Predicting Boiling Points and Flash Points of Monochloroalkanes from

Correlation pubs.acs.org/IECR

Predicting Boiling Points and Flash Points of Monochloroalkanes from Structure Felix A. Carroll,*,† David M. Brown,† and Frank H. Quina‡ †

Department of Chemistry, Davidson College, Davidson, North Carolina 28035, United States Instituto de Química, Universidade de São Paulo, C.P. 26077, São Paulo 05513-970, Brazil

‡

S Supporting Information *

ABSTRACT: Boiling points (TB) of acyclic monochloroalkanes are predicted from their boiling point numbers (YBP) with the 1/3 relationship previously established for hydrocarbons, TB (K) = −16.802 Y2/3 BP + 337.377 YBP − 437.883. The YBP values are determined from molecular structure through the relationship YBP = 1.726 + ICl + 2.779C + 1.716M3 + 1.564M + 4.204E3 + 3.905E − 0.329D + 0.241G + 0.479V + 0.574S. Here ICl is a contribution to the YBP value resulting from the substitution of Cl for H on an alkane, while the other parameters are the same as those reported earlier for calculating YBP values of alkanes. For a data set of 82 acyclic monochloroalkanes having from 4 to 20 carbon atoms, the average absolute deviation between reported TB values and those predicted with these equations was 1.39 K, and the R2 of the correlation was 0.999. In addition, the calculated YBP values provide a useful means to predict the flash points of monochloroalkanes through the relationship TFP (K) = 150.5 ln(YBP) − 185.6.

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INTRODUCTION Boiling point prediction methods continue to be an area of interest in both theoretical and applied chemistry. Some methods involve complex multiparametric or neural network procedures based on theoretical electronic properties or mathematical connectivity functions.1 These approaches can be accurate, but they may be inaccessible to those who do not have proprietary software or who are not practitioners of the techniques required. Much simpler methods involving structural increment counts also have been developed for organic compounds containing a variety of functional groups.2,3 Such procedures are easy to use, but typically they do not offer sufficient accuracy over a large range of molecular weights. Liquid alkyl chlorides comprise a major class of solvents and reagents, both in the research laboratory and in industry. As a result, these compounds have the potential for significant environmental impacts as well.4 One of the most important physical properties of an organic liquid, both for determining appropriate uses as a solvent or reagent and for estimating its persistence in the environment, is its normal boiling point (TB). When experimental boiling points are not available, as may be the case when a new compound is proposed for synthesis, a predicted boiling point is desirable. One of the complicating factors in predicting alkyl chloride boiling points directly from structure is that boiling points do not increase linearly with the incremental addition of structural units. For example, Figure 1 shows the curvature observed when the boiling points (●) of the linear 1-chloroalkanes from 1-chlorobutane to 1-chloroeicosane are plotted against chain length. An approach to this problem that was originally proposed by Kinney is to develop a mathematical function of boiling point that does increase linearly with structural increments.5,6 Kinney’s relationship between TB and a parameter Y, which he called a boiling point number, is shown in eq 1. © XXXX American Chemical Society

Figure 1. Nonlinear relationship of literature TB values (●, left axis) and linear relationship of YBP values (○, right axis) with the number of carbon atoms in a series of 1-chloroalkanes from 1-chlorobutane to 1chloroeicosane. The solid line shows the best-fit linear correlation (R2 = 0.999) of the YBP values.

TB (K) = 230.14Y 1/3 − 269.85

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Kinney then developed a method for calculating Y values from structural increments. This approach was rather accurate for many lower molecular weight compounds but was less accurate Received: August 21, 2014 Revised: November 24, 2014 Accepted: December 3, 2014

A

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for larger structures.7 We found that the Kinney approach could be made more accurate for higher molecular weight acyclic alkanes by using a quadratic equation to calculate TB values from a new boiling point number, which we termed YBP (eq 2).7 2/3 1/3 TB (K) = −16.802YBP + 337.377YBP − 437.883

to nonhydrocarbons. The results presented here show that a method developed for hydrocarbons can be used to predict the boiling points and flash points of monochloroalkanes as well.

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METHOD AND RESULTS We compiled a data set with the normal boiling points of 82 linear and branched acyclic monochloroalkanes (listed in the Supporting Information) containing from 4 to 20 carbon atoms and having boiling points from 341.4 to 644.4 K. We then calculated the experimental YBP values for these alkyl chlorides from eq 6, where a = −16.802, b = 337.377, and c = −437.883.7 As a test of the applicability of our method to alkyl chlorides, we plotted the YBP values for the 1-chloroalkanes (○) against the number of carbon atoms in the series of compounds from 1-chlorobutane to 1-chloroeicosane. An excellent linear correlation was obtained, as shown in Figure 1.

