Hygroscopicity of Organic Compounds as a Function of Carbon Chain

Article

Hygroscopicity of Organic Compounds as a Function of Carbon Chain Length, Carboxyl, Hydroperoxy, and Carbonyl Functional Groups Sarah Suda Petters, Demetrios Pagonis, Megan Suzanne Claflin, Ezra J. T. Levin, Markus D. Petters, Paul J. Ziemann, and Sonia M. Kreidenweis J. Phys. Chem. A, Just Accepted Manuscript • DOI: 10.1021/acs.jpca.7b04114 • Publication Date (Web): 16 Jun 2017 Downloaded from http://pubs.acs.org on June 19, 2017

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The Journal of Physical Chemistry

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Hygroscopicity of Organic Compounds as a

2

Function of Carbon Chain Length, Carboxyl,

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Hydroperoxy, and Carbonyl Functional Groups

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Sarah Suda Petters1,†,*, Demetrios Pagonis2, Megan S. Claflin2, Ezra J. T. Levin1, Markus D.

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Petters3, Paul J. Ziemann2, and Sonia M. Kreidenweis1

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1

Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado 80523-

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1371, United States 2

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Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder,

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Colorado 80309-0216, United States 3

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Department of Marine Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina 27695-8208, United States

Draft for submission to the Journal of Physical Chemistry A: June 13, 2017.

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ACS Paragon Plus Environment


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Abstract. The albedo and microphysical properties of clouds are controlled in part by the

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hygroscopicity of particles serving as cloud condensation nuclei (CCN). Hygroscopicity of

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complex organic mixtures in the atmosphere varies widely and remains challenging to predict.

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Here we present new measurements characterizing the CCN activity of pure compounds in

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which carbon chain length and the number of hydroperoxy, carboxyl, and carbonyl functional

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groups were systematically varied to establish the contributions of these groups to organic

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aerosol apparent hygroscopicity. Apparent hygroscopicity decreased with carbon chain length

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and increased with polar functional groups in the order carboxyl > hydroperoxy > carbonyl.

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Activation diameters at different supersaturations deviated from the -3/2 slope in log-log space

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predicted by Köhler theory, suggesting that water solubility limits CCN activity of particles

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composed of weakly functionalized organic compounds. Results are compared to a functional

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group contribution model that predicts CCN activity of organic compounds. The model

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performed well for most compounds but under-predicted the CCN activity of hydroperoxy

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groups. New best-fit hydroperoxy group/water interaction parameters were derived from the

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available CCN data. These results may help improve estimates of the CCN activity of ambient

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organic aerosols from composition data.

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Introduction

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Atmospheric aerosols participate in the formation of clouds by serving as cloud condensation

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nuclei (CCN),1-2 affect the global radiative balance through direct and indirect effects,3-4 and can

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be detrimental to the wellbeing of plants and animals.5-6 Numerous organic compounds

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contribute to the aerosol mass in the atmosphere,7-8 many of which are formed through secondary

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processes. Secondary organic aerosol (SOA) forms when volatile organic compounds are

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oxidized in the atmosphere, yielding low-volatility compounds that can condense.9-10 Low-

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volatility compounds also form in aqueous phase reactions as dissolved organic compounds are

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oxidized in cloud droplets11-12 or wet aerosols.13-14 Due to the chemical complexity of SOA, the

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chemical evolution and properties of SOA in the atmosphere are often described by simplified

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metrics such as the elemental oxygen to carbon ratio (O:C),15-16 the fraction of mass spectral

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fragment at mass-to-charge ratio 44 (f44),17 the change in H:C ratio relative to the O:C ratio,18 or

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the average oxidation state of carbon.19 The O:C ratio and f44 are both good predictors of the

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hygroscopicity of the organic fraction of aerosols in the atmosphere,15-17 but the relationship with

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hygroscopicity is not linear and may vary greatly under different conditions,20-23 demonstrating

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the need to include more detailed information such as molecular structure.

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Many organic compounds present in SOA contribute to the hygroscopicity of the whole

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particle.24-25 The hygroscopicity of each compound depends on the length of the carbon chain

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and the number and type of attached functional groups.23 Aerosol water content can be predicted

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by chemical equilibrium models such as UNIFAC for arbitrary mixtures of organic functional

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groups in solution.26 UNIFAC predictions of aerosol properties are widely used in the aerosol

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community27-31 and also serve as the core component of a model we have developed to predict

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CCN activity from functional group composition and particle dry diameter30 (hereafter referred

3



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to as the U-CCN model). UNIFAC is semi-empirical and thus measurements are necessary to

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constrain predictions.28-29 Most single-component studies characterizing the CCN activity of

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organic aerosols have been limited to chemicals that are commercially available. Suitable

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datasets for fitting empirical UNIFAC interaction parameters remain limited due to the difficulty

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of synthesizing a large suite of compounds with targeted functional groups. Although the U-CCN

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model was previously validated against a database of published laboratory CCN measurements,

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that database had limited coverage for many functional groups that are important in atmospheric

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organic aerosols. This work aims to close the gap for carboxyl, hydroperoxy, and carbonyl

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functional groups, which are investigated by using organic synthesis to systematically vary the

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type and location of the functional group on the carbon backbone. The U-CCN model uses a

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UNIFAC core to predict water activity,26 a functional group prediction of molar volume,32 and a

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liquid-liquid phase equilibrium algorithm.33 These are combined with traditional Köhler theory

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to predict CCN activity. The efficacy by which individual functional groups promote CCN

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activity is strongly tied to the water activity and solubility/miscibility for highly dilute

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solutions,30 meaning that validation and tuning of UNIFAC group interaction parameters for the

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U-CCN model must be performed against actual CCN measurements. Therefore this work

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primarily focuses on measurements of CCN activity, expressed in terms of the compound’s

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hygroscopicity parameter, to improve the fidelity of the U-CCN model.

