods for Measurement of Synthetic Polymer Material Flammability Using a Fire Propagation Apparatus (FPA)[END_REF] and ISO 12136. 33 This bench-scale fire calorimeter implemented at INERIS some 15 years ago has been described in detail by Brohez et al. [START_REF] Brohez | [END_REF]35 and was recently used by Ribière et al. 36 on the fire-induced hazards of Li-ion battery cells. It belongs to the family of fire calorimeters that are bench scale multipurpose testing apparatuses focusing on the characterization of burning behaviour of materials and products in fire conditions. Figure 3 represent a schematic drawing of the equipment as well as a photographic illustration of the apparatus installed in INERIS fire lab. Testing capability of the equipment encompasses ignitability, fire propagation potential, thermal and chemical characteristics in fire condition.

Repeatability and reproducibility of data count among the major advantages of the equipment [START_REF] Marlair | Fire Safety Science, proceedings of the 7 th International Symposium[END_REF] together with its capacity of revealing atypical fire phenomena, like-liquid phase decomposition process of organophosphorous pesticides. [START_REF] Chen | [END_REF] In particular, parametric tests on product samples of about 50 g under controlled air intake allow for characterizing fire behaviour of the studied material or product (liquid, solids, gases) on the full spectrum of fire conditions (fuel rich or fuel lean). Scientific-sound diagnosis of the fire behaviour of materials is achieved thanks to the access to key measures such as mass loss, HRR by application of fire calorimetry laws based on the assessment of oxygen consumption (OC) 39 and carbon dioxide generation (CDG), [START_REF] Mark | Physical Properties of Polymers Handbook[END_REF] measurements of fire effluent concentrations and related emission yields allowing for an evaluation of pollutants and fire toxicity issues.

In this study, preliminary tests were performed in order to choose the most suitable operating conditions, according to ignitability and fire propagation conditions. IL samples (50-65 g)

were poured in a glass sample holder. An external heat flux of 50 kW/m 2 was set in operation by four infrared heaters in an air-flushed jacket, allowing the sample to be heated to a temperature where its vapours or flammable decomposition products can be ignited by an electric spark or pilot flame. These preliminary tests revealed good resistance to ignition for all samples and apart from [EMIM] [DCA] sample, no sustained combustion without application of an external heat flux. According to these observations, testing protocols for reference burning tests were adapted as follow: an external flux of 50 kW/m 2 was applied until ignition was observed, and after ignition external heat flux was diminished to 25 kW/m 2 .

In the case of [EMIM] [DCA] the test was performed with reduced initial mass of sample and by stopping the external flux after ignition, according to observed reactivity in the preliminary experiment with this chemical.

Smoke analysis was performed by use of an online Fourier Transform Infrared (FTIR) spectrometer calibrated over 20 gases for derivation of CO 2 , CO, SO 2 , NOx, HCN, HCl, BF 3

and HF concentrations and mass release rate versus time. The continuous measurement of exhaust gas opacity is done by an opacimeter based on the Beer-Lambert's law, assuming that soot is the only condensed matter responsible for smoke incapacity. Inlet air flow was adjusted to obtain well ventilated fire conditions, as reflected by an equivalence ratio phi parameter [(fuel/air) vs. (fuel/air) stoechiometric ] « 1.

First order assessment of fire-induced toxicity

The toxicity assessment of combustion products involving ILs in accidental pool fires is provided here by means of a simplified model of dispersion of pollutants in a confined environment and illustrated in a theoretical case study. We consider a pool fire of an IL with a given surface S (m 2 ) developing in an enclosure of volume V (m 3 ). The room is subject to a constant air renewal rate corresponding to an inlet flow rate Q (Nm 3 /h), as illustrated in figure 4. In this configuration, we assume that the pollutants are evenly distributed in the entire room (which means the assumption that the room behaves as a well mixed reactor) .

