SUPPLEMENTARY INFORMATION S1. Preparation and stability of ... · Nanoemulsion stability...

Thermogelling, photocrosslinkable nanoemulsions for the formation of tunable microporous organohydrogels Matthew E. Helgeson1, Shannon E. Moran1, Harry Z. An1, and Patrick S. Doyle1* 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA USA 02139 SUPPLEMENTARY INFORMATION S1. Preparation and stability of oil-in-water nanoemulsions S1.1. Control of drop size distribution by high-pressure homogenization In order to assess the ability of high-pressure homogenization to produce oil-in-water nanoemulsions of controlled droplet size distribution, silicone oil-in-water nanoemulsions containing PEGDA and SDS were prepared under various homogenization conditions and analyzed by dynamic light scattering (DLS). In all cases, mother emulsions were prepared at a droplet volume fraction of φ = 0.33 with P = 0.33 PEGDA and Cs = 200 mM. Samples for DLS were then prepared by dilution of the mother emulsion to a concentration of φ = 0.002 in an aqueous solution containing P = 0.33 PEGDA. First, a silicone oil-in-water emulsion was prepared with a φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS composition. DLS was performed on the pre-emulsion (prepared as previously described). An arbitrary size distribution was fit to the data using the CONTIN algorithm [S.W. Provencher, Computer Physics Communications 1982, 27:229; ibid. 27:213-227] and the results are shown in Figure S1 (white bars). The pre-emulsion exhibits a broad, bimodal size distribution with droplet sizes ranging from approximately 0.5-1000 μm. This pre-emulsion homogenized at 15 kpsi for 16 passes (N=16). The resulting nanoemulsion was analyzed using DLS, and the measured size distribution (via CONTIN) is also shown in Figure S1 (grey bars). The homogenized nanoemulsion exhibits a significant reduction in overall droplet size, with a z-average diameter of <D> = 46 nm. The resulting droplet size distribution is monomodal and significantly narrower, exhibiting a z-average polydispersity of σ = 0.207.

101 102 103 104 105 1060.0

0.2

0.4

Pre-emulsion (N = 0) Nanoemulsion (N = 16)

freq

uenc

y (a

.u.)

Droplet diameter (nm)

Figure S1. Droplet size distributions measured for silicone oil-in-water nanoemulsions with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS. Data are shown for the pre-emulsion (N=0) and a nanoemulsion homogenized at 15 kpsi for N=16.

Mesoporous organohydrogels from thermogelling photocrosslinkable nanoemulsions

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In order to more broadly probe the effect of homogenization conditions on nanoemulsion formation, we studied the dependence of the z-average droplet diameter, <D>, and polydispersity, σ, as a function of N, the number of passes through the homogenizer, and Δp, the homogenization pressure, for silicone oil-in-water nanoemulsions containing the same compsition as above. Nanoemulsions were subsequently diluted and analyzed using DLS via Cumulant analysis to obtain <D> and σ. The results for nanoemulsions with varying N at Δp = 15 kpsi are shown in Figure S2 (note that error bars correspond to the variance of the size distribution, <D>σ). The effective droplet diameter initially decreases dramatically, exhibiting a milder decrease after N = 2. The polydispersity similarly decreases dramatically until N = 8. We find that the data for N > 2 are in accord with the exponential relationship proposed by Mason and co-workers [K. Meleson et al., Soft Materials 2004, 2(2-3):109] (solid line in Figure S2).

0 4 8 12 160

100

200

300

400

Z-av

erag

e di

amet

er, <

D>

(nm

)

N

<D> / <D>∞ ∝ exp(-N)

Figure S2. Evolution of average droplet diameter with increasing number of passes, N, through the homogenization device for a silicone oil-in-water nanoemulsion with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS at a homogenization pressure of 15 kpsi. Line gives fit to the exponential relationship suggested by Mason and co-workers [K. Meleson et al., Soft Materials 2004, 2(2-3):109].

Similar measurements were performed on a series of nanoemulsions to determine the impact of Δp on nanoemulsion droplet size. Emulsions of the same composition as the above were prepared and separately homogenized at a range of Δp for N = 16 passes. The results are plotted in Figure S3. The effective droplet diameter and polydispersity decrease significantly over the range of Δp = 5-22.5 kpsi. Furthermore, we find that the droplet diameter follows the power-law relationship proposed by Mason and co-workers [K. Meleson et al., Soft Materials 2004, 2(2-3):109], where <D> ~ Δpn. However, we find that the scaling exponent n = -0.9 obtained for silicone oil-in-water nanoemulsions containing PEGDA is different than the value of n = -0.6 observed for silicone oil-in-water nanoemulsions without PEGDA measured by Mason and co-workers [id.]. The origin of this different scaling behavior is presently unclear; however, it presumably arises from some combination of the following effects: (1) the difference in geometry of the homogenization devices between the two studies, (2) the effect of PEGDA on both the rheology of the fluid and (3) the effect of same on the interfacial tension of the nanoemulsion droplets.

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5 10 15 20 25 3010

100

Z-av

erag

e di

amet

er, <

D>

(nm

)

Δp (kpsi)

<D> ∝ Δp-0.9

Figure S3. Evolution of average droplet diameter with increasing homogenization pressure, Δp, through the homogenization device after N=16 for a silicone oil-in-water nanoemulsion with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS. Line gives fit to the power law relationship suggested by Mason and co-workers [K. Meleson et al., Soft Materials 2004, 2(2-3):109].

