CORRELATION pubs.acs.org/IECR
Simple Method to Evaluate and to Predict Flash Points of Organic Compounds Felix A. Carroll,*,† Chung-Yon Lin,† and Frank H. Quina‡ † ‡
Department of Chemistry, Davidson College, Davidson, North Carolina 28035, United States Instituto de Química, Universidade de S~ao Paulo, CP 26077, S~ao Paulo 05513-970, Brazil
bS Supporting Information ABSTRACT: Flash points (TFP) of organic compounds are calculated from their flash point numbers, NFP, with the relationship TFP = 23.369NFP2/3 þ 20.010NFP1/3 þ 31.901. In turn, the NFP values can be predicted from boiling point numbers (YBP) and functional group counts with the equation NFP = 0.974YBP þ ∑i niGi þ 0.095 where Gi is a functional group-specific contribution to the value of NFP and ni is the number of such functional groups in the structure. For a data set consisting of 1000 diverse organic compounds, the average absolute deviation between reported and predicted flash points was less than 2.5 K.
’ INTRODUCTION The flash point (TFP) of a liquid is the lowest temperature at which the mixture of air and vapor above the surface of the liquid can be ignited. For this reason, flash points are the most commonly specified measure of the fire hazard associated with the storage, transport, and use of flammable substances.1,2 Flash points are classified as being either closed cup or open cup, depending upon the design of the test apparatus. The open cup flash point of a substance is generally higher than the closed cup flash point, often by several degrees, but this is not always the case.1,3 Because of their importance for the safe use of flammable chemicals, methods for evaluating reported TFP values and for predicting the flash points of new substances continue to be of interest. Previously reported methods for predicting the flash points of organic compounds may be classified by their input parameters. Some methods are based entirely or partly on other physical properties, such as boiling point,4,5 density,6 vapor pressure,7 or enthalpy of vaporization.8 Some methods are based on functional group counts9,10 or molecular connectivity indices.1113 Still others use computed descriptors, such as theoretical properties of an electron density surface.14 The selected parameters may then be utilized in linear quantitative structureproperty relationships (QSPR), may be transformed into nonlinear relationships incorporating exponential or logarithmic terms, or may be used in neural network procedures. Each of these approaches has its particular advantages as well as its inherent limitations. Any compound for which literature values of vapor pressure, density, or enthalpy of vaporization are available is likely to be a compound for which an experimental flash point is also available. Therefore, methods based on these physical properties may be useful in evaluating reported flash points but have little predictive utility. Multiple linear regression methods for predicting TFP values from connectivity or from computed parameters are limited because flash points are not linear with the number of repeat units in a series of homologous compounds, as is illustrated for a series of 1-alkenes by the filled circles in Figure 1. In principle, neural network procedures r 2011 American Chemical Society
should be able to overcome this restriction because they are inherently nonlinear with input parameters, but they have not proven to be more accurate than other methods. Boiling points are also nonlinear with the number of carbon atoms in a homologous series. Recently, however, we developed an accurate correlation of alkane boiling points (TB) with structure through use of boiling point numbers (YBP), which do vary linearly with structural increments.15 The YBP values were determined from experimental boiling points via eq 1, where the constants a, b, and c are 16.80, 337.38, and 437.88, respectively. The YBP values could also be calculated from the length of the longest carbon chain, the nature and location of substituents on this chain, and the overall shape of the molecule. Predicting boiling points from YBP values calculated from structure gave a correlation between literature and predicted values with R2 = 0.999 and an average absolute deviation (AAD) of 1.45 K.16 " pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi#3 b þ b2 4aðc TB Þ YBP ¼ ð1Þ 2a The success of this approach led us to introduce a new flammability parameter for paraffins, the flash point number (NFP), as shown in eq 2. Here the constants a, b, and c are 23.369, 20.010, and 31.901, respectively. " pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi#3 b þ b2 4aðc TFP Þ NFP ¼ ð2Þ 2a For acyclic alkanes, NFP values were found to correlate with boiling point numbers as shown in eq 3. NFP ¼ 1:020YBP 1:083
ð3Þ
Received: October 20, 2010 Accepted: March 15, 2011 Revised: March 9, 2011 Published: March 25, 2011 4796
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CORRELATION
Figure 1. Nonlinear relationship of reported TFP values (b, left axis) and linear relationship of NFP values (O, right axis) with the number of carbon atoms in a series of 1-alkenes. The solid line shows the best-fit linear correlation of the NFP values, and the dashed line shows the bestfit nonlinear correlation of the TFP values.