With such simplified assumption, the evolution of concentration Cp (mg/Nm 3 ) of a pollutant p is then given by equation ( 2):

p p in p dC t V P Q C Q C t dt (2)
where P p and C in are the rate of produced pollutant p (expressed in mg/h) and the inlet concentration of pollutant p (expressed in mg/Nm 3 ), respectively. Thus, if we may assume P p as a constant against the time interval of interest, equation ( 2) can be solved as a system of differential equations of first order in C(t), and hence, concentration versus time between final and initial conditions may be expressed by equation ( 3

): , ,0 , 1 Q t Q t p V V p f p p in P C t C e C e Q
(3) However, combustion conditions change continuously with time. Thus, we have considered time intervals, in consistency to periodicity of data acquisition scans to discretize equation (3) and use results obtained for concentration of pollutant p as initial conditions for the implementation of the calculation of the concentration of same pollutant on the next interval.

Assuming that fresh air is blown into the room free of considered pollutants and, at time zero the concentration of pollutant p in the room is zero, the calculations lead to the following equation, for each pollutant:

, , , 1
1 i i pi t t p i p i P C C e e Q (4)
where τ (h -1 ) = Q/V is the number of fresh air renewals by hour and Δt (h) is the time step.

The final step in the methodology consists in converting concentrations of toxics resulting from previous calculations in state of the art fire-induced toxicity indices relating to given critical conditions, as developed by ISO TC92.

In practice, Fractional Effective Dose (FED), respectively Fractional Effective Concentration (FEC), are computed for considering asphyxiant effects, respectively irritant effects of fire gases, assuming a dose effect for asphyxiants and a concentration effect for irritant gases, as referred to in the latest version of ISO 13571. [START_REF]Life-threatening components of fire -Guidelines for the estimation of time available for escape using fire data[END_REF] Corresponding parameters, X FED and X FEC can be obtained from the evolution of pollutant concentrations in the room using equation ( 5) and equation (6), respectively:

Critical values used here in equation ( 5) and equation ( 6) refer to escape impairment that is supposed reached for X FED or X FEC equal to 1 for ordinary sensitive people.

Results and discussion

Complete heats of combustion

The results obtained for all tested ILs by use of the oxygen bomb calorimeter are given in

Pyrolysis Combustion Flow Calorimetry data

Typical results from the PCFC focus on heat release measurements and the results that were recorded from each of the materials are shown in table 3. The data in the table covers the following measurements:

Char yield: this is obtained by measuring the sample mass before and after pyrolysis.

The higher the char yield, the more carbon/inorganic material left behind. As more carbon is left behind, the total heat release should decrease. HRR Peak(s): this was the recorded peak maximum of HRR found during each experiment. The higher the HRR value, the more heat given off at that event. This value roughly correlates to peak heat release rate that would be obtained by the cone calorimeter (ISO 5660), or by the Tewarson Apparatus. Where more than one number is shown in table 3, this indicates that the heat release is a multi-peak heat release, which would be due to multi-step thermal decomposition of the PBILs.

HRR peak(s) Temperature: this correspond to temperatures at which the HRR peaks are observed.

Total HR: this is the total heat release for the sample, which is the area under the curve(s) for each sample analysis.

Char notes: description of the sample residues collected from each test.

From the data in table anions cannot be ruled out either. For example, the TFSI does show greatly lowered initial peak HRR values when compared to the DCA which may come from the evolution of fluoride and sulphur oxides in the TFSI. Ultimately, each of these ILs has its own flammability as dictated by its chemical structure and so some of these results will be useful to mapping out chemical structure / heat release properties appropriate for ILs, as has been done already for polymers via PCFC. [START_REF] Walters | [END_REF] It should be noted here that the PCFC results measure the heat release of the material when the PBILs is pyrolyzed/thermally decomposed under an inert atmosphere followed by a subsequent combustion in a furnace where oxygen in present. The significance of this is that in a real fire event, a material only encounters oxygen prior to ignition. Once a material is ignited, all oxygen is consumed at the flame front. This means that post ignition, all flammable material is pyrolyzed and decomposed in an anaerobic manner. Since the PCFC mimics this real fire behaviour (anaerobic thermal decomposition followed by flame front oxidation), the PCFC can provide a realistic measurement of how much heat release could be given off as the material burns. Indeed, some of the results seen here match some of the observations seen in the FPA (section 3.3) in that PBILs did char, even under forced combustion conditions. PCFC alone is not enough to understand IL flammability, but it is a useful tool and since it only consumes 5-10 mg of material per test, it can be a further useful tool for assessing heat release potential at low cost and low consumption of sample, at a an early stage of ILs development.