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Theoretical Here the hygroscopicity parameter κ is used together with Köhler theory to describe CCN

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activation (eq 1) and aerosol water uptake in sub-saturated relative humidity (eq 2):24,34 − , = max 1 − −

4


(1)

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− 1 , = − 1 −

73 74 75 76 77

(2)

where is the critical water vapor supersaturation at CCN activation, is the droplet diameter,

is the particle dry diameter, = 4 ⁄

is the Kelvin diameter, is the surface

tension of the air/solution interface, is the molar mass of pure water, is the universal gas constant, is temperature,

is the density of pure water, is the water vapor saturation ratio

(relative humidity expressed as a fraction), and = ⁄ is the diameter growth factor.

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Throughout this work is expressed as a percent: " = − 1 × 100%. By convention κ

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values are normalized to a standard state by using the surface tension of pure water and room

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temperature ( = 0.072 N m-1, = 298.15 K) in calculations. This κ is denoted as “apparent κ”

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because it subsumes surface tension and other physicochemical processes to express the

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observed CCN activation or water uptake. Specifying any two of the variables κ, " , or the

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variables κ, , will uniquely determine the third, facilitating intercomparison of values across

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different studies. Hereafter ‘hygroscopicity’ and ‘κ’ refer to apparent κ unless otherwise

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specified.

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Mismatch between κ derived from CCN measurements and κ derived from sub-saturated

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measurements has been observed35-36 but remains difficult to interpret. Contributing factors

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include reduced surface tension,36-37 solubility limitations,38 and particle irregularity and surface

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restructuring during measurements.39 To account for these factors we further differentiate

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between apparent κ and intrinsic κ, ,-./ , which can be expressed by fully dissolved compounds

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in the absence of surface tension reduction or particle irregularities during the measurement.40

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For a mixture of compounds of limited solubility, = ∑2 12 ,-./,2 32 , where 12 is the volume

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fraction of the ith component in the dry particle, ,-./,2 is the intrinsic κ of the ith component, and

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94 95 96 97 98

32 is the dissolved fraction of the solute. This fraction is found using 2 = 42 5 /57,2 , where

42 is the aqueous solubility of the ith component expressed as volume solute per volume water at

saturation, 5 is the volume of water, and 57,2 is the total dry volume of the ith component. The

fraction dissolved is then 32 = 2 for partial dissolution (2 < 1) and 32 = 1 for

complete dissolution (2 ≥ 1). For pure compounds, κ is given by = 3,-./

99 100

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(3)

Based on eq 3, for dissolved compounds (3 = 1), measurements of log" as a function of

varying log follow κ isolines with a slope of ~–3/2. For compounds with limited solubility

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(3 < 1), the measurements will exhibit off-isoline behavior at the onset of solubility

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limitation, when the particles are not fully dissolved at the point of activation.34,41-43

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CCN activity was modeled using the open-source U-CCN model of Petters et al.30 In this study

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improvements to the UNIFAC core (Fredenslund et al.26 eq 1–7 with the parameters table given

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by Petters et al.30 containing values from Hansen et al.,44 Raatikainen and Laaksonen,28 and

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Compernolle et al.29) are investigated for the hydroperoxide functional group. We specifically

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seek interactions parameters that are optimal for CCN predictions only. As we have shown

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previously,30,45 the transition from CCN inactive (κ ~ 0) to CCN active (κ consistent with

109

predictions based on molar volume) requires the removal of solubility/miscibility limitations.

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Thus the prediction for compounds with solubility limitations is highly sensitive to the water

111

activity and liquid-liquid phase separation prediction at water activities very near unity. Three

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approaches were taken: (1) original parameters were used (“default approach”); (2) the

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CH(OOH) subgroup was replaced with a CH(O) and an OH group (“replacement

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approach”);29,46-47 and (3) the CH(OOH) parameters were adjusted based on an exhaustive grid

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search (“adjusted approach”). A brute force grid search was performed seeking a new pair of

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UNIFAC interaction parameters26 (>?→A ,>A→? ) for hydroperoxide (m) and water (n). This

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procedure produced a line in parameter space for which the mean error was ~0. These values are

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summarized in the Supporting Information (SI). Along this line, SSE asymptotically approached

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a low value. The variation of modeled apparent κ as a function of carbon chain length showed

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that some parameters result in unphysical behavior, and these parameters are discarded.

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Unphysical behavior is diagnosed by plotting simulated homologous series of functionalized n-

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alkanes.30 The physical basis for the unphysical regions is that, due to its reduced oxidation state,

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the water affinity of the hydroperoxide group should not exceed that of the hydroxyl group. The

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primary effects on CCN activity are molecular size and water solubility/miscibility and

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secondary effects include activity coefficients and surface tension; thus the U-CCN model

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captures the majority of the physical processes. Specifics about the procedure to constrain the

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parameter space to a physical solution are provided in the SI. Adjusted parameters were selected

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from within the set of reasonable parameters based on lowest available mean error and SSE.

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These parameters are >?→A = −667.3 and >A→? = 963.6. In discussion to follow, UNIFAC

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refers to the model with adjusted parameters unless otherwise specified. For the adjusted

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L parameters the sum square error (SSE): ∑logDE FGHI − logDE GJ7H/KH and the mean

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error: meanlogDE FGHI − logDE GJ7H/KH were both assessed. The modeled behavior of

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hypothetical C6–C35 hydroperoxides was evaluated.