The NFP values of the alkanes could also be predicted from structure by considering the number and pattern of substituents on the main carbon chain. In either case, the flash points were then calculated from these NFP values using the relationship in eq 4. TFP ðKÞ ¼ 23:369NFP 2=3 þ 20:010NFP 1=3 þ 31:901
ð4Þ
For a set of 102 linear and branched acyclic alkanes, the correlation of literature flash points with TFP values predicted using eq 4 had R2 = 0.985 and an average absolute deviation (AAD) of 3.4 K.17 For hydrocarbons in general, we subsequently found that NFP values were accurately predicted with eq 5. NFP ¼ 0:986YBP þ 0:179D þ 0:685T þ 0:679B 0:136 ð5Þ Here D is the number of olefinic double bonds in the structure, T is the number of triple bonds, and B is the number of aromatic rings. Using YBP values determined from literature boiling points and eq 1, we calculated the NFP values of 300 hydrocarbons with eq 5 and then used these NFP values to predict their flash points with eq 4. The correlation of literature and predicted TFP values had an R2 of 0.990 and an AAD of 2.9 K.18 The success of this method for predicting the flash points of hydrocarbons led us to investigate whether incorporation of additional functional group parameters into eq 5 might enable the prediction of flash points of nonhydrocarbons as well. This was indeed the case, and we now report a simple and highly accurate method to correlate, evaluate, and predict the flash points of organic compounds in general.
’ METHOD AND RESULTS For this study we compiled a list of reported boiling points and flash points for a set of 1000 diverse organic compounds.19 Among these were 132 cyclic and acyclic alkanes as well as 868 other compounds containing one or more functional groups. Among these 868 compounds were 86 alkenes plus 103 multifunctional compounds containing a total of 241 carboncarbon double bonds, 42 carboncarbon triple bonds, 173 aromatic (benzene or pyridine) rings, 138 esters, 121 ethers, 98 aldehyde or ketone carbonyl groups, 6 dialkyl carbonates, 102 alcohol
hydroxyl groups, 15 phenolic OH groups, 18 carboxylic acids, 9 carboxylic acid anhydrides, 72 aliphatic amines, 23 anilines or pyridines, 10 amides, 24 nitriles, 26 thiols, 22 dialkyl sulfides, 7 disulfides, 8 fluorides, 63 chlorides, 21 bromides, 16 iodides, and 10 nitro compounds. The literature boiling points found for a particular compound in the data set were generally very consistent from one source to another, but often there was great variation in the reported flash points.20 We found only one TFP value for 96 of the compounds. For the other 904 compounds, however, the average difference between the highest and lowest reported TFP value for each compound was more than 7 K. The discrepancies were quite large for some compounds (e.g., 91 K for 2,6-diisopropylaniline), and 121 of the compounds had differences among reported TFP values of 15 K or more. In some cases, it appeared that TFP values in degrees Fahrenheit may have been erroneously reported as degrees Celsius values. The references often did not specify whether values were from open cup or closed cup measurements, which could account for several degrees of difference.1 Other sources of the variations in reported values from one reference to another may have been differences in the apparatus used, experimental error, or measurements carried out on impure samples. Since the references usually did not provide details that would allow evaluation of these possibilities, we could only take reported values at face value. Some specific examples illustrate the issues involved in assembling a data set. The five primary sources for TFP values in this study reported the flash point of 2-octanol as 333, 344, 345, 349, and 354 K.20 These five values imply five independent measurements, so one option would have been to use their average in the data set. Often, however, the references reported either one or the other of two very different flash points for a compound. As an illustration, three sources gave 319 K as the flash point for diallyl sulfide, while two sources reported 296 K. In such cases, averaging the five reported values did not seem appropriate because there might have been only two independent experimental determinations. Nevertheless, it would not be possible to develop an accurate correlation of flash points with molecular structure without first determining in some way which reported values to include in the data set. One of the strongest forms of chemical intuition is based on the concept of homology. That is, we expect a series of similarly related compounds to show a regular trend in properties. In fact, there is a linear variation in the NFP values of a homologous