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We also improved Kinney’s method for calculating boiling point numbers from structure by introducing new parameters and then optimizing the coefficients of all parameters for a set of 198 linear and branched acyclic alkanes containing from 6 to 30 carbons.7 The resulting equation is shown in eq 3. YBP = 1.726 + 2.779C + 1.716M3 + 1.564M + 4.204E3 + 3.905E + 5.007P − 0.329D + 0.241G + 0.479V (3)

+ 0.967T + 0.574S

Here C is the number of carbon atoms in the longest chain, M3 is the number of methyl substituents on carbon 3 of this chain (counting from either end), M is the number of methyl substituents at other positions, E3 and E are the number of corresponding ethyl substituents, P is the number of propyl substituents, D is the number of 2,2-dimethyl groupings (counting from either end), G is the number of geminal substitutions at other positions, V is the number of vicinal alkyl substituents, T is the number of instances of two methyl substituents on both carbons one and three of a three-carbon segment of the main chain, and S is the square of the ratio of total number of carbons to the number of carbons in the longest chain. This approach gave an excellent correlation of experimental and predicted boiling points of alkanes (R2 = 0.999). Subsequently, we found that this approach also worked well for alkenes, alkylbenzenes, and alkynes.8 The YBP values of hydrocarbons also provided a simple and accurate way to predict their flash points. The flash point (TFP) is the lowest temperature at which the vapor−air mixture above a substance can be ignited, so it is an indicator of the fire hazard associated with a flammable liquid. Experimental flash points are generally less available than boiling points, however, and there can be wide variation in the TFP values reported for the same compound.9 A flash point prediction method can be employed to assess the fire hazard of a compound for which there is no reported TFP value or for which reported values differ significantly,10 and a number of methods for predicting flash points of organic compounds from structure have been reported.11−16 We found that the flash points of hydrocarbons could be calculated from a new parameter, called the flash point number (NFP), as shown in eq 4. 2/3 1/3 TFP (K) = 23.369NFP + 20.010NFP + 31.901

YBP

∑ niGi i

⎤3 b2 − 4a(c − TBP) ⎥ ⎥ 2a ⎦

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Next we sought to modify eq 3 so that the YBP values of linear and branched monochloroalkanes could be calculated directly from molecular structure. The simplest approach was to add a parameter, ICl, to account for the effect on YBP of replacing a hydrogen atom of an alkane with a chlorine atom. This produced eq 7, which is identical to eq 3 except for the addition of the ICl parameter and the omission of the P and T parameters because those structural features were not included among compounds in the data set. We also modified the interpretation of the M3 parameter, which measures the effect of a methyl substituent on the third carbon from either end of the longest carbon chain. The chloroalkane TB data suggested that this methyl effect is muted when a chloro substituent is nearby. For that reason, we counted a methyl substituent on carbon 3 as M, not M3, when it was vicinal or geminal to a chloro substituent. However, M3 retains its meaning as the count of methyl substituents on carbon 3 of an alkyl chain when there is not a vicinal or geminal chlorine. YBP = 1.726 + ICl + 2.779C + 1.716M3 + 1.564M + 4.204E3 + 3.905E − 0.329D + 0.241G + 0.479V + 0.574S

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To establish the predictivity of the resulting correlation, we divided the data set into groups of 1°, 2°, and 3° chloroalkanes. Then we used the random number generator of Microsoft Excel to assign about 70% of each category of alkyl chlorides to a training set (58 compounds total) and the remainder to a test set (24 compounds).18 We determined the experimental YBP values of compounds in the training set with eq 6 and then calculated their predicted YBP values using initial guesses for the ICl values of 1°, 2°, and 3° alkyl chlorides. Next we used the Solver add-in of Microsoft Excel to determine the values of ICl that produced the lowest average absolute deviation (AAD) between YBP values calculated from boiling points with eq 6 and those predicted from structure using eq 7. The resulting values of ICl for 1°, 2°, and 3° alkyl chlorides are shown in Table 1. Although these ICl values are empirical, they seem chemically reasonable because intermolecular interactions involving a chlorine substituent should be more pronounced when the chlorine is bonded to a less substituted carbon than when it is bonded to a more substituted carbon atom.