S1.2. Nanoemulsion stability To assess the stability of the nanoemulsions over long time periods, a silicone oil-in-water nanoemulsion was prepared with a composition of φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS and homogenized at 15kpsi for 16 passes. The nanoemulsion was split into two samples, one of which was stored at 4 °C and one which was stored at room temperature (23 °C). A time course of the average droplet diameter, <D>, and polydispersity, σ, for the different storage temperatures was then collected, as follows. Dilutions of the mother emulsions to φ = 0.002 in an aqueous solution with P = 0.33 PEGDA were made every 12 hours for the first 2 days and then every 2-3 days afterwards. Diluted samples were then characterized using DLS. The results of the time course measurements are plotted in Figure S4. For the sample stored at room temperature (red points), we observe a rapid initial increase in the average effective droplet diameter for the first 48 hours, followed by a more gradual increase in diameter. The cause of this initial increase is currently unknown, but is consistent with previous measurements of droplet size stability in other systems [Wooster et al., Langmuir 2008, 24(22):12758]. At longer times, we find that the average droplet diameter evolves according to that expected for Ostwald ripening in the absence of droplet coalescence (Figure S4 inset), <D>3 ~ ωt, where ω is the rate of Ostwald ripening due to oil transfer between droplets through solubility in the continuous phase [Wooster et al., Langmuir 2008, 24(22):12758]. This result indicates that droplet coalescence is significantly reduced for the nanoemulsions, presumably due to the large increase in Laplace pressure of the droplets once the size of the droplets is reduced to the nano-scale. The best-fit ripening rate for the sample stored at room temperature, ω=0.45±0.01nm3/min, is significantly smaller than that observed for hydrocarbon-based oils due to the significantly lower solubility of silicone oil.





0 4 8 12 1650

100

150

200

0 4 8 12 16

100

200

300

ω = 0.09±0.01 nm3/min

Ave

rage

dro

plet

vol

ume

(103 n

m3 )

time (days)

ω = 0.45±0.01 nm3 /min 4 °C

25 °C

Effe

ctiv

e di

amet

er (n

m)

time (days)

Figure S4. Nanoemulsion stability characterized by evolution of the effective hydrodynamic diameter of a silicone oil-in-water nanoemulsion with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS after homogenization at 15 kpsi for N = 16 passes. Data are shown for samples stored at the temperatures indicated. Inset shows volume representation of the data, which were fit to a linear growth model with a constant rate of Ostwald ripening, ω.

For the sample stored at 4 °C, no rapid initial increase in droplet diameter was observed. Furthermore, the increase in droplet size from 56 nm at 0 days to 61 nm at 15 days is considerably less than that observed for storage at room temperature, suggesting that the nanoemulsions can be stored under refrigeration for several weeks without any significant increase in droplet size distribution. Nevertheless, the small (but measurable) increase in drop size is consistent with Ostwald ripening (Figure S4 inset), however at a significantly lower rate of ω=0.09±0.01nm3/min, presumably due to the decrease in solubility of silicone oil at lower temperature. Finally, we note that, since the storage temperature of 4°C is below the Krafft temperature for SDS (~10 °C), crystallization of the surfactant was observed over long periods of time (> 20 days). However, this effect was found to have no influence on the measured nanoemulsion size distribution after melting of the SDS crystals at room temperature. S2. Effect of drop size on organohydrogel formation (and vice versa) S2.1. Dependence of nanoemulsion viscoelasticity on drop size In order to determine the dependence of nanoemulsion drop size distribution on the formation of organohydrogels, rheological characterization was performed on a series of nanoemulsions with average droplet diameters ranging from 30-250 nm. Nanoemulsion stocks for each droplet size were prepared as previously prescribed using different homogenization pressures to achieve a range of average droplet sizes (see Figure S3). The composition of the nanoemulsions was held fixed at φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS. The average droplet size and polydispersity were determined by dynamic light scattering. In all cases, the polydispersity was less than 35%.





100 101 102

102

103

104

32 nm 51 nm 102 nm 231 nm

G',

G"

(Pa)

ω (rad/s)

Figure S5. Nanoemulsion linear viscoelasticity (G’, closed symbols; G”, open symbols) for the average drop sizes, <D>, indicated for silicone oil-in-water nanoemulsions with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS at temperatures above the gel transition (see Figure 4a).

Rheological measurements were carried out according to the same thermal scan used to determine the gel temperature illustrated in Figure 1. Subsequently, the high-temperature linear viscoelasticity of the samples were measured (Figure S5). In general, we find that nanoemulsions having an average droplet diameter of less than 100 nm exhibit the same thermoreversible gelation reported for nanoemulsions with <D> ~ 40 nm (Figure 3). However, the gel temperature, Tgel, decreases with increasing droplet size (Figure 4a, bottom). In this regime of droplet size (<D> < 100 nm), consistently solid-like linear viscoelastic behavior is observed, in which G’ and G” are nearly independent of the applied frequency. Furthermore, the plateau modulus, Gp, is found to decrease systematically with increasing droplet size (Figure 4b, bottom). Significantly different behavior is observed for nanoemulsions with average droplet size greater than 100 nm. In this regime, samples appear turbid and exhibit a yield stress, such that samples require agitation in order to flow within the sample vial. This behavior is observed over the entire range of temperatures available, 5 °C to 80 °C (Figure 4a, bottom). Correspondingly, the linear viscoelasticity of nanoemulsions with <D> > 100 nm is markedly different from the solid-like behavior observed with smaller droplet sizes. Specifically, the viscoelastic moduli exhibit behavior that is qualitatively similar to that observed for concentrated emulsion pastes [P. Coussot, Soft Matter 2007, 3(5):528], in which G” exhibits a minimum in the accessible frequency range, with a corresponding inflection in G’ and an effective plateau modulus at this inflection that is orders of magnitude smaller than that for the solid-like organohydrogels (Figure 4b, bottom). Such qualitative behavior is consistent with that generally observed for soft colloidal