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CCN activity was also estimated independently based on modeled molar volume and Flory-

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Huggins-Köhler theory, which assumes an athermal mixture of molecules of different sizes,

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considering only the entropy of mixing.48 Because it ignores chemical interactions, Flory-

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Huggins-Köhler theory describes the CCN activity for compounds that are completely dissolved

138

at compositions corresponding to water contents at the critical supersaturation.45,49 Flory-

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Huggins-Köhler theory has been shown to model the CCN activity of effectively soluble

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molecules with molecular weights ranging from 80 to 2000 Da.45

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Experimental

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Two types of organic compounds were synthesized for this study: compounds containing

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hydroperoxy groups and compounds containing carbonyl groups. Hydroperoxides included three

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isomers each of C10, C12, C14, and C16 hydroperoxyacids (alkoxy hydroperoxyalkanoic acids) and

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the same number of dihydroperoxides (dialkoxy dihydroperoxyalkanes). Carbonyls included a

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C16 dione, C18 diketoaldehyde, and a C19 ketoester. Hydroperoxides were synthesized in solution

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by ozonolysis of either an alkenoic acid or diene in the presence of excess alcohol.50-51 Details of

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the synthesis are reported in the SI and the reaction mechanism is reproduced in Scheme S1.

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Precursor compounds, targeted molecular structures, molecular weights, molar volumes

150

estimated from the U-CCN model, computer readable SMILES strings (simplified molecular

151

input line-entry system), and UNIFAC representations are summarized in Tables S2 and S3. The

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alcohols and alkenes were selected to determine the carbon chain length of the hydroperoxide

153

product as well as the placement of the functional groups. A total of 24 hydroperoxides were

154

synthesized.

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Synthesized hydroperoxides were purified using reverse-phase high-performance liquid

156

chromatography (HPLC) as described in previous work.23,25 Compound fractionation was

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achieved using an Agilent 1100 series HPLC equipped with an Agilent Zorbax Eclipse Plus

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XDB-C18 column (250 × 4.6 mm, 5 µm particle size) at room temperature and a Zorbax

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Reliance cartridge guard column (4.6 × 12.5 mm with 5 µm particle size) with a flow rate of 0.8

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mL min-1. A gradient elution method was employed starting with a 5:95 acetonitrile:water

161

mixture for 5 min, then ramping to 100:0 over 50 min and holding at this composition for 15

8


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min. The column eluent was routed directly to a Collison atomizer, where the solution was

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atomized, passed through silica gel and charcoal diffusion driers to remove the solvent, and

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residual hydroperoxide particles were routed to the Scanning Flow-CCN measurement setup

165

described below.

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Carbonyls were synthesized and purified using a method we have employed previously,52 and

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which is described in the SI. 1H and 13C NMR were used to verify the structure and purity of the

168

synthesized compounds. Molecular structures, SMILES strings, molecular weights, modeled

169

molar volumes from the U-CCN model, and UNIFAC representations are summarized in Table

170

S4. The purified carbonyls were dissolved in acetonitrile, atomized, dried, and the residual

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carbonyl particles were then routed to a Scanning Mobility Particle Sizer (SMPS)-CCN

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measurement and a Hygroscopicity Tandem Differential Mobility Analyzer (HTDMA), both of

173

which are described below.

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The CCN measurement setup had two modes of operation: Scanning Flow-CCN23,25,53 and

175

SMPS-CCN.54-55 Schematics of these previously described setups are provided in Figures S1 and

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S2 in the SI. Scanning Flow-CCN mode had a faster duty cycle and was used to capture eluting

177

HPLC peaks; SMPS-CCN mode had a larger dynamic range and was used for selected

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dihydroperoxides and for the already-purified carbonyls. In all cases aerosol was size-selected by

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a differential mobility analyzer (DMA; TSI 3071A) operated at 9:1.5 L min-1 sheath-to-sample

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flow ratio. The flow was split between a condensation particle counter (CPC; TSI 3010, 1

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L min-1) and a streamwise gradient continuous flow CCN counter56 (DMT, 0.5 L min-1). CCN

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instrument supersaturation was determined using size-selected ammonium sulfate aerosol and the

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Aerosol Inorganic Model57-58 to relate dry diameter and critical supersaturation.25,41,59 For

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Scanning Flow-CCN measurements, the diameter was held constant while the CCN flow was

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scanned linearly from ~0.3 to 1.0 L min-1 in 44 s, translating to a supersaturation scan of 0.2 to

186

1.1%. For SMPS-CCN measurements the DMA voltage was exponentially ramped from 9000 to

187

50 V in 220 s, translating to a diameter ramp from 330 to 20 nm.55,60 SMPS-CCN data were

188

inverted to correct for multiply charged particles as described previously.61 Supersaturation and

189

diameter at 50% activation were used to calculate apparent κ using eq 1.

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The HTDMA instrument has been described previously62-64 and measured the humidified

191

diameter of wet aerosols at a controlled relative humidity.65 A schematic of the setup is provided

192

in Figure S3 in the SI. Dry 100 nm particles were selected by a DMA (TSI 3080) before

193

humidification by passage through a Nafion membrane immersed in water. The water

194

temperature dictates the dewpoint of the sample,66 which is then passed to a thermally isolated

195

high-flow DMA67 whose temperature inhomogeneities are actively held within ±0.02 °C.62 The

196

instrument was operated in comparative hygroscopicity mode. In this mode the aerosol flow to

197

the instrument alternates between sample aerosol and ammonium sulfate. The ammonium sulfate

198

growth factor is used to determine the relative humidity inside the instrument.62-64 Two important

199

modifications were made to the HTDMA instrument. First, the high flow DMA was placed in a

200

secondary temperature controlled enclosure that was held at ~19 °C, in addition to the heat

201

exchangers mounted to the column.62 The slight cooling below room temperature (22–30 °C)

202

allowed for stable long-term humidification at a relative humidity of 99% while avoiding

203

condensation of vapor in lines outside the secondary enclosure. Second, the humidified size

204

distributions were characterized using 10 Hz data acquisition, scanning from 3000 to 50 V in 50

205

s using a sheath:sample flow ratio of 14.5:1. The fast scans were implemented to interface the

206

instrument with the HPLC system (data not shown here). The rapid scanning leads to smearing

207

of the peak. However, it was verified experimentally that the accuracy of the retrieved mode

10


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208

diameter was not affected by the scan rate. Due to limited instrument availability, only carbonyls

209

were measured using the HTDMA instrument. Relative humidities and growth factors were used

210

to calculate apparent κ using eq 2.