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In subsequent work involving 1000 diverse organic compounds, we found that NFP values were linearly related to YBP values through eq 5, where Gi is a functional group-specific parameter and ni is the number of instances of that functional group in the structure.17 NFP = 0.095 + 0.974YBP +

⎡ −b + =⎢ ⎢ ⎣

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On the basis of the success of our approach for predicting the boiling points and flash points of hydrocarbons from structure, we have begun to investigate the applicability of this approach B

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Table 1. ICl Values for Alkyl Chloride Substitution Patterns substitution pattern

ICl

primary alkyl chloride secondary alkyl chloride tertiary alkyl chloride

6.47 5.47 5.21

As an example of the application of the method, consider the calculation of YBP for 2-chloro-2,4,4-trimethylpentane. This is a 3° alkyl chloride, so ICl is 5.21. There are three methyl substituents on a five-carbon chain, so C is 5 and M is 3. The two methyl groups on C4 are on the second carbon from one end of the chain, so D is 1. Therefore, the calculated value of YBP is 1.726 + 5.18 + 5 × 2.779 + 3 × 1.564 − 0.329 + 0.574 × (8/5)2 = 26.66. This matches the experimental YBP value obtained with eq 6. We calculated predicted YBP values of the other alkyl chlorides in the training set in the same way. Then we used these YBP values in eq 2 to predict boiling points of compounds in the training set. The resulting correlation of reported and predicted TB values (Figure 2) was very good, with an R2 of

Figure 3. Correlation of literature boiling points of 24 linear and branched acyclic alkyl chlorides in the test set with TB values predicted using YBP values calculated from structure via eq 7. The diagonal line represents perfect correlation of literature and predicted TB values.

predict properties of alkyl halides. For the 29 monochloroalkanes for which boiling points were reported, the AAD calculated using data in this paper is 2.68 K.19 Another group developed a method for predicting boiling points of mono- and polyhaloalkanes from electrotopological state indices. For the 28 monochloroalkanes in that study, the R2 was 0.981, and the AAD was 8.3 K.20 It is notable that these other methods rely on parameters that must be computed in some way, and the necessary software may not be available to all chemists. Thus, the empirical method reported here not only offers a more accurate way to predict the boiling points of monochloroalkanes than do other methods but it also requires only parameters that are evident from a structure drawing. In addition to predicting an unknown physical property value, an accurate empirical correlation can also help chemists evaluate the reliability of literature data. For example, the experimental boiling point for 4-chloroheptane (417.15 K) appears to be the value of 143.4−144.4 °C, similar to that reported in 1928.21 In that study, the boiling point of 3chloroheptane was reported to be the same (143.1−144.4 °C). Based on many sources, the boiling point of 3-chloroheptane is now widely accepted to be 423.15 K. Therefore, it seems reasonable that 423.15 K may be a more accurate TB value for 4-chloroheptane as well. That conclusion is reinforced using the method reported here, which also predicts the two isomers to have the same TB value (424.93 K). Additionally, removing 4chloroheptane from the training set makes no difference in the value of ICl for 2° chloroalkanes, further supporting the view that its reported TB value is not consistent with the boiling points reported for homologous compounds. Next we used the YBP values calculated with eq 7 to predict the NFP values of the alkyl chlorides from eq 5. The Gi value for chlorine found in our earlier study of 1000 compounds (1.533) was based on a set of diverse aliphatic, aromatic, and multifunctional alkyl chlorides. Using 1.533 for Gi in eq 5 produced eq 8.

Figure 2. Correlation of literature boiling points of 58 linear and branched acyclic alkyl chlorides in the training set with TB values predicted using YBP values calculated from structure via eq 7. The diagonal line represents perfect correlation of literature and predicted TB values.

0.999, a standard error (SE) of 1.94 K, and an AAD between reported and predicted TB values of 1.44 K. When we used the values of ICl optimized for the training set to predict boiling points of the compounds in the test set, a similarly excellent correlation was obtained (Figure 3). The R2 was 0.999, the SE was 1.93 K, and the AAD between reported and predicted TB values was 1.26 K. The AAD for the entire group of 82 compounds was 1.39 K.