glasses [P. Coussot, Soft Matter 2007, 3(5):528], in which the observed minimum in G” corresponds with the characteristic β-relaxation of kinetically arrested droplets brought about by hydrodynamic interactions in effective “cages” of nearest neighbors. In summary, we conclude that a decrease in the average nanoemulsion droplet size below 100 nm results in a dramatic transition in rheological behavior from glassy, paste-like behavior with moderate viscoelasticity to solid-like organohydrogel behavior with significantly higher moduli. S2.2. Effect of thermoreversible gelation on drop size distribution In order to determine the effect of organohydrogel formation on the droplet size distribution, a nanoemulsion corresponding to the composition in Figure 1 (φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS) was prepared by homogenization at 15 kpsi for N = 10 passes. A sample of the resulting nanoemulsion was diluted according to the procedure described previously, and the droplet size distribution was measured by dynamic light scattering using three replicate measurements. The as-prepared nanoemulsion had a z-average droplet size of <D> = 50±1 nm and a polydispersity of σ = 0.22±0.2 nm, obtained using a Cumulant analysis of the measured autocorrelation function. An arbitrary droplet size distribution was also fit to the data using the CONTIN method, and the resulting average size distribution is shown in Figure S6 (red points). The measured droplet size distribution is consistent with the z-average droplet size, exhibiting a maximum near 50 nm.

101 102 1030

50

100

150 Before gelation:

<D> = 50 nm, σ = 0.22 After gelation:

<D> = 53 nm, σ = 0.24

freq

uenc

y (a

.u.)

Droplet diameter (nm)

Figure S6. Droplet size distributions measured via CONTIN analysis of dynamic light scattering data for silicone oil-in-water nanoemulsions with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS before (red) and after (blue) gelation and subsequent re-entry into the liquid state. Legend gives average diameter and polydispersity calculated using Cumulant analysis. Data represent three replicate measurements for each sample.

Subsequently, the mother nanoemulsion (with φ = 0.33) was heated at 50 °C for 30 minutes in order to induce gelation. The sample was then allowed to cool at 5 °C for an additional 30 minutes in order to revert the sample to the liquid state. After re-entry into the liquid state, a sample of the mother nanoemulsion was diluted and characterized by dynamic light scattering. The resulting sample had a z-average droplet size of <D> = 53±2 nm and a polydispersity of σ = 0.24±0.2 nm. A CONTIN analysis was again used to fit an arbitrary size distribution, and the result is plotted in Figure S6 (blue points). The resulting droplet size distribution is nearly identical to that of the as-prepared nanoemulsion. Indeed, a





student t-test comparing the results found that the two distributions are identical to within 95% confidence. This result shows that, over reasonably short time periods the process of thermoreversible gelation does not significantly alter the droplet size distribution. Moreover, any small increase in the average droplet size due to gelation could be explained by the increased rate of Ostwald ripening observed at elevated temperatures (see Figure S4). This is consistent with the hypothesized mechanism of colloidal gelation of spherical droplets, as opposed to other processes in which the droplets transform into some other morphology, such as may be observed in microemulsions [H. BaggerJorgensen et al., Langmuir 1997, 13(16):4204]. S3. Supplementary SANS measurements of continuous phase microstructure S3.1. Temperature-induced formation of PEGDA aggregates In order to assess the structure of PEGDA in the continuous phase, SANS measurements were performed on a sample containing 33 vol% PEGDA in D2O at several temperatures. The measured absolute scattering spectra are shown in Figure S7. Qualitatively, the data show a significant increase in scattering with increasing temperature, consistent with an increase in the effective size of PEGDA chains.

10-1

100

2x100

10 °C25 °C40 °C60 °C

I [cm

-1]

q [Å-1]

Figure S7. SANS spectra (points) for 33 vol% PEG700DA in D2O at the temperatures indicated. Points represent experimental data, and lines give fits to a model for polymer chains with excluded volume (see text).

The data were quantitatively analyzed using a model for polymer chains with excluded volume interactions [B. Hammouda, Advances in Polymer Science 1993, 106:87-133]. The scattered intensity I(q) is assumed to follow the form:





( ) ( )0 incI q I P q I= + , (S1) where I0 and Iinc are the coherent scattered intensity at zero angle and the incoherent background intensity, respectively. The latter was independently determined to be 0.197 cm-1 and independent of temperature by Porod analysis [S.R. Kline, Journal of Applied Crystallography 2006, 39:895]. The form factor, P(q), is given by

( )( )

( ) ( )( )/ 2 , ,2m

m mP q U q m U qU q

γ γ = − (S2)

where

( )2 2 2 21 26

gq RU q

m m = + +

(S3)

and

( )( ) ( ) 1

0,

U q x tx U q t e dtγ − −= . (S4)

The model fitting parameters are the effective radius of gyration, Rg, and the Porod exponent, m. Equations (S1-S4) were fit to the absolute scattering spectra using the NCNR Igor SANS package [S.R. Kline, Journal of Applied Crystallography 2006, 39:895], and the resulting best fits are shown as the solid lines in Figure S7. In all cases, the model fits are in excellent quantitative agreement with the measured scattering.