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Results and Discussion

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Figure 1 and Table S5 summarize results for the hygroscopicity of hydroperoxyacids.

213

Hygroscopicity decreased with increasing carbon chain length and ranged from κ = 0.1 to 0.03.

214

The data fall between the Flory-Huggins prediction, which over-predicted κ for some but not all

215

of the isomers, and the adjusted U-CCN model prediction, which greatly under-predicted κ. This

216

under-prediction is discussed below in the context of the U-CCN model adjustment. Molecules

217

with the hydroperoxy group near the end of the carbon chain tend to have larger κ and were

218

predicted better by Flory-Huggins-Köhler theory. Compounds that fall below the Flory-Huggins

219

prediction undergo solubility- (referring to solubility of crystalline solids) or miscibility-

220

(referring to liquid-liquid phase separation) controlled droplet activation and are expected to be

221

only sparingly soluble/miscible in water.

222 223

Figure 1. Hygroscopicity of hydroperoxyacids (alkoxy-hydroperoxyalkanoic

224

acids) as a function of carbon chain length. Selected molecules are pictured at

225

right. Each point is the average of several experiments and error bars indicate the

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226

min and max of all results. Colors indicate the length of the hydrophobic alkyl

227

tail. The black line shows the Flory-Huggins prediction and the purple line shows

228

the adjusted approach U-CCN model prediction. For Cn ≥ C11, the U-CCN

229

prediction is below the lower limit of the ordinate.

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Figure 2 and Table S6 summarize the hygroscopicity measurements of the dihydroperoxides.

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Hygroscopicity decreased with increasing carbon chain length and ranged from κ = 0.05 to

232

0.007. The rate of decrease is more rapid and reaches lower values than for the

233

hydroperoxyacids. All data fell below the Flory-Huggins prediction, suggesting that the

234

dihydroperoxides are sparingly soluble in water. The data are reasonably well described by the

235

adjusted U-CCN model prediction, although several isomers have κ values that are significantly

236

lower than the adjusted U-CCN model. With the exception of C10 compounds, the

237

dihydroperoxides with the COOH group positioned closer to the center of the molecule were

238

more hygroscopic, consistent with our previous results for similar dihydroperoxides.23

239 240

Figure 2. Hygroscopicity of dihydroperoxides (dialkoxy-dihydroperoxyalkanes)

241

as a function of carbon chain length. Each point is the average of several

242

experiments and error bars indicate the min and max of all results. Colors indicate

12


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243

the length of the hydrophobic alkyl tail. The black line shows the Flory-Huggins

244

prediction and the purple line shows the adjusted approach U-CCN model

245

prediction.

246

Although at present the observed isomeric differences evident in the data in Figures 1 and 2 are

247

within the uncertainty of the HPLC-CCN technique (i.e. the error bars overlap), it is known that

248

the position of organic functional groups can affect compound properties including the

249

hygroscopicity,68 solubility,69 vapor pressure,70-71 melting point,72-73 reaction mechanisms,74-76

250

viscosity,77 and glass-transition temperatures.78 The behavior of dihydroperoxides is broadly

251

consistent with the hypothesis that polar functional groups are more efficient in promoting

252

solubility if they are positioned near the center of the molecule.69 The hydroperoxyacids do not

253

show a strong correlation between the length of the hydrophobic alkyl tail and isomer

254

hygroscopicity. Competing effects may be at play. The hydrophobic tail may fold as solvating

255

water molecules attract polar functional groups to the solvation shell of the molecule, and since

256

folded isomers tend to have a reduced effective molecular size69 they may be more

257

hygroscopic.45,49 However, if the tail instead forms a hydrophobic branch it may disrupt the first

258

solvation layer of water molecules, reducing hygroscopicity. These effects are influenced by the

259

specific geometries of molecules, and standard UNIFAC is indifferent toward the location of

260

functional groups. Therefore, isomer effects cannot be resolved with the U-CCN model. Marcolli

261

and Peter68 modified the UNIFAC functional group definitions to separately account for

262

molecular alkyl tails, and although this approach is attractive, caveats include the need for more

263

fitted parameters and the absence of a large volume of observational data.

264

Figure 3 and Table S7 summarize the additional tests of hygroscopicity and supersaturation at

265

CCN activation of C10 and C12 dihydroperoxides as a function of particle dry diameter.