series of 1-alkenes with the number of carbons in the structure, as shown by the open circles in Figure 1. We therefore reasoned that a similar pattern might also be observed for other homologous series, and if so, it could be used to select reported flash points that were most consistent with the trend exhibited by like compounds. For example, the circles in Figure 2 show NFP values calculated from the various reported flash points for a series of 2-alkanols, from 2-propanol to 2-dodecanol. The asterisks indicate the NFP values that are more consistent with a linear dependence (indicated by the diagonal line) of NFP on the number of carbon atoms in the structures. Therefore the TFP values associated with the points indicated by asterisks were chosen for inclusion in the data set. In some cases, reported flash points were available for only a few compounds of a homologous series, so we selected those TFP values that seemed most consistent with the trend exhibited by closely related compounds. For example, the flash point of 2,6-dimethylheptane was reported to be both 29920a and 326 K,20e but six isomeric 4797
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CORRELATION
Table 1. Values of Parameters in Equation 6 functional group
olefinic double bond
Figure 2. NFP values (O) calculated from reported TFP values for a series of 2-alkanols. The asterisks indicate those values chosen for inclusion in the data set, and the diagonal line shows the least-squares correlation through the selected values.
dimethylheptanes have reported TFP values of 288 K. Therefore, we used 299 K as the flash point of 2,6-dimethylheptane, because that reported value was closer to the 288 K value reported for very similar compounds. Having thus selected what seemed to be the most consistent reported TFP values of the 1000 compounds in the data set, we calculated their NFP values with eq 2. By generalization of eq 5, we hypothesized that the NFP values could be correlated with boiling point numbers and functional group counts through eq 6. Here a and b are constants, Gi is a functional group-specific contribution to the value of NFP, and ni is the number of each such functional groups in the structure.
∑i ni Gi
ð6Þ
Using the multiparametric linear regression function of the statistical software package JMP,22 we then determined the value of each parameter that produced the best overall correlation of reported and predicted NFP values. The results are shown in Table 1, which lists the functional groups included in the study, the instances of each in the data set, the values of the constants a and b, the values of the Gi, and the standard error for each parameter.23 Using NFP values calculated via eq 6 to predict TFP values with eq 4 produced the correlation between reported and predicted flash points shown in Figure 3.24 The value of R2 is 0.993, and the standard error is 3.38 K. The AAD for the 1000 compounds is less than 2.5 K, which is less than 0.8% average absolute error.
’ DISCUSSION The results obtained in this study compare quite favorably with those of other flash point prediction methods. A QSPR method based on computed properties and the number of triple bonds in a structure gave an AAD of 13.9 K, while a neural network correlation based on boiling point and computed parameters gave an AAD of 12.6 K.14b Two methods based on molecular connectivity patterns and functional group counts gave AAD values of 10.3 and 10.2 K, respectively,11,12 while a method based only on functional group counts gave an AAD of 9.8 K.10 A method requiring both boiling points and
ni
value
std error
a
0.095
b
0.974
0.004
241
0.147
0.039
G1
0.096
carboncarbon triple bond
G2
42
0.780
0.093
benzene or pyridine ring
G3
173
0.830
0.063
ester
G4
138
2.352
0.060
ether
G5
121
0.855
0.048
aldehyde or ketone carbonyl dialkyl carbonate
G6 G7
98 6
1.865 3.589
0.069 0.248
alcohol OH
G8
102
4.735
0.076
phenol OH
G9
15
3.272
0.169
carboxylic acid
G10
18
6.249
0.148
carboxylic acid anhydride
G11
9
3.666
0.207
primary or secondary
G12
52
2.032
0.091
tertiary aliphatic amine aniline or pyridine nitrogen
G13 G14
20 23
1.217 2.676
0.141 0.139
secondary amide
G15
4
6.900
0.305
tertiary amide
G16
6
4.487
0.249
nitrile
G17
24
1.961
0.122
thiol
G18
26
1.312
0.118
dialkyl sulfide
G19
22
0.846
0.133
aryl fluoride
G20
8
0.767
0.221
aryl or alkyl chloride aryl or alkyl bromide
G21 G22
63 21
1.533 2.915
0.067 0.136
aryl or alkyl iodide
G23
16
2.884
0.155
dialkyl disulfide
G24
7
1.807
0.231
nitro group
G25
10
3.367
0.194
intramolecular OH 3 3 3 O hydrogen bond
G26
23
1.737
0.155
other intramolecular
G27
10
0.773
0.204
aliphatic amine
21
NFP ¼ a þ bYBP þ
parameter
hydrogen bonda a
Including OH 3 3 3 N, NH 3 3 3 O, and NH 3 3 3 N hydrogen bonds.