Fire propagation apparatus tests

An overview of the results obtained from the combustion of ILs in the was not negligible as compared to flammable solvents like hexane or ethanol but rather short (135s, 138s and 140s respectively) as compared to the other ILs (430s for the [EMIM][BF 4 ]).

Moreover, it was observed that self extinction may occur when external heat flux is set to zero after initial ignition, except for [EMIM] [DCA]. It is noteworthy that ILs generally present good to remarkable flame retardancy properties, as mentioned by Armand. 43 Decomposition temperatures of many ILs generally lies in between 150°C up to 400°C, as studied in differential scanning calorimetry of thermo parametric analysis. 44 This explained that even under significant heat stress (50 kW/m 2 ), production of flammable mixture flow in our experiments required significant time for ignition to take place. Here again, as the FPA results indicate, confirmed flame retardancy properties of tested ILs do not suppress any fire risk.

Provided favourable environmental conditions are achieved, sustained combustion may indeed take place, leading to thermal and chemical threats that vary in nature and intensity for each IL. The combustion rate influencing both thermal and chemical related threats in well ventilated conditions is highly variable. As shown in figure 6a, combustion of [EMIM] [DCA] is developing with very fast kinetics with a peak of heat release about 8 400 kW/m 2 , by far exceeding current values for hydrocarbon pool fires. [START_REF] Ufuah | Proceedings of the World Congress on Engineering[END_REF] This is due to the association of the imidazolium type cation with the dicyanamide anion as reported by Fox. 19 Similarly to the case of IMBILs, heat release profile for tested PBILs differ from conventional liquid pool fire behaviour, where the combustion of the vapour phase of the burning chemical appears as being the driving phenomenon. Figure 7a showed that decomposition of condensed PBILs took place, driving in most cases the combustion process in our test conditions. Here also effective heats of combustion showed quite variable. Figure 7b shows the effect of anion on the combustion. The behaviour of PBILs having the same cation [P 6,6,6,14 ] follow a general trend ranking as [TFSI] > [Cl] > [(iC 8 ) 2 PO 2 ] > [DCA] when looking which anion reduced effective heat of combustion the most. When comparing effective heat release to the theoretical ones, the combustion efficiency varies from 55 to 101%.

Comparison between total heat release from PCFC and FPA calorimeter are illustrated in figure 8. As can be seen, in a majority of cases, the thermal efficiency of the combustion process remains higher in the PCFC, which seems consistent with more favourable combustion conditions in terms of combustible oxidizer mixture.

Products emission factors characterized in well ventilated conditions reveal that high conversion rates of hetero-atoms in parent toxic effluents are obtained in several cases. The combustion of ILs having fluorine elements leads to the formation of HF which condensed vapours attack the quartz glass with production of SiF 4 . Whereas the presence of fluorine in organo-halogenated materials generally contributes to good flame retardancy of such materials, its conversion into HF in fire environments triggers unique hazards to exposed people and fire fighters and even equipments that may justify due considerations in practical use of ILs in laboratory and industrial settings.

Fire induced Toxicity examination of burning ILs

Data obtained by performing combustion tests in the Tewarson calorimetry (i.e. time to ignition, burning rate, actual heat release rate and emission factors of pollutants) can be used as "source term" information allowing to a researcher to perform contextual assessment of risks pertaining to ILs in the real world. Although tremendous progress has been achieved in that domain of fire safety science, such an evaluation remains a complex issue that requires careful consideration of risk assessment objectives, access to reliable input data, and appropriate selection of modelling tools. Therefore an in-depth examination of this issue is considered out of the scope of this paper. Nevertheless, by considering a fictive case study here as a pedagogic material, we develop hereafter how the combustion data obtained by use of the FPA can be used to address fire induced toxicity issues, in terms of fire safety engineering practice. We consider a case study where a given IL is used in a batch reactor.