Table S1. SANS model parameters obtained for PEG700DA T (°C) I0 (cm-1) Rg (Å) m

2V * (cm3/g) Nagg

10 0.298±0.002 5.48±0.06 1.43±0.03 0.82146 0.92±0.03 25 0.422±0.002 6.88±0.03 1.42±0.02 0.83085 1.11±0.03 40 0.566±0.012 10.03±0.08 1.46±0.01 0.84774 1.46±0.07 60 1.212±0.008 16.2±0.09 1.46±0.01 0.88205 3.00±0.09

* Data collected for PEG700 in H2O by Cruz et al. [J. App. Polym. Sci. 2004, 91: 2685-2689]

The resulting model fit parameters I0, Rg, and m are listed for each of the temperatures measured in Table S1. We find that the power law exponent, m, changes negligibly with increasing temperature, suggesting that the solvent quality of the polymer remains relatively unchanged over the temperature range considered. The value of m~1.4 is similar to that expected for semi-flexible chains [B. Hammouda, Advances in Polymer Science (1993), 106:87-133]. The origin of this apparent flexibility of PEGDA in water is currently unknown. By contrast, we find that the effective radius of gyration increases significantly from 5.5 Å to 16.2 Å over the measured temperature range. We note that the measured Rg for PEGDA (Mn = 700 g/mol) of 6.9 Å at 25 °C is in fair quantitative agreement with that predicted by the empirical correlation for PEG in water at the same temperature by Kuga [J. Chromatography (1981), 206(3): 449-461], 7.1 Å (Figure S8). As such, the observed increase in Rg could possibly arise from two factors: (i) changes in solvent quality leading to changes in the conformation of individual chains, or (ii)





aggregation of PEGDA chains due to inter-chain interactions. That excluded volume parameter m remains unchanged with temperature suggests that the solvent quality of the polymer remains independent of temperature over this range. Furthermore, a significant increase in Rg would require water to become a better solvent at higher temperatures, which is contrary to the well-studied lower critical solution temperature (LCST) for PEG in water [E.E. Dormidontova, Macromulecules 2002, 35(3): 987]. Thus, we conclude that the observed increase in the apparent radius of gyration with increasing temperature results from aggregation of PEGDA chains. Previous SANS measurements of larger molecular weight PEGs with various end groups, including PEGOH and PEGDME, indicated network formation due to hydrogen bonding between PEG end groups [Ho et al., Journal of Polymer Science Part B-Polymer Physics (2003), 41(1): 135-138]. However, those systems exhibited significant low-q scattering due to mass fractal behavior of the resulting polymer network. Since no such scattering is observed in the current system, we conclude that the increase in Rg (as well as I0) in the PEGDA system results from the formation of discrete aggregates in solution. We subsequently use the measured values of I0 in order to estimate the degree of aggregation by

( )2

2 2 20

ˆagg

A

V MI N

Nρ φΔ

= (S5)

where Δρ is the neutron contrast between PEGDA and D2O (5.69×10-6 Å-2, calculated using the scattering length density calculator (http://www.ncnr.nist.gov/resources/sldcalc.html), 2V is the specific volume of PEGDA in D2O, φ2 is the volume fraction of PEGDA in solution (0.33), M2 is the molecular weight of an individual PEGDA molecule (700 g/mol), NA is Avagaadro’s number, and Nagg is the apparent aggregation number of PEGDA. The resulting estimates of Nagg are tabulated in Table S1 and plotted in Figure S8. At low temperatures, Nagg ~ 1, such that PEGDA exists as monomers in solution. However, as the temperature is increased, Nagg > 1, suggesting the formation of aggregates in solution. The value of Nagg > 3 at 60 °C suggests that PEGDA does not form cohesive micelles, such as those observed in associative polymers, but rather forms loose aggregates. As suggested by the independence of the excluded volume parameter with respect to temperature, these aggregates are mediated by interactions between the acrylic PEGDA end groups. Thus, the observed temperature dependence of PEGDA aggregation suggests that the acrylic end groups exhibit decreased solubility in water with increasing temperature.

This aggregation of PEGDA monomers with increasing concentration has practical implications for the structure and colloidal stability of the current nanoemulsion systems of interest. Specifically, the decreased solubility of the acrylic end groups with increasing temperature is consistent with the proposed mechanism of nanoemulsion gelation whereby an increase in temperature results in partitioning of PEGDA monomers at the oil-water interface, resulting in bridging between nanoemulsion droplets.





0 20 40 60 800

10

20

Rg (Å

)

Kuga

0 20 40 60 800

1

2

3

4

Nag

g

T (°C) Figure S8. Apparent radius of gyration (top), Rg, and aggregation number (bottom) from model fits in Figure S7 for 33 vol% PEG700DA in D2O versus temperature. Dotted line gives the predicted value of Rg for PEG700 estimated using the empirical correlation developed by Kuga [J. Chromatography 1981, 206(3): 449-461] for PEG in good solvent. Solid line is drawn to guide the eye.