experimental specific gravity values produced a correlation with an AAD of 8.3 K.6b A neural network method based on functional group counts gave an average absolute error of 8.1 K.9 The best previously reported correlation had an AAD greater than 3 K, but the procedure requires knowing not only the boiling point of a compound and the number of carbon atoms it contains but also its enthalpy of vaporization.8 Thus, the method reported here is more accurate than previously reported methods for prediction of the flash points of organic compounds. In addition, the present method is very simple to use because it requires only molecular structure—the most fundamental form of knowledge about an organic compound—and boiling point—the most widely reported and generally reliable physical property of an organic liquid. One value of successful empirical correlations is that they can suggest experimental data that might be erroneous. For example, the two reported values of the flash point of 1-tridecene, 352 and 365 K, both give NFP values that are inconsistent with the linear trend exhibited by the NFP values of the other 1-alkenes in Figure 1, so the results of this study suggest that it could be worthwhile to redetermine TFP for this compound.25 Similarly, 4798
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Figure 3. Correlation between literature flash points of 1000 diverse organic compounds and those predicted from structure and boiling point via eq 6. The diagonal line represents perfect correlation between literature and predicted values, and the size of the data points indicates the standard error in the correlation.
the correlation of NFP values with the number of carbons in the 2-alkanols (Figure 2) suggests that the TFP values reported for 2-decanol may also be in error. In some cases we do not have data for a sufficient number of homologous compounds for this kind of analysis, but we suspect that a reported flash point may be in error because it is inconsistent with the predicted value even though the predicted values for a group of positional isomers are quite accurate. As noted above, we used 299 K as the flash point of 2,6-dimethylheptane, since this reported value seemed more consistent than did the other reported value (326 K) with the 288 K TFP value reported for six isomeric dimethylheptanes. Those six isomers gave excellent correlations between reported and predicted flash points (AAD = 2.1 K), but 2,6-dimethylheptane gave a predicted TFP value of 289 K and a deviation of 10 K. For this reason, we recommend that its flash point also be redetermined. Although the Gi values in Table 1 are purely empirical, such parameters often can provide clues to underlying physical phenomena. The positive values of G1G25 indicate that incorporation of any of these functional groups into a structure will raise the flash point of the compound. It is important to recognize that the effect of a functional group on boiling point is accounted for through the YBP value of the substituted compound. Therefore, the Gi values indicate specific effects on combustibility relative to an aliphatic segment contributing the same incremental value to YBP. Furthermore, specific bonding patterns, and not just replacement of hydrocarbon segments with less combustible groups, appear to influence flash points. For example, the value of G10 for a carboxylic acid is much larger than G4 for an ester, even though they are the stoichiometric equivalent in the combustion of isomeric compounds. It seems reasonable that the Gi values in Table 1 may be related to the radical chain mechanisms involved in combustion.26 One way a functional group could influence flammability is by altering the polarity and thus the radical reactivity of nearby CH bonds.27 Particular functional groups may also interact directly
CORRELATION