The following assumption is a worst case fire scenario of likely occurance is the burning in pool-like configuration that involves 10 liters of IL which ignites due to some reason in a building developing an overall volume of 80 m 3 , behaving as a well stirred reactor and the burning pool is assumed being limited to a surface of 0.06 m 2 . Due to limited self-sustained potential of combustion of ILs, and expected fast intervention by fire fighters, we assume in addition that only 30% of the 10 liters of IL will burn ultimately. For the [EMIM] [DCA], we assume a complete consumption of the entire liquid in the fire, due to observed fast combustion process. Thus, in these conditions, the escape impairment criteria obtained by concept of FED and FEC for number of fresh air renewals per hour τ = 3 h -1 , 5 h -1 and 10 h -1 have been computed and plotted in figure 9 and figure 10, respectively, for IMBILs and PBILs.

Although these trends are illustrated for a fictive case study, with basic assumptions that have limited validity, the curves showing the evolution of FED and FEC indices reveal how the users of our experimental methodology may use the data in order to render the use of the given ILs safer. First observation of figure 9 and figure 10 at first illustrates that the fire-induced toxicity potential may clearly differ from one IL to another, due to differences in combustion rates and in nature and rates of fire product releases. In this fictive scenario, the fire one involving the studied ILs examined as the described worst case leads to production of irritant gases in such quantities that fractional effective concentration rises over the critical threshold value for after a few minutes, whichever the air renewal rate is, for both for the IMBILs and the PBILs under consideration. This is due to the release of a large amount of various irritant gases such as NO x , SO 2 , HF, BF 3 , CH 2 O and HCl, essentially formed from the IL relating anion structure. Only combustion of [P 4,4,4,2 ][DEP] does not produce any irritant pollutant, as shown in table 4. Thus, in an accidental fire scenario the major toxicity issue involving this chemical would be the asphyxiants released like the CO.

Detailed comparison however shows that the emergency situation would occur sooner or later according to type of IL considered, possibly allowing different fire safety management strategies (smoke gas protection, emergency escape training, pre-planned intervention procedures...) or in some cases requesting a review of the process in order to diminish the seriousness of the ultimate worst case scenario. Dealing with the asphyxiants gases (limited here to HCN and CO), concerned toxic compounds evolution versus time and relating fractional effective dose is significantly affected by the air renewal rate up to the point to offer a possibility to handle the situation in some cases by increasing the ventilation rate of the building in case of a fire, or by setting a high but still reasonable air renewal rate (up to 10 V/h). Indeed, in the considered case study, large air renewal rate may impede the toxic threat resulting from the emission of asphyxiants from burning IMBILs to become ever critical (X FED always below 1) whereas for the PBILs, large renewal rates offer significantly higher evacuation time before escape impairment.

As a reminder, the present exercise has been provided just for illustrating how our experimental approach may help the user to consider safe use of ILs including the way fireinduced toxicity may be taken into consideration. It only gives trends on this latter aspect, as the integral modelling approach based on the use of equations ( 2) to ( 6) is very simplistic and considers assumptions that very rapidly find their validity limits. Actual evaluation of fire toxicity issue into the building would require tools to proceed to compartment fire modelling (integrating cold and hot smoke layers and fire plume), relying on the use of a fire risk dedicated zone models like Computational Fluid Modelling. Indeed, for such an exercise, same date qualifying the "source term" of fire gases emission characteristics would serve as input data, whereas Q in and Q out would results as output calculations or resulting from boundary conditions.

Conclusion

In this paper, we have shown that the innovative experimental approach resulting from the combined use of three techniques, namely the oxygen bomb calorimetry, the pyrolysis flow calorimetry and the fire calorimetry based on the operation of the Fire Propagation Apparatus, allows for the provision of very useful and consistent information regarding many aspects of flammability of ILs that can in turn feed contextual fire risk analysis for given IL applications.