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Page 14 of 43

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Specifically, isomers #2.1 and #2.4 (Table S3) are shown (the C10 square and C12 triangle in

267

Figure 2). Each isomer was measured twice and differences between the first and the repeated

268

experiments are consistent with the ±10% variability in the supersaturation of the CCN

269

instrument. The relationship between critical supersaturation and dry diameter shows an

270

inflection point (panels A and C) where the C10 dihydroperoxide observation departs from the κ

271

isoline. No inflection point is observed for the C12-dihydroperoxide. The off-isoline behavior is

272

usually attributed to sparing solubility of the compound.34,41-43 The pure-compound κ model

273

including solubility34 (eq 3) describes the observations well in both cases. Based on the best fit of

274

eq 3 to the data, the solubility of the C10 compound was 4 = 0.31 with ,-./ = 0.042 and the

275

solubility of the C12 compound was 4 = 0.52 with ,-./ = 0.04. The solubility-controlled

276

transition in apparent κ (i.e. the inflection point) began for C10 dihydroperoxide particles near

277

= 150 nm (panel A). The C10 particles were not fully dissolved at the point of activation for

278

< 150 nm due to their solubility. The undissolved fraction increased at lower diameters and

279

could not contribute water uptake to the growing droplet. The C12 compounds did not exhibit

280

significant solubility limitations in the range measured.

281 282

Figure 3. Hygroscopicity of dihydroperoxides as a function of particle dry

283

diameter (A and B) and supersaturation at activation as a function of particle dry

14


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284

diameter (C and D). Panels A and C show the C10 dihydroperoxide (tail length 3)

285

and panels B and D show the C12 dihydroperoxide (tail length 4). Open symbols

286

indicate a repeated experiment. Each point is the average of ~3 experiments. Error

287

bars indicate the range of measured values. Grey lines of constant hygroscopicity

288

are shown in panels C and D. Black lines show the κ solubility model for pure

289

compounds (eq 3).34

290

The success of the pure-compound κ solubility framework in describing the observations in

291

Figure 3 is consistent with the near unity yields expected for the target hydroperoxides produced

292

in the synthesis of these standards.50,79-81 It is reassuring that these compounds behave as pure

293

compounds, given that for this set of solubility experiments the HPLC purification step was

294

skipped (due to its very low throughput). The HPLC-CCN measurements of the C10

295

dihydroperoxide isomers in Figure 2 were collected in a diameter range (123, 123, and 177 nm;

296

Table S6) close to the onset of solubility limitations shown in Figure 3 (panel A). Thus, it is

297

unsurprising that their apparent κ = 0.011 fell short of the fitted ,-./ = 0.042 in Figure 3. The

298

C12 dihydroperoxide apparent κ = 0.029 was nearer the fitted ,-./ = 0.04. Many sparingly

299

soluble compounds that should show inflection points do not in practice.34,42-43,82-83 Reasons for

300

this behavior frequently cited in the literature are solubilization of the analyte by trace impurities

301

present in the particle and uncertainties about the dried particle phase state.42,82

302

We now explore if effects other than solubility, such as reduced surface tension, viscosity, or

303

volatility, could contribute to the observed CCN data. Solubility, surface tension reduction,

304

viscosity, volatility and hygroscopicity are all linked to molecular structure. The surface tension

305

of C12 and C10 dihydroperoxides in aqueous solution is expected to lie between that of sodium

306

fatty acid salts and dicarboxylic acids (c.f. Figure 1 of Petters and Petters55), based on functional

15



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Page 16 of 43

307

group composition and carbon chain length. Thus, the surface tension reduction may affect CCN

308

activation. However, figure 3A shows a sharp reduction in κ at smaller diameters, contrary to

309

model predictions that show larger κ at smaller diameters regardless of the extent to which the

310

surfactant partitions to the surface.36,55,84 Facchini et al.85 proposed that surface tension reduction

311

during CCN activation greatly enhances the CCN activity of organic compounds. This prediction

312

was made using bulk measurements of surface tension from environmental samples. Bulk

313

measurements have also been used to predict strong CCN enhancement in more recent work

314

(e.g., Giordano et al.,86 Nozière et al.87). The problem with this approach is that hygroscopic

315

growth and CCN activation are controlled by both surface tension and water activity, and the

316

partitioning of surfactants to the surface of small droplets perturbs the water activity within the

317

droplet.88-90 Measurements of CCN activity for surfactant compounds have repeatedly shown that

318

strong CCN enhancement does not occur,55,90-91 and delayed CCN activation tests show that the

319

partitioning of surfactants is likely not time dependent within the measurement timescale.92

320

Measurement of surface tension is typically done in the bulk. Although measurements have been

321

recently extended to sizes as small as 5 microns, this is still much larger than the growing and

322

activating CCN droplet.93 The surface tension of droplets is undoubtedly perturbed by the solute;

323

however, few authors are willing to attribute their discrepancies entirely to surface tension

324

reduction.36-37 Several theories predicting surface partitioning in nanoscale droplets have been

325

developed89,94-96 and may be included in future versions of the U-CCN model.

326

Based on the molecular structures, the viscosities of the compounds in this study are well

327

below the 100 Pa·s delineation between liquid and semi-solid,97 making it unlikely that water

328

uptake was kinetically limited by water diffusion through the condensed phase organic matrix. It

329

is surprising, however, that the C10 compound exhibited a lower solubility than the C12

16


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330

compound, because the larger molecule would be expected to be less soluble due to the addition

331

of the hydrophobic group. One potential explanation is volatility. If the C10 particles partially

332

evaporated in the CCN instrument, a lower κ would be measured. The thermal gradient in the

333

CCN instrument is larger for larger supersaturation, and thus the decrease in κ with increasing

334

supersaturation might be also due to volatilization.98 Volatilization experiments that measure

335

aerosol diameter changes at ambient99-100 or chilled temperatures64 or with liquid water to inhibit

336

evaporation60,101-102 could probe this hypothesis.