with radical intermediates or may fragment thermally to produce functional group-specific radicals that alter combustion. For example, the presence of bromine, iodine, or sulfur atoms in a structure could lead to less reactive radical intermediates that inhibit chain reaction combustion processes. This could explain why Gi values for bromine and iodine are much larger than the corresponding values for chlorine and fluorine and why the value of G24 for a disulfide is about twice the value of G19 for a thioether. In contrast to the positive values of G1G25, the values of G26 and G27 are negative. That is, flash points of organic compounds appear to be lowered by intramolecular hydrogen bonding, and this effect is entirely separate from any influence of hydrogen bonding on boiling point (which is accounted for in the YBP value). This result is reasonable if intramolecular hydrogen bonding alters the electrostatic potential of the molecule near the hydrogen bond.28 This analysis of Gi values points to an important conceptual advantage of flash point prediction methods that are based on functional group counts. Alternative methods that consider only connectivities or computed electronic properties of reactants are unlikely to account fully for the specific chemical effects of reactive intermediates produced by different functional groups during combustion. On the other hand, a method based on functional group counts can more adequately capture the various effects of different structural units throughout the combustion process. As noted above, there are inherent limitations to any flash point prediction method, and the present procedure is no exception. First, there must be a sufficient number of reliable TFP values for compounds incorporating a particular functional group before the value of Gi for that group can be determined.29,30 Second, the method reported here generally assumes that each functional group exerts an independent influence on the value of NFP. This seems likely to be the case if the functional groups in a multifunctional compound are well separated on a molecular skeleton, but it may not be true if the functional groups are close enough to interact electronically or sterically with each other. In fact, the G26 and G27 parameters reflect just this type of interaction for compounds capable of intramolecular hydrogen bonding. More subtle effects, such as those arising from conjugation of unsaturated groups or the close proximity of polar bonds, could also be significant. As an alternative to identifying a specific Gi value for each of the many possible group interactions, the present method could be extended by calculating NFP values using both functional group counts and selected computed properties of the reactants.
’ CONCLUSIONS The method for predicting the flash points of organic compounds with NFP values presented here not only is very simple but also is more accurate than other reported methods. The Gi values may provide insight into the combustion of compounds with different functional groups. Furthermore, the use of flash point numbers provides a new way to evaluate the reliability of reported flash point data. Redetermination of the flash points of compounds whose reported TFP values disagree with those predicted using the method presented here could lead to a refinement of the Gi values in eq 6 and therefore to even more accurate prediction of the flash points of organic compounds. 4799
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’ ASSOCIATED CONTENT
bS
Supporting Information. Data set of 1000 compounds along with their literature boiling points and references, YBP values, reported flash points and references, counts of the structural parameters used in eq 6, and predicted TFP values. This material is available free of charge via the Internet at http:// pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Tel.: 704-894-2544. Fax: 704-894-2709. E-mail: fecarroll@ davidson.edu.
’ ACKNOWLEDGMENT Financial and fellowship support from Davidson College and from Conselho Nacional de Desenvolvimento Científico e Tecnologico are gratefully acknowledged. F.H.Q. is affiliated with the Brazilian National Institute for Catalysis in Molecular and Nanostructured Systems (INCT-Catalysis). ’ REFERENCES (1) Zalosh, R. G. Industrial Fire Protection Engineering; John Wiley & Sons Ltd.: West Sussex, 2003. (2) Jones, J. C. Hydrocarbon Process Safety; Whittles Publishing: Caithness, Scotland, 2003. (3) Higman, C.; van der Burgt, M. Gasification, 2nd ed.; Elsevier: Oxford, 2008. (4) Patil, G. S. Estimation of Flash Point. Fire Mat. 1988, 12, 127. (5) Satyanarayana, K.; Rao, P. G. Improved Equation to Estimate Flash Points of Organic Compounds. J. Haz. Mat. 1992, 32, 81; also see the comment about this method in ref 17. (6) (a) Satyanarayana, K.; Kakati, M. C. Correlation of Flash Points. Fire Mat. 1991, 15, 97. (b) Metcalfe, E.; Metacalfe, A. E. M. On the Correlation of Flash Points. Fire Mat. 1992, 16, 153. (7) Mack, E.; Boord, C. E.; Barham, H. N. Calculation of Flash Points for