This has been illustrated by testing 12 of them, splitting into two major families: IMBILs and PBILs. It is confirmed that the fundamental properties of ILs (like very low volatility) in their native form does mean that these materials are harder to ignite, ever for ILs associated to reactive anions like [DCA]; this claim is true. However, the difficulty in ignition can be offset by external sources of energy and once ignited by significantly high heat releases when the materials finally ignite and burn under forced conditions.

Interestingly, many ILs show self-extinguishing behaviour under the sole radiation of the experimental pool fires at small scale. However, how this would translate to real world fire requested to predict heat of combustion, confirmation and first order thermal threat may be confirmed at the micro-scale, and some 20/25 g of sample allows for full implementation of our approach, including making use of the FPA and proceeding to first order evaluation of fire induced toxicity.

At last, the authors reasonably hope that their work brings a new step forward to greener use of those fascinating chemicals that are ILs and that persisting misleading statements about non flammability of ILs will be corrected by all stakeholders in soon future.

  Figure 6b confirms that the heat flow profile reveals unique to each IL, generally significantly lower however as compared to conventional solvents and hydrocarbon fires. For example the combustion of [EMIM][OTf] leads to combustible decomposition products as shown by the appearance of a peak in the reaction medium. The combustion of [EMIM][BF 4 ] is more typical of that of an hydrocarbon showing a plateau throughout the reaction of oxidation. These results confirm the important role of the anion on the flammability, with the general trend of OTf > BF 4 > MeSO 3 > EtOSO 3 > DCA when looking which anion opposes to heat release the most.

  fighting behaviour is not clear at this time. The three techniques used in this paper complement clearly each other, bringing in a logical order of use [e.g.: (a) OB calorimetry, b) PCFC c) FPA], sets of valuable information in order to progress knowledge of ILs flammability for serving fire safety engineering that can be performance based (e.g. meeting prerequisite safety goals), instead of relying on prescriptive codes. Another outstanding result of our work per se is the production of the combustion characteristics of a set of ILs, among the most popular, including detailed identification and quantification of combustion products.

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 123456 Figure 1: Structures of the cations and anions used in this study

Figure 7 :

 7 Figure 7: Combustion heat flux as a function of time during the combustion of PBILs

Figure 8 :Figure 9 :

 89 Figure 8: Comparison between total heat release from PCFC and FPA calorimeter

Figure 10 : 10 Figure 1 :

 10101 Figure 10: Evolution of fractional effective concentration of irritant gas involved in accidental fire scenario for a) IMBILs and b) PBILs, line for τ = 3, dash for τ = 5 and dot for τ = 10

Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 23456 Figure 2: Operating principle of the Pyrolysis Combustion Flow Calorimeter (courtesy Richard E. Lyon)

Figure 7 :

 7 Figure 7: Combustion heat flux as a function of time during the combustion of PBILs

Figure 9 :Figure 10 :

 910 Figure 9: Evolution of fractional effective doses of toxic gas involved in accidental fire scenario for a) IMBILs and b) PBILs, line for τ = 3, dash for τ = 5 and dot for τ = 10

table 2 .

 2 These values are averages of triplicate determinations. For comparison, predicted values of same ILs making use of the purpose-built model22 are given in first column in the same table. As can be seen from the table 2, predicted values of heats of combustion of ILs from the IL-dedicated model were strongly correlated with those obtained from the bomb calorimeter, with an R 2 of 0.99 as illustrated in figure5. This is noteworthy in that, the model based on a quantitative structure-property relationship was initially developed from a database that did not contain any PBILs and still gives very good correlation to real world experimental

results. This confirms that this model, based on weight percentages of main elements in ILs, demonstrated robust prediction capabilities for heats of combustion of ILs. Another conclusion of this experimental part of the work is that combustibility of ILs cannot be ignored, as analysed in terms of potential fire loads that rise up to 40 MJ/kg in the case of the PBILs.