337

Figure 4 shows the hygroscopicity of C14 hydroperoxide compounds as a function of the

338

number of carboxyl (panel A) or hydroperoxy (panel B) groups. Hygroscopicity increased with

339

the number of functional groups, consistent with previous results.23 Hydroperoxyacids measured

340

here (panel A) were slightly more CCN active (κ ~ 0.05) than previous results (κ ~ 0.02), but lie

341

within the experimental uncertainty shown in Figure 1. Note that only the isomer with a C5 tail

342

was measured in both studies. The observed slope d(κ)/d(carboxyl) was roughly equivalent to the

343

slope of the adjusted U-CCN model prediction. Current dihydroperoxide (panel B) observations

344

are consistent with previous results and range from κ = 0.007 to 0.03. The observed slope

345

d(κ)/d(hydroperoxy) was shallower than the U-CCN model prediction. The U-CCN prediction of

346

κ was consistent with observations for dihydroperoxides but under-predicted κ for the

347

hydroperoxyacids and monofunctional hydroperoxides. All compounds were less CCN active

348

than the Flory-Huggins model prediction.

17



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Page 18 of 43

349

350 351

Figure 4. Hygroscopicity of C14 hydroperoxides as a function of the number of

352

carboxyl (A) and hydroperoxy (B) groups. Data from Suda et al.23 are included

353

for comparison (open circles). Each point is the average of several experiments

354

and error bars indicate range. The length of the hydrophobic alkyl tail is indicated

355

with a number (Suda et al.23) or a color (this work). The black line shows the

356

Flory-Huggins prediction, the dotted grey line shows the observed slope, and the

357

purple line shows the adjusted U-CCN model.

358

Figure 5 and Table S8 summarize the hygroscopicity of carbonyls as a function of carbon

359

chain length. Hygroscopicity ranged from 0.0001 to 0.03, generally decreasing with carbon

360

number and increasing with the number of keto groups. Between compounds 1 and 2, the

361

addition of a keto group raises κ despite the addition of two carbons. Between compounds 2 and

362

3, the loss of a keto group renders compound 3 effectively CCN inactive. All observations fall

363

below the Flory-Huggins prediction, likely due to solubility limitations. The U-CCN model

364

prediction was κ ~ 0. Nevertheless, the increase in κ between compounds 1 and 2 shows that the

365

keto group promotes CCN activity. This trend is qualitatively consistent with predictions for

366

carbonyls of lower molar mass.30

18


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367 368

Figure 5. Hygroscopicity of carbonyls as a function of carbon chain length. All

369

compounds are pictured at right. Averages (horizontal lines) and individual

370

measurements (circles) are both shown. Sub-saturated measurements (using

371

HTDMA technique) are shown if they were above the detection limit (square).

372

The black line shows the Flory-Huggins prediction. The U-CCN model prediction

373

was κ ~ 0.

374

Figure 5 also shows HTDMA measurements of carbonyl compounds (summarized in Table

375

S9). The diameter growth factor, from eq 2, was 0.809, 1.007, and 0.877 for carbonyl

376

compounds 1, 2, and 3 at 98–99% relative humidity. The C18 diketoaldehyde showed

377

significantly lower κ for measurements taken at sub-saturated relative humidity than for those

378

taken at supersaturated relative humidity. Growth factors less than one could be indicative of

379

slight evaporation or surface restructuring upon humidification.39,103 The growth factor of the C18

380

diketoaldehyde corresponds to κ = 0.0006 at 98.7% relative humidity; compounds with negative

381

κ are not included because water uptake was below the detection limit.

382

Figure 6 shows the relationship between the measured and modeled CCN activity for

383

hydroperoxides using the three different model approaches. The under-prediction of κ using the

19



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Page 20 of 43

384

default approach is a prominent feature (panel A). The C12 hydroperoxyacids (all isomers;

385

labeled “2”) and C10 dihydroperoxides (all isomers; labeled “5”) have larger error because some

386

of the diameters corresponded to sizes where κ varies strongly due to miscibility limitations. In

387

panel B, the replacement approach performed well for the dihydroperoxides (green circles 5–8

388

and purple square 12) and under-predicted κ for other compounds (hydroperoxyacids and C14

389

hydroperoxides from previous work.23,30

390 391

Figure 6. Hydroperoxide hygroscopicity modeled by the U-CCN model as

392

compared with the observed hygroscopicity. Panel A shows the default approach;

393

Panel B shows the replacement approach; and Panels C and D show the adjusted

394

approach. Panel D uses different axis scaling. Error bars indicate the range of

395

observed values for different isomers (x-error) and the range of modeled values

396

for different dry diameters (y-error). Grey shading indicates where compounds are

397

inactive as CCN under typical stratus conditions. Blue triangles numbered 1–4

398

refer to C10, C12, C14, and C16 hydroperoxyacids (Tables S2 and S5); green circles

399

numbered 5–8 refer to C10, C12, C14, and C16 dihydroperoxides (Tables S3 and

400

S6); and purple squares numbered 9–12 refer to C14 hydroperoxides from

401

previous work.23,30

20


Page 21 of 43

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402

Predictions for the adjusted approach are superimposed in Figures 1, 2 and 4. These figures

403

show that the U-CCN model captured the absolute κ value and the relationship between κ and

404

carbon number well for the dihydroperoxides (Figure 2). In contrast, predictions are poor for the

405

hydroperoxyacids (Figure 1), and C14 monofunctional hydroperoxides (Figure 4). Figure 6 panels

406

C and D summarize the same data. The adjusted approach performed slightly better than the

407

replacement approach. The observed ranges in κ were generally larger than the modeled ranges

408

likely in part due to the different isomers measured, which UNIFAC cannot distinguish.

409

The closeness between the Flory-Huggins prediction and the data (Figure 1), suggests that the

410

hydroperoxyacids were sufficiently soluble, i.e. that the 3 term in eq 3 approaches unity.