Pure Organic Substances. Ind. Eng. Chem. 1923, 15, 963. (8) Catoire, L.; Naudet, V. A Unique Equation to Estimate Flash Points of Selected Pure Liquids. Application to the Correction of Probably Erroneous Flash Point Values. J. Phys. Chem. Ref. Data 2004, 33, 1083; this paper reported an AAD of about 3 K. By combining data from several tables in the paper, we calculated the AAD to be 3.38 K. (9) Gharagheizi, F.; Alamdari, R. F.; Angaji, M. T. A New Neural Network Group Contribution Method for Estimation of Flash Point Temperature of Pure Components. Energy Fuels 2008, 22, 1628. (10) Rowley, J. R.; Rowley, R. L.; Wilding, W. V. Estimation of the Flash Point of Pure Organic Chemicals from Structural Contributions. Proc. Safety Prog. 2010, 29, 353. (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. (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. (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. (14) (a) Katritzky, A. R.; Petrukhin, R.; Jain, R.; Karelson, M. QSPR Analysis of Flash Points. J. Chem. Inf. Comput. Sci. 2001, 41, 1521. (b) Katritzky, A. R.; Stoyanova-Slavova, I. B.; Dobchev, D. A.; Karelson, M. QSPR Analysis of Flash Points: An Update. J. Mol. Graphics Model 2007, 26, 529. (15) This work was an extension of a method originally proposed by Kinney, C. R. A System of Correlating Molecular Structure of Organic
CORRELATION
Compounds with their Boiling Points. I. Aliphatic Boiling Point Numbers. J. Am. Chem. Soc. 1938, 60, 3032. (16) 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. (17) Carroll, F. A.; Lin, C.-Y.; Quina, F. H. Calculating Flash Point Numbers from Molecular Structure: An Improved Method for Predicting the Flash Points of Acyclic Alkanes. Energy Fuels 2010, 24, 392. (18) Carroll, F. A.; Lin, C.-Y.; Quina, F. H. Improved Prediction of Hydrocarbon Flash Points from Boiling Point Data. Energy Fuels 2010, 24, 4854. (19) Because of uncertainty about the accuracy of the data, as discussed here, we rounded all reported boiling points and flash points to the nearest kelvin for this study. A listing of the compounds, along with their TB and TFP values, is provided in the Supporting Information. (20) The following sources of flash point data were checked for each of the 1000 compounds: (a) The Online Chemical Database; Department of chemistry, University of Akron; http://ull.chemistry.uakron. edu/erd/. (b) http://www.sigmaaldrich.com. (c) http://www.alfa.com. (d) http://www.acros.com. (e) http://www.lookchem.com. For some compounds, other sources were also consulted, and these are indicated in the Supporting Information. (21) The six isomers with TFP values of 288 K are 2,3-dimethylheptane, 2,4-dimethylheptane, 2,5-dimethylheptane, 3,4-dimethylheptane, 3,5-dimethylheptane, and 4,4-dimethylheptane. References are given in the Supporting Information. (22) JMP, version 7.0.2; SAS Institute Inc.: Cary, NC, 2007. (23) Details of the functional group counts for each compound are provided in the Supporting Information. (24) A sample calculation is provided in the Supporting Information. (25) Based on the NFP value calculated via eq 6, 360 K might be a more accurate value. (26) Glassman, I.; Yetter, R. A. Combustion, 4th ed.; Academic Press: Burlington, MA, 2008. (27) For example, see (a) El-Taher, S. Ab Initio Study of Reactions of Hydroxyl Radicals with Chloro- and Fluoro-Substituted Methanes. Int. J. Quantum Chem. 2001, 84, 426. (b) Shestakov, A. F.; Denisov, E. T.; Emel’yanova, N. S.; Denisova, T. G. The Energy and Geometric Characteristics of the Transition State in Reactions of RO2 3 with Carbonyl Compound CH Bonds. Rus. J. Phys. Chem. A 2009, 83, 361. (c) Khursan, S. L.; Semes’ko, D. G.; Teregulova, A. N.; Safiullin, R. L. Analysis of the Reactivities of Organic Compounds in Hydrogen Atom Abstraction from Their CH Bonds by the Sulfate Radical Anion SO4 3 . Kinet. Katal. 2008, 49, 202. (28) A change in the electrostatic potential surface near hydrogen due to intramolecular hydrogen bonding in hydroxyacetone was reported in the Supporting Information for Niederer, C.; Goss, K.-U. Quantum Chemical Modeling of Humic Acid/Air Equilibrium Partitioning of Organic Vapors. Environ. Sci. Technol. 2007, 41, 3646. (29) For this reason, the data set includes aryl fluorides but not alkyl fluorides. (30) Furthermore, additional experimental data would help refine the Gi values of functional groups in the present study.
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