S3.2. Effect of temperature on interdroplet interactions In order to determine the nature of the interdroplet interactions leading to nanoemulsion gelation, SANS measurements were performed on a silicone oil-in-D2O nanoemulsion with P = 0.33 PEGDA, and Cs = 200 mM SDS homogenized at 15 kpsi for 16 passes. DLS measurements on this sample indicate that the average drop size is approximately 40 nm. In order to measure the interdroplet interactions accurately, the sample was diluted from φ = 0.33 to 0.01 with a mixture of 33 vol% PEGDA in D2O. D2O was chosen as the solvent in order to enhance contrast between the droplets and the surrounding fluid. This also results in contributions to the measured scattering from PEGDA in the continuous phase. SANS spectra were measured at temperatures corresponding to those previously used for the measurement of PEGDA aggregation, and the results are plotted in Figure S9. The effect of elevated temperature results in significant changes in scattering at high-q values in the range where aggregation of PEGDA is observed (Figure S7), as well as at low q-values. The latter is presumably from changes in interdroplet interactions, and was the subject of further study.





10-2 10-1

100

101

102

103 Drop size 10 °C25 °C40 °C60 °C

I [cm

-1]

q [Å-1]

PEGDAaggregation

Interdropletattractions

Figure S9. SANS spectra (points) for silicone oil-in-water nanoemulsion with φ = 0.01, P = 0.33 PEGDA, and Cs = 200 mM SDS at the temperatures indicated. Lines give combined model fits for PEGDA aggregation (using the polymer with excluded volume model) and polydisperse spheres with square well interactions. Best fit model parameters are listed in Table S3.

The SANS data were modeled using a combined scattering model that takes into account both the presence of PEGDA as well as the presence of and interactions between nanoemulsion droplets (note that, because the structure of nanoemulsion droplets dominates the scattering, droplet-PEGDA interactions can be neglected), ( ) ( ) ( )emulsion PEGDAI q I q I q= + (S6) where Iemulsion(q) represents the contribution of the scattering arising from the droplets, IPEGDA(q) is the contribution arising from the polymer, and Iinc is the incoherent scattering arising from all components in the fluid. Consistent with this decomposition of the scattering, we simply assume that IPEGDA(q) is identical to eq. (S1) used to fit the scattering for PEGDA in the absence of oil and surfactant. We assume that the scattering from the emulsion droplets can be modeled as that for polydisperse spheres with uniform scattering length density and isotropic interactions. Assuming that the scattering of such a fluid can be modeled by the scattering of monodipserse spheres with average size, the contribution to the scattering from the droplets is thus given by

( ) ( ) ( )emulsion p incI q n P q S q I= + (S7)





where np is the number density of droplets and Iinc is the incoherent scattering arising from the oil and surfactant. The quantity ( )P q is the average form factor for polydisperse spheres given by

( )( ) ( )

( )0

0

f D P q dDP q

f D dD

∞

∞=

. (S8)

The form factor P(q) for uniform spheres is given by

( ) ( ) 213

drop

J qaP q

qaρ

= Δ

(S9)

where ( ) ( ) 2

1 sin cosJ x x x x x= − is the spherical Bessel function of the first kind and the neutron

contrast dropρΔ is the difference in scattering length density between a droplet and D2O. Furthermore, we assume Schultz polydispersity of the droplets, such that the diameter distribution function, f(D), is given by

( ) ( )( )

( )

21

exp 12 1

1z

DzDDf D z

D D z+

− +

= + Γ + (S10)

where D is the average droplet diameter, z is related to the standard deviation in diameter by

( )21Dz D σ= − and ( )1zΓ + is the Gamma function.

The average structure factor of the nanoemulsion droplets, ( )S q , is modeled using the convolution theorem,

( ) ( )( )

ˆˆ1

C qS q

C qρ=

− (S11)

where ( )C q is the direct Fourier transform of c(r), the direct correlation function, for which a suitable closure approximation must be chosen. Here, we assume short-range attractive interactions between droplets, for which the Percus-Yevick approximation is appropriate [S.R. Kline, Journal of Applied Crystallography 2006, 39:895]. Closure of the structure factor requires appropriate selection of a pair potential to represent inter-droplet interactions. Since we believe the interdroplet interactions to be governed by bridging of PEGDA chains, we choose the square well (SW) potential,





( )0

b

r Dr k T D r

rε λ

λ

∞ ≤Φ = < ≤ >

(S12)

where λ is the well width (λ > D ) and ε is the well depth. The SW potential has been shown to adequately capture the scattering from colloidal dispersions with interparticle interactions mediated by polymer-induced bridging interactions [F. Nettesheim, Langmuir 2008, 24: 7718-7726]. The combined model comprising eqs. (S6-S12) was fit to the data in Figure S9, as follows. For data at each temperature, the model fits for PEGDA from Figure S7 were assumed to be identical to IPEGDA(q). First, the data at 10 °C were fit assuming ε = 0 in order to obtain an approximate value of D = 360 Å. This approximate value was used to estimate the well width in the SW potential, which was held fixed for all temperatures studied. Assuming that the interactions are due to polymer bridging, the longest range of attraction is given by max D Lλ = + , where L is the fully extended end-to-end distance of a single