411

This is clearly not captured by U-CCN model for this specific functional group pair. Potential

412

explanations for this behavior include strongly reduced surface tension, presence of trace

413

impurities, interactions between the hydroperoxide and acid groups that render the molecule

414

soluble but are not be captured in the functional group framework, or acid-water group

415

interaction parameters that are not tuned well enough to exactly model miscibility/solubility. At

416

this time the data generated for this work and the compounds analyzed in previous work23 are

417

insufficient to conclusively attribute the source of the error. Additional CCN studies targeting the

418

carboxyl group will be needed.

419

The grey regions in Figure 6 show κ values that are too low to be considered CCN active under

420

typical warm cloud (e.g., stratus) conditions. Compounds in these regions do not participate in

421

water uptake and contribute to CCN activity only by virtue of the effect of their dry volumes on

422

the overall diameter of a growing droplet. As compounds evolve by photochemical oxidation in

423

the atmosphere they generally become more hygroscopic,15-16 leaving the CCN-inactive region of

424

Figure 6. This solubility-controlled transition is aided by the addition of hygroscopic functional

21



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Page 22 of 43

425

groups to the carbon backbone23 and will be better estimated as more hygroscopicity

426

measurements for different functional groups become available.

427

To our knowledge, this and our previous work23 constitute the first measurements of the CCN

428

activity of pure organic hydroperoxides available in the literature. Compernolle et al.29 derived

429

hydroperoxide interaction parameters based on the SPARC (Sparc Performs Automated

430

Reasoning in Chemistry)104 model. These parameters have since been implemented in several

431

UNIFAC models that predict water uptake.30,47 Why does SPARC under-predict interactions

432

between hydroperoxides and water? The Compernolle parameters were intended for use in

433

predicting vapor-liquid equilibrium of α-pinene reaction products, and the authors point out that

434

as water uptake increases the accuracy of these interaction parameters for UNIFAC will decrease

435

because hydroperoxides participate in hydrogen bonding. The ability of polar organics to form

436

associated solutions or to be solvated by water generally complicates their treatment in models.

437

Multifunctional hydroperoxides are known to form intra- and inter-molecular hydrogen

438

bonds.48,105-107 For example, hydrogen bonding can draw the molecules closer together or

439

contract them, leading to misestimated molar volume in solutions.32,108-109 Similarly,

440

hydroperoxides can form dimers or larger clusters via hydrogen bonds,48,106-107 thereby acting as

441

larger molecules in solution.48 These complex interactions may contribute to the difficulty of

442

uniquely representing hydroperoxides within the UNIFAC modeling framework. Petters et al.30

443

used the U-CCN model with homologous series to quantify how effective functional groups are

444

in removing solubility limitations to CCN activation. These sensitivities can be expressed by

445

comparison to effect of changing carbon chain length (CHn). For example, to maintain constant

446

CCN activity, one hydroxyl group (promoting solubility) compensates for the addition of four

447

CHn groups (reducing solubility) and this is expressed as ∆PCHA S⁄∆POHS ~ − 4. The sensitivity

22


Page 23 of 43

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448

for

the

hydroperoxy

group

using

the

U-CCN

model

default

approach

is

449

∆PCHA S⁄∆PCHA OOHS ~ − 2. Conversely, the sensitivity for the hydroperoxy group using the

450

adjusted approach is ∆PCHA OOHS⁄∆PCHA S ~ − 4, similar to that of a hydroxyl group. This is

451

physically reasonable given that both functional groups form hydrogen bonds with water

452

molecules.

453

Conclusions

454

Twenty-seven compounds were synthesized to systematically investigate how the number and

455

location of functional groups affect CCN activity. Measurements of CCN activity were

456

performed

457

(hydroperoxyacids) dialkoxy-dihydroperoxyalkanes (dihydroperoxides), and carbonyls. For most

458

compounds the sample was purified with HPLC and the HPLC eluent was interfaced with

459

Scanning Flow-CCN analysis to determine the CCN activity expressed as an apparent κ value.

for

three

main

compound

groups:

alkoxy-hydroperoxyalkanoic

acids

460

Hydroperoxyacids were more CCN active than dihydroperoxides of the same carbon chain

461

length. The keto functional group promoted CCN activity. CCN activity generally decreased

462

with increasing carbon chain length. Polar functional groups closer to the center of the molecule

463

were more effective in promoting CCN activity except for the hydroperoxyacids that did not

464

show this pattern. This may be due to folding of the hydrophobic alkane tail of the molecules,

465

which can simultaneously reduce molecular size (promoting hygroscopicity) and disrupt the first

466

solvation layer of water (reducing hygroscopicity).

467

Size

and

supersaturation-resolved

CCN

experiments

were

performed

with

two

468

dihydroperoxide compounds and showed that solubility limitations were likely responsible for

469

measured CCN activity below the Flory-Huggins prediction from molecular size. The C10

23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

470

dihydroperoxide appeared to have limited solubility at CCN activation for dry diameters less

471

than 150 nm, while the C12 dihydroperoxide did not.

Page 24 of 43

472

The data collected in this work were used to evaluate the performance of a group contribution

473

model that predicts CCN activity. This model combines a UNIFAC core, a molar volume

474

prediction, and a liquid-liquid phase separation algorithm with Köhler theory to model the

475

activation diameter for organic compounds. Improved hydroperoxide-water group interaction

476

parameters were derived. The adjusted parameters result in excellent agreement between the data

477

and the dihydroperoxide compounds. However, the adjusted parameter model still

478

underestimates CCN activity for the monfunctional hydroperoxides and hydroperoxyacids.

479

Potential explanations for this behavior include strongly reduced surface tension, trace impurities

480

that remove solubility or miscibility limitations, hydroperoxide-acid group interactions that

481

promote solubility but are not captured in the functional group framework, or acid-water group

482

interaction parameters that do not capture miscibility/solubility near the point of droplet

483

activation. Additional studies are required to better understand the cause of these discrepancies.