  3, it is clear that the chemical structure of the ILs has an effect on the measured flammability and thermal decomposition / heat release rates of each material. The anion of each IL appears to have a strong influence on char yields and total HR. For instance, this can be examined if we compare [P 6,6,6,14 ][Cl], [P 6,6,6,14 ][(iC 8 ) 2 PO 2 ], [P 6,6,6,14 ][DCA], and [P 6,6,6,14 ][TFSI] which all have the same cation [P 6,6,6,14 ] but differing anions. From this comparison, the dicyanamide (DCA) and the bis(trifluoromethylsulfonyl)imide (TFSI) clearly reduce flammability by increasing char yields in fire conditions mimicked in the PCFC, but vapour phase heat release reductions provided by decomposition chemistry of these two

  Tewarson calorimeter is summarized in table 4 where the following data are listed:

	External heat flux applied (kW/m 2 )
	Initial sample mass (g)
	Overall Mass loss (%) (Char residue by difference to 100%)
	Time to ignition (s)
	Average mass loss rate (g/m 2 /s)
	Peak Mass lost rate (g/m 2 /s)
	Phi factor
	Actual (effective) heat release (MJ/kg)
	Energy conversion efficiency (%)
	Pollutants yields (mg of gas/g of sample)
	Carbon, chlorine, fluorine, sulphur and nitrogen conversions efficiency into related
	combustion products (%).

The data and the observation made during the tests confirm generally good resistance to ignition of ILs, according to ignition time requested under applied external heat flux in initial phase of the tests (50 kW/m 2 ). However, once ignition was obtained sustained and flaming combustion phases were observed and characterized, and confirming real combustibility of all the ILs tested as anticipated from oxygen bomb and PCF calorimetry. Once ignited and provided with sufficient heat, ILs will burn and cannot be considered any longer as "nonflammable". The ignition delay of [P 6,6,6,14 ][(iC 8 ) 2 PO 2 ], [EMIM][DCA] and [P 4,4,4,2 ][DEP]

14 ][Cl] Total heat release from Tewarson calorimetry (MJ/kg) Total heat release from PCFC (MJ/kg)

	Figure 8: Comparison between total heat release from PCFC and FPA calorimeter
	35
						[P 6,6,6,14 ]Cl
				[P 6,6,6,14 ][DCA]	[P 6,6,6,14 ][(iC 8 ) 2 PO 2 ] [P 6,6,6,14 ][DCA] [P 4,4,4,2 ][DEP]
	30		[P 4,4,4,1 ][MeSO 3 ]		[P i4,i4,i4,1 ][TOS] [P 6,6,6,14 ][(iC 8 ) 2 PO 2 ] [P 4,4,4,1 ][MeSO 3 ] [P 6,6,6,14 ][TFSI] [P 6,6,6,
						[P 4,4,4,2 ][DEP]
	25
					[P i4,i4,i4,1 ][TOS]
	20
					[P 6,6,6,14 ][TFSI]
	15
	15		20	25		30	35
	1200				[P 6,6,6,14 ]Cl
						[P 6,6,6,14 ][(iC 8 ) 2 PO 2 ]
	1000				[P 6,6,6,14 ][DCA]
	Heat release rate (kW/m 2 )	200 400 600 800	b			[P 6,6,6,14 ][TFSI]
		0
		0	3	6	9	12	15
				Time (min)

Table captions Table 1 :

 captions1 Mains characteristics of the studied ionic liquids

Table 2 :

 2 Comparison of experimental gross heats of combustion to IL-dedicated model values

Table 3 :

 3 Heat release rate data for phosphonium-based ionic liquid samples tested with the PCFC

Table 4 :

 4 Burning behaviour of ionic liquids in the Fire Propagation Apparatus

Table 1 :