PEG700DA molecule. Assuming a C-C bond distance of 1.57 Å, we find λmax ≈ 1.47 D . This well width was held fixed for all further fitting. Subsequently, the data at 10 °C were fit to the combined model with φ, D , σD, Δρdrop, and ε as adjustable parameters. The best-fit values of these parameters are shown in Table S2, and the resulting model fit is shown in Figure S9. The resulting model fit is in excellent agreement with the measured SANS spectrum over the entire q-range. Furthermore, the quantitative accuracy of the model fit combining PEGDA and spherical droplet structure suggests that the presence of SDS, even at a significant concentration (200 mM in this case) does not contribute significantly to the measured scattering beyond its contribution to that from the droplets. This demonstrates that SDS does not participate in PEGDA self-assembly in the continuous phase.

Table S2. SANS model parameters obtained for dilute silicone oil-in-water nanoemulsions* T (°C) φ D (Å) σD Δρdrop (10-6 Å-2) Dλ bk Tε

10

0.0103±0.0001 357±1 0.297±0.002 4.96±0.16 1.47

0.33±0.0925 0.53±0.0940 1.07±0.0860 1.46±0.09

* Model parameters except for ε were fit to data at 10 °C and held constant for all other conditions.

The droplet volume fraction, φ, agrees well with that expected for the sample, and Δρdrop is similar to that expected for pure silicone oil, confirming the assumption of negligible contributions to the scattering from the surfactant. Furthermore, the average droplet diameter agrees well with DLS measurements for this sample, validating the use of spheres with Schultz polydispersity to model the droplet form factor. The resulting SW well depth of 0.33kbT suggests very mild attractions at low temperatures. Given that similar well depths have been measured for silica nanocolloids in the absence of bridging species [F. Nettesheim, Langmuir 2008, 24: 7718-7726], these mild effective attractions may simply reflect van der Waals interactions between droplets, such that polymer-mediated interdroplet attractions are negligible.





2 4 6 8 10 12 14102

103

0 20 40 600

1

2

ε / k

bT

T (°C)

10 °C 25 °C 40 °C 60 °C

I [cm

-1]

q [Å-1×103]

Figure S10. Magnification of SANS spectra in Figure S11 at low q-values. Inset plot shows the best-fit square well depth, ε, with increasing temperature.

The SANS spectra at the remaining temperatures were fit by leaving ε as an adjustable parameter with all other model parameters held fixed. The results of the model fitting are shown as the solid lines in Figure S9, and a magnifaction of the data at low-q (where the model is most sensitive to the interdroplet potential) is shown in Figure S10. The resulting values of ε at the various temperatures are listed in Table S2, and plotted in the inset of Figure S10. We find that the strength of attraction between droplets given by ε increases significantly from ε~0.3kbT at 10 °C to ε~1.5kbT at 60 °C. Considering the previously measured behavior of PEGDA in the continuous phase, we hypothesize that decreased solubility of the PEGDA acrylic groups at elevated temperatures leads to interfacial partitioning of PEGDA molecules which result in significant bridging attractions between droplets. Since the measured strength of attractions is greater than kbT, we conclude that it is these polymer-mediated bridging interactions which are responsible for gelation of the nanoemulsion at higher temperatures in more concentrated nanoemulsions. S4. Photopolymerization of crosslinkable nanoemulsions S4.1. Rheo-UV measurements In situ rheological measurements during UV photopolymerization of crosslinkable nanoemulsions were performed on a nanoemulsion containing φ=33% and P=33% with an average droplet diameter of <D> = 42 nm. An appropriate amount of DC1173 photoinitiator was added to the mother nanoemulsion to produce a nanoemulsion with I=1 vol% DC1173. Rheo-UV measurements were carried out as previously described. After sample loading, equilibration, and pre-shear, dynamic oscillatory measurements were used to monitor the linear viscoelasticity at an applied frequency of 10 rad/s and strain amplitude of 0.1%.





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Figure S11. Photocuring of a nanoemulsion with φ=33%, P=33% PEGDA, I=1% DC1173, and Cs = 200 mM SDS below (top) and above (bottom) thermal gelation. “UV” label indicates exposure of sample to 13 mW/cm2 UV irradiance for 10 s. Linear viscoelasticity was measured at a frequency of 10 rad/s and strain amplitude of 0.1%. Left axis (red): G’, right axis (black): temperature.

First, measurements were made under isothermal conditions at 15 °C (Figure S11, top), such that the nanoemulsion remains below the gel temeprature state over the entire course of measurement. During the measurement, the sample was exposed to UV light with a measured irradiance of 13 mW/cm2 in 10 s increments, with 60 s between consecutive exposures (black arrows). In order to ensure appropriate comparison between measurements, UV exposures were performed until maximum conversion of the photocrosslinking reaction was achieved. This was assumed to occur when a final UV exposure produced no discernable increase in G’ over a 120 s time period. The ultimate modulus of the gel after complete crosslinking was 7.7×105 Pa. Subsequently, the measurement was stopped, and the non-linear viscoelasticity of the sample was measured using large amplitude oscillatory shear (Figure 5d, blue symbols). Additional measurements were made on a sample equilibrated at 15 °C and subsequently gelled using a temperature ramp from 15 °C to 50 °C at a rate of 10 °C/min. During the temperature ramp, G’ begins to increase significantly at a temperature of 21 °C, and exhibits gelation at Tgel = 21 °C (note that the gelation temperature with photoinitiator is significantly less than the corresponding nanoemulsion without