484

The net result of the adjusted parameters is that the hydroperoxide group is approximately as

485

effective as the hydroxyl group in removing solubility/miscibility limitations from otherwise

486

non-polar molecules. This similarity between OH and CHn(OOH) is unsurprising due to the

487

expected underlying hydrogen bonding driving solvation and dissolution processes of the

488

molecule.

24


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489 490


Supporting Information Synthesis of compounds; instrument schematics; synthesized compounds, experimental results,

491

and modeling results; and exhaustive grid search for adjusted parameters.

492

Corresponding Author

493

*Sarah Suda Petters ([email protected]), Colorado State University, Department of Atmospheric

494

Science, Fort Collins, Colorado 80523-1371, United States.

495

†

Present address: Department of Environmental Sciences and Engineering, University of North

496

Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7400, United States.

497

Acknowledgements

498

This research was funded by the United States Department of Energy, Office of Science grant

499

DE-SC 0012043. The authors thank Patrick Chuang for the use of the high flow DMA and

500

Timothy Wright for construction of the HTDMA enclosure and high-resolution scanning

501

software.

25



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(107) Yablonskii, O. P.; Belyaev, V. A.; Vinogradov, A. N., Association of hydrocarbon hydroperoxides. Russ. Chem. Rev. 1972, 41 (7), 565-573, doi 10.1070/RC1972v041n07ABEH002077.

844 845 846

(108) Barley, M. H.; Topping, D. O.; McFiggans, G., Critical assessment of liquid density estimation methods for multifunctional organic compounds and their use in atmospheric science. J. Phys. Chem. A 2013, 117 (16), 3428-3441, doi 10.1021/jp304547r.

847 848 849 850

(109) Kurtén, T.; Tiusanen, K.; Roldin, P.; Rissanen, M.; Luy, J.-N.; Boy, M.; Ehn, M.; Donahue, N., α-pinene autoxidation products may not have extremely low saturation vapor pressures despite high O:C ratios. J. Phys. Chem. A 2016, 120 (16), 2569-2582, doi 10.1021/acs.jpca.6b02196.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

851 852

TOC Graphic

36


Page 36 of 43

Hydroperoxyacids

Apparent κ (−)

OOH Page 37 The of Journal 43 of Physical Chemistry 1 1 O -1 10 3 OOH

3 O 1 5 2 OOH 5 O 3 4 tail length 5 ACS Paragon Plus Environment 6 1 2 3 4 5 6 7 8 7 10-2 10 12 14 16 8 Carbons (−)

O OH O OH O OH

9 10 11

Apparent κ (−)

10-1

Dihydroperoxides The Journal of Physical Chemistry Page 38 of 43 1

OOH

1

O

O HOO

1 HOO 2 2 2 O OOH 3 OOH O 4 3 O 3 HOO -2 5 10 6 tail length 7 ACS Paragon Plus Environment 1 2 3 4 5 6 8 10 12 14 16 9 Carbons (−) O

Apparent κ (−) PageA39 of 43 0.1

B

D

0. 0.1

1

ACS Paragon Plus Environment 0.1 100 200 300 50 100 200 300 50 Dd (nm) Dd (nm)

01

01

0.1

100 200 300 50 Dd (nm)

1

3 4

κ=

0.2 tail length

0.

0.5 0.4 0.3

-3

-3

C12

10

10

C10

1 0.05 2 0.04 3 0.03 4 0.02 5 6 7 0.01 8 50 9

sc (%) The Journal of Physical C 1 Chemistry

100 200 300 Dd (nm)

Apparent κ (−)

B 10-1 A The Journal of Physical Chemistry Page 40 of 43 C14

8 5 4

C14 4

-2 3 1 10 2 2 2 3 3 = tail length 3 -3 4 10 5 6 tail length ACS Paragon Plus Environment 3 4 5 7 9 7 -4 0 1 1 2 8 10 −C(=O)OH −C(OOH)−O− 9

O

2

O

O

O

1 10-2 1 2 3 4 -3 5 10 2 6 3 7 8 10-4 ACS Paragon Plus Environment 9 1 2 10 16 18 19 11 Carbons (−)

O

Apparent κ (−)

O

O

O

Carbonyls

10-141 Page The of Journal 43 of Physical Chemistry

3

Modeled κ (−)

1 A 10

B

9

12

-2

1 10-4 2 3 10-6 4 5 10-8 6 7 low 8 9

C The Journal of56Physical Chemistry 7 8

10

8 11

10

1

6

4 3

9

11

1 2 3 4

10

5 7 61 8 12

9 11

0.1

D

Page 425 of 43 12

6

7

2 43

1

8

-2

10

2

10-3

5 2 12

724

10-8 10-6 10-4 10-2 Observed κ (−)

1

ACS Paragon Plus Environment -8 10 10-6 10-4 10-2 1 10-8 10-6 10-4 10-2 1 Observed κ (−) Observed κ (−)

3 10 9

11

4

10-3 10-2 0.1 Observed κ (−)

New hygroscopicity measurements ➛➛ New interaction Page 43 The of Journal 43 of Physical Chemistry parameters

0.1

κ (-)

0

C10 12

-2

Before

log10 UNIFAC κ (−)

14 C18 1 16 0.01 2 C16 C10 12 14 16 3 After Carbonyls 4 -3 Hydroperoxyacids 10 Dihydroperoxides 5 ACS Paragon Plus Environment 6 C19 107-4 -4 -6 -8

24

0

-2 -4 -6 -8

low

-8

-6

-4

-2

0

log10 Measured κ (−)

Hygroscopicity of Organic Compounds as a Function of Carbon Chain

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