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DISCUSSION There are very few reports of alkyl chloride boiling point prediction methods in the literature, but we can compare the present results with those of investigators who included alkyl chlorides in studies of diverse organic compounds or who studied chloroalkanes along with other alkyl halides. In one study, a semiempirical topological index method was used to

NFP = 0.095 + 0.974YBP + 1.533 = 1.628 + 0.974YBP (8) C

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values was obtained (Figure 5), with an R2 of 0.995, a standard error of 2.68 K, and an AAD of 2.2 K. Thus, the calculation of

When NFP values for the monochloroalkanes in the current data set were used in eq 4 to predict their flash points, the correlation between reported and predicted TFP values had an AAD of 5.2 K. This result compares favorably with that of other methods for predicting flash points, which usually give AADs of 6−12 K.15,22,23 Nevertheless, we noted an increasing difference between predicted and reported TB values for compounds boiling above 500 K. This result suggested that eq 5 is not suited for higher boiling compounds, particularly the 13 monochloroalkanes with TB values ranging from 500 to 644 K. We decided to seek a flash point prediction method that not only is more accurate for the higher molecular weight chloroalkanes but also is based more directly on YBP values and flash points. First, we plotted reported TFP values against calculated YBP values for all of the linear 1-chloroalkanes from 1-chlorobutane to 1-chloroeicosane (except for 1-chlorononadecane, for which a reported flash point was not available). As shown in Figure 4, a smooth trend was observed for 15 of the

Figure 5. Correlation of reported flash points of 81 linear and branched acyclic alkyl chlorides with TFP values calculated directly from YBP values with eq 9. The diagonal line represents a perfect correlation of literature and predicted values.

YBP from structure via eq 7 provides a simple yet highly accurate way to predict the flash points of monochloroalkanes.

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CONCLUSIONS The method for predicting the boiling points of monochloroalkanes presented here is quite simple to use, but its accuracy is comparable to or better than that of computational methods based on theoretical, connectivity, or topological parameters. In addition, the YBP values calculated with eq 7 provide a useful way to predict the flash points of the monochloroalkanes. This successful extension of prediction methods developed for hydrocarbons suggests that our approach may be useful for other nonhydrocarbons as well. Efforts to develop similar correlations for compounds containing different functional groups are currently underway.

Figure 4. Correlation of reported flash points of 1-chloroalkanes with YBP values predicted with eq 7. The dashed line shows a plot of eq 9, and the flash point of 1-chlorooctadecane is indicated (×).

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homologues, but the flash point found for 1-chlorooctadecane (×) is inconsistent with the trend exhibited by the other compounds. Flash points can be measured in a variety of ways (e.g., open cup or closed cup, large sample volume or small sample volume), so some variation in reported TFP values can be expected.24 Nevertheless, the only reported flash point for 1chlorooctadecane that we could find differs from that predicted by the best-fit line through the data points for the other 1chloroalkanes (eq 9) by 29.6 K. That is considerably more than the mean 1.9 K deviation from the best-fit line observed for the other 1-chloroalkanes. Therefore, we conclude that it is reasonable to exclude 1-chlorooctadecane when calculating the best-fit line through the other data points. Doing so produced a correlation of reported and predicted flash points having an R2 of 0.997. TFP (K) = 150.5 ln(YBP) − 185.6

ASSOCIATED CONTENT

S Supporting Information *

Data set of monochloroalkanes along with their literature boiling points and references, YBP values, counts of the structural parameters used in eq 7, predicted TB values, reported flash points and references, predicted TFP values, and sample TBP and TFP calculations. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*F. A. Carroll. Tel. 704-894-2544. Fax: 704-894-2709. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial and fellowship support from Davidson College and ́ from Conselho Nacional de Desenvolvimento Cientifico e Tecnológico are gratefully acknowledged. F.H.Q. is affiliated

When we used eq 9 and YBP values calculated with eq 7 to predict the flash points of 80 linear and branched compounds in the flash point data set (excluding 1-chlorooctadecane), a much improved correlation of predicted and reported TFP D

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(22) Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Pidol, L.; Jeuland, N.; Creton, B. Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods. Energy Fuels 2011, 25, 3900−3908. (23) Mathieu, D.; Alaime, T. Insight into the Contribution of Individual Functional Groups to the Flash Point of Organic Compounds. J. Hazard. Mater. 2014, 267, 169−174. (24) Chen, C.-P.; Chen, C.-C.; Chen, H.-F. Predicting Flash Point of Organosilicon Compounds Using Quantitative Structure Activity Relationship Approach. J. Chem. 2014, 2014, Article ID 482341, http://dx.doi.org/10.1155/2014/482341.

with the Brazilian National Institute for Catalysis in Molecular and Nanostructured Systems (INCT-Catalysis) and the USP Consortium for Photochemical Technology.