photoinitiator) at which G’ = G” (G” not shown). Similar to the corresponding nanoemulsion without photoinitiator (Figure 1), the modulus exhibits a plateau with increasing temperature after which G’ does not increase significantly. Once the temperature was equilibrated at 50 °C for 30 seconds, the sample was UV irradiated in discrete exposures in the same manner as before, until the maximum conversion of photocrosslinking was achieved. The ultimate modulus of the gel after complete crosslinking was 5.6×105 Pa. Note that, although the organohydrogel has significantly higher elasticity than the nanoemulsion in the liquid state, the corresponding gel modulus after photocrosslinking is significantly lower. The origin of this change in modulus with state of the nanoemulsion upon photocrosslinking is currently unknown. Subsequently, the measurement was stopped, and the non-linear viscoelasticity of the sample was measured using large amplitude oscillatory shear (Figure 5d, red symbols). S4.2. Stop flow lithography In order to test the ability for photopatterned microgel particles to be produced from photocrosslinkable nanoemulsions in the liquid state, we performed stop flow lithography (SFL) of nanoemulsions containing φ=33%, P=33% PEGDA, I=1% DC1173, and Cs = 200 mM SDS. Two such nanoemulsions were prepared, one homogenized at 15 kpsi for N = 18, resulting in an average droplet diameter of <D> = 32 nm, and the other homogenized at 15 kpsi for N = 1, resulting in an average droplet diameter of <D> = 312 nm, as characterized by dynamic light scattering. We note that the nanoemulsion samples with <D> = 32 nm and 312 nm appeared transparent and opaque, respectively, under visual observation. SFL was carried out in the following manner (Figure S12a, excerpted from [Diao et al., Journal of the American Chemical Society 2011, 133(11): 3756-3759]). Microfluidic channels with straight, rectangular cross-section (width = 300 μm, height = 30 μm) were prepared by soft lithography. Briefly, polydimethylsiloxane (PDMS, Sylgard 184, Dow Corning) was poured on an SU-8 photoresist patterned silicon wafer and cured to create a bas-relief microchannel device. Channels with end reservoirs were cut from the wafer with a scalpel and inlet and outlet ports were punched into the device with a blunt syringe (Small Parts, Inc.) to introduce the hydrogel pre-cursors. A photomask featured with square shapes was designed using AUTOCAD and printed at 50,800 dpi by FineLine Imaging (Colorado Springs, CO). For SFL, the microfluidic device was placed on a translating stage inverted microscope. The inlet channel was loaded with a photocrosslinkable nanoemulsion using a pressure-controlled manifold. The mask was placed in the field-stop of the microscope and square features were projected to the pre-cursor by ultraviolet (UV) exposure from a Lumen 200 lamp (Prior) through a wide excitation UV filter set (11000v2: UV, Chroma) while the flow of pre-cursor was stopped. The ultimate feature sizes of the patterned squares were 30 μm x 30 μm, determined through fluorescence imaging of the microchannel during UV illumination. Pulses of UV exposure were obtained by a computer-aided UV shutter (UniBlitz). Incident UV intensities were measured using a UVA Power and Dose meter (ACCU-CAL-30 UVA, DYMAX). In all experiments, the measured exposure was 0.89 μW, and the UV exposure time was fixed at 200 msec.





Figure S12. (a) Schematic of the stop flow lithography process, reproduced with permission from Diao et al. [Journal of the American Chemical Society 2011, 133(11): 3756-3759]. (b) In situ phase contrast images during SFL depicting the production of microgel particles from nanoemulsions containing φ=33%, P=33% PEGDA, I=1% DC1173, and Cs = 200 mM SDS with (left) <D> = 32 nm and (right) <D> = 312 nm.

SFL was carried out for both the nanoemulsion samples with <D> = 32 nm and 312 nm. For the nanoemulsion with smaller average droplet size, SFL produced discrete microgel particles with a square shape that is successfully reproduced from the photolithographic pattern (Figure S12b, left image). We note the appearance of small, point-like inhomogeneities within the microgel interior, as well as a line-like defect which spans several adjacent particles. These defects are not due to nanoemulsion droplets, but are rather due to defects (such as bubbles or scratches) contained within the transparency film of the photomask, and are reproduced exactly regardless of the position in the sample where photolithography is performed. By contrast, SFL of the nanoemulsion containing relatively large droplets poorly reproduces the photolithographic pattern within the fluid, resulting in gels with ill-defined boundaries. Because the nanoemulsion sample is optically turbid, this result is presumably due to scattering of the photopatterned light by emulsion droplets within the field of view. In summary, we conclude that SFL of nanoemulsions with droplet sizes such that the process fluid is optically transparent produces can be used to synthesize complex microgel particles with complex shapes, similar to other photocrosslinkable fluids [Helgeson et al., Current Opinion in Colloid and Interface Science 2011, doi:10.1016/j.cocis.2011.01.005].