 1 Mains characteristics of the studied ionic liquids

	Ionic liquid	Supplier	Designation	Molecular formula	Molecular weight	Purity (a)
	[P 6,6,6,14 ][Cl]	CYTEC	Cyphos IL 101	C 32 H 68 ClP	519.31	96.4
	[P 6,6,6,14 ][(iC 8 ) 2 PO 2 ]	CYTEC	Cyphos IL 104	C 48 H 102 O 2 P 2	773.27	95.0
	[P 6,6,6,14 ][DCA]	CYTEC	Cyphos IL 105	C 34 H 68 N 3 P	549,90	95.7
	[P i4,i4,i4,1 ][TOS]	CYTEC	Cyphos IL 106	C 20 H 37 O 3 PS	388.55	>99.0 (<0.6[iPr] 3 P; <0.1 MeOT)
	[P 4,4,4,1 ][MeOSO 3 ]	CYTEC	Cyphos IL 108	C 14 H 33 O 4 PS	328.45	96.7
	[P 6,6,6,14 ][TFSI]	CYTEC	Cyphos IL 109	C 34 H 68 F 6 NO 4 PS 2	764.00	97.8
	[P 4,4,4,2 ][DEP] [EMIM][MeSO 3 ] [EMIM][EtOSO 3 ] [EMIM][DCA] [EMIM][BF 4 ] [EMIM][OTf]	CYTEC BASF BASF BASF BASF BASF	Cyphos IL 169 Basionics TM ST 35 Basionics TM LQ 01 Basionics TM VS 03 Basionics TM EE 03 Basionics TM VS 11	C 18 H 42 O 4 P 2 C 7 H 14 N 2 O 3 S C 8 H 16 N 2 O 4 S C 8 H 11 N 5 C 6 H 11 BF 4 N 2 C 7 H 11 F 3 N 2 O 3 S	384.47 206.26 236.29 177.21 197.97 260.23	96.3 ≥95.0 (≤0.5w; ≤2Cl -) ≥95.0 ≥98.0 (≤1.0w) ≥98.0 (≤0.5w) ≥98.0 (≤0.5w)

a As received from the supplier. [iPr] 3 P, triisobutylphosphine; w, water; Cl -, chlorine.

Table 3 :

 3 Heat release rate data for phosphonium-based ionic liquid samples tested with the PCFC

	Sample [P 6,6,6,14 ][Cl] [P 6,6,6,14 ][(iC 8 ) 2 PO 2 ] [P 6,6,6,14 ][DCA] [P i4,i4,i4,1 ][TOS] [P 4,4,4,1 ][MeOSO 3 ] [P 6,6,6,14 ][TFSI] [P 4,4,4,2 ][DEP]	Char % yield 0.05 0.06 0.04 0.04 0.04 0.04 3.48 3.44 3.79 1.33 1.54 1.40 1.32 4.67 4.92 5.01 0.51 0.46 0.52 3.27 1.77 1.90	HRR Peak(s) value (W/g) 561 -489 553 -488 558 -494 564 -483 568 -494 559 -485 646 -536 572 -463 530 -432 46 -581 -522 39 -615 -570 43 -551 -491 41 -534 -495 579 -501 583 -506 573 -498 12 -401 10 -400 11 -385 270 -98 -125 312 -100 -121 299 -112 -118	HRR peak Temp (°C) 407 407 408 402 397 396 449 447 449 412 -491 410 -490 409 -487 414 -489 398 397 398 186 -441 200 -464 198 -443 375 -445 -503 378 -447 -493 375 -446 -501	Total HR (MJ/kg) 32.3 32.1 32.3 33.4 34.0 33.4 29.3 26.7 26.2 28.2 29.4 27.5 26.8 21.9 22.0 21.4 27.4 27.6 27.5 27.9 28.8 27.8	Char notes small black specks around inner edge of pan, no film, pan still white none shiny lacy black char all over inside of pan lots of dark black residue all over inside, over edge and also down part of front of pan black bead of ash, no film residue lots of dark black residue all over inside, over edge and also down part of front of pan fine dusting of black residue all over inside of pan

Table 4 :

 4 Burning behaviour of ionic liquids in the Fire Propagation Apparatus

Acknowledgements

The authors would like to thank CYTEC Canada, Inc., Niagara Falls, ON, for supplying phosphonium samples and BASF Ludwigshafen, Germany, for supplying imidazolium samples. Special thanks are addressed to Jean-Pierre Bertrand for having conducted the experiments on the Tewarson calorimetry. This work was supported in part by the "IEED PIVERT" project and was funded, in part, by the U.S. Department of Commerce, National Institute of Standards and Technology, under Grant 12 # 70NANB9H9183.