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REFERENCES

(1) For a discussion and leading references, see Katritzky, A. R.; Kuanar, M.; Slavov, S.; Hall, C. D.; Karelson, M.; Kahn, I.; Dobchev, D. A. Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction. Chem. Rev. 2010, 110, 5714−5789. (2) Joback, K. G.; Reid, R. C. Estimation of Pure-Component Properties from Group-Contributions. Chem. Eng. Commun. 1987, 57, 233−243. (3) Stein, S. E.; Brown, R. L. Estimation of Normal Boiling Points from Group Contributions. J. Chem. Inf. Comput. Sci. 1994, 34, 581− 587. (4) Chlorinated Paraffins; de Boer, Jacob, Ed.; Springer-Verlag: Berlin, 2010. (5) Kinney, C. R. A System Correlating Molecular Structure of Organic Compounds with their Boiling Points. I. Aliphatic Boiling Point Numbers. J. Am. Chem. Soc. 1938, 60, 3032−3039. (6) Kinney, C. R. Calculation of Boiling Points of Aliphatic Hydrocarbons. Ind. Eng. Chem. 1940, 32, 559−562. (7) Palatinus, J. A.; Sams, C. M.; Beeston, C. M.; Carroll, F. A.; Argenton, A. B.; Quina, F. H. Kinney Revisited: An Improved Group Contribution Method for the Prediction of Boiling Points of Acyclic Alkanes. Ind. Eng. Chem. Res. 2006, 45, 6860−6863. (8) Godinho, J. M.; Lin, C.-Y.; Carroll, F. A.; Quina, F. H. A Simple Method to Correlate, Evaluate, and Predict Boiling Points and Flash Points of Alkynes. J. Braz. Chem. Soc. 2012, 10, 1895−1899 and references therein. (9) Radnoff, D. Accuracy of Reported Flash Point Values on Material Safety Data Sheets and the Impact on Product Classification. J. Occup. Environ. Hyg. 2013, 10, 540−546. (10) Dearden, J. C.; Rotureau, P.; Fayet, G. QSPR Prediction of Physico-Chemical Properties for REACH. SAR QSAR Environ. Res. 2013, 24, 279−318. (11) Suzuki, T.; Ohtaguchi, K.; Koide, K. A Method for Estimating Flash Points of Organic Compounds from Molecular Structures. J. Chem. Eng. Jpn. 1991, 24, 258−261. (12) Gharagheizi, F.; Alamdari, R. F. Prediction of Flash Point Temperature of Pure Components Using a Quantitative Structure− Property Relationship Model. QSAR Comb. Sci. 2008, 27, 679−683. (13) Patel, S. J.; Ng, D.; Mannan, M. S. QSPR Flash Point Prediction of Solvents Using Topological Indices for Application in Computer Aided Molecular Design. Ind. Eng. Chem. Res. 2009, 48, 7378−7387. (14) Katritzky, A. R.; Petrukhin, R.; Jain, R.; Karelson, M. QSPR Analysis of Flash Points. J. Chem. Inf. Comput. Sci. 2001, 41, 1521− 1530. (15) Katritzky, A. R.; Stoyanova-Slavova, I. B.; Dobchev, D. A.; Karelson, M. QSPR Analysis of Flash Points: An Update. J. Mol. Graphics Modell. 2007, 26, 529−536. (16) Liu, X.; Liu, Z. Research Progress on Flash Point Prediction. J. Chem. Eng. Data 2010, 55, 2943−2950. (17) Carroll, F. A.; Lin, C.-Y.; Quina, F. H. Simple Method to Evaluate and to Predict Flash Points of Organic Compounds. Ind. Eng. Chem. Res. 2011, 50, 4796−4800 and references therein. (18) Gramatica, P. Principles of QSAR Models Validation: Internal and External. QSAR Comb. Sci. 2007, 26, 694−701. (19) Arruda, A. C. S.; Junkes, B.de S.; Souza, E. S.; Yunes, R. A.; Heinzen, V. E. F. Semi-empirical Topological Index to Predict Properties of Halogenated Aliphatic Compounds. J. Chemometrics 2008, 22, 186−194. (20) Cash, G. G.; Hartigan, S.; Tunkel, J. Normal Boiling Points of Haloalkanes from Electrotopological State Indices. Toxicol. Environ. Chem. 2008, 90, 1073−1089. (21) Dillon, R. T.; Lucas, H. J. Some Derivatives of n-Heptane. J. Am. Chem. Soc. 1928, 50, 1711−1714. E

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