(b)

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S4.3. Production of hydrogel-nanoemulsion composite microgels for encapsulation and release Stop-flow lithography (SFL) was used to pattern multifunctional nanoemulsion-laden particles (φ = 0.33, P = 0.33, Cs = 100 mM, I = 0.02) by polymerizing tablet shapes across the interface of up to four co-flowing precursor streams. Neighboring precursor streams are labeled with either DiOC18 (10 µM) or PKH26 (5 µM) in an alternating manner. By adjusting the location of the interfaces using the flow rates of the stream, we can make particles that contain variable proportions of different chemistries. To demonstrate encapsulation and release of soluble proteins using composite microgel particles, we synthesized tablet-shaped particles (φ = 0.20, P = 0.33, Cs = 100 mM, I = 0.02) and incubated them (~1 particle/µL 100x TE buffer) with 2.5 µM Chol-PC-BT overnight using a temperature-controlled shaker plate (Biomega H5000-HC) at 45 ºC and 1500 rpm. The highly hydrophobic nature of the cholesterol group (Chol) allows biotin (BT) to irreversibly adsorb at the oil-water interface at elevated temperatures. Once loaded with Chol-PC-BT, the particles were washed four times with 100x TE buffer at 45 ºC and incubated with 3.5 µM streptavidin-Cy3 (SA-Cy3) fluorescent reporter at 45 ºC and 1500 rpm for 4 hours. To achieve effective release, a photocleavable spacer (PC) is inserted between the cholesterol and biotin, which allows the BT-SA-Cy3 complex to be trigger released upon UV irradiation. High resolution micrographs of the particles were taken at specific time points before and after 30 seconds of UV exposure. S5. Generalization of organohydrogels to multiple materials In order to generalize the observed phenomenon of thermoreversible organohydrogel formation in oil-in-water/PEGDA nanoemulsions, we attempted to create nanoemulsions comprised of other surfactant and oil systems. Specifically, in order to demonstrate the formation of nanoemulsion gels from biocompatible materials, we confined our studies to generally regarded as safe (GRAS) compounds, including fatty acids and fatty acid esters. Potassium oleate (KOlAc, Sigma Aldrich), a GRAS food additive compound, was chosen as a model biocompatible surfactant due to its similarity in emulsifying properties to SDS. Nanoemulsions with compositions ranging from φ = 0.25-0.33 PDMS oil, P = 0.33 PEGDA, and Cs = 100-200 mM were prepared according to the procedures described previously, and rheological characterization was performed in order to test for thermoreversible gelation. Representative rheological data for samples with φ = 0.3 and Cs = 150 mM KOlAc as well as φ = 0.33 and Cs = 200 mM KOlAc are shown in in Figure S13 (squares and circles, respectively), including temperature ramps, frequency sweeps for T > Tg, and temperature jumps. For nanoemulsions with <D> < 100 nm, the nanoemulsions exhibited thermally-induced gelation that qualitatively resembles that observed for nanoemulsions with SDS as the surfactant (Figure S13A), and the linear viscoelasticity exhibits similarly solid-like behavior (Figure S13B). In all cases where thermal gelation occurs, the gelation is reversible, as evident by temperature jump experiments (Figure S13C). Furthermore, the linear viscoelasticity of nanoemulsions with φ = 0.33 and Cs = 200 mM for droplet sizes <D> = 56 nm and 187 nm demonstrate that the gel-paste transition with increasing <D> observed in emulsions with SDS is also observed when KOlAc is used as the surfactant (Figure S13B). These results show that the formation of nanoemulsion organohydrogels is independent of the specific surfactant used. We also note that both observed Tgel and linear viscoelasticity for nanoemulsions containing Cs = 200 mM KOlAc are nearly quantitatively identical to that of nanoemulsions of similar droplet size containing SDS, the observed moduli of gels formed differ between the two surfactants. This suggests that although the gel temperature is primarily determined by the polymer end-group chemistry, the interfacial properties of the surfactant play a significant role in determining the ultimate strength of the organohydrogel.





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(a) (b) (c) Figure S13. Thermal rheology of PDMS-in-water/PEGDA/KOlAc nanoemulsions. (a) Representative temperature ramp for φ = 0.33 and Cs = 200 mM KOlAc. (b) Frequency sweeps for T > Tgel for the droplet sizes and concentrations shown, demonstrating both solid-like viscoelasticity above Tgel as well as the previously observed gel-paste transition with increasing droplet size. (c) Temperature jump measurements for φ = 0.33 and Cs = 200 mM KOlAc. Upper plot shows linear viscoelastic moduli, and lower plot shows the sample temperature. Subsequently, formation of nanoemulsions was studied using several biocompatible oils including the fatty acid monoesters ethyl laurate (EL, Sigma Aldrich) and isopropyl myristate (IM, Sigma Aldrich), as well as the propylene glycol diester Miglyol 840 (Sasol). High pressure homogenization was used to prepare oil-in-water/PEGDA emulsions from each of these oils using either SDS or KOlAc as the surfactant. In all cases, emulsions with <D> > 100 nm were observed. Representative rheological data for an IM-in-water/PEGDA emulsion with φ = 0.33, P = 0.33, and Cs = 200 mM KOlAc having <D> = 165 nm are shown in Figure S14, where the linear viscoelastic moduli exhibit the same paste-like qualitative behavior of the PDMS-in-water/PEGDA nanoemulsions with similar droplet sizes.

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These results show that the gel-paste transition can be generalized to oils other than PDMS. We thus hypothesize that nanoemulsions of these oils with smaller droplet sizes (e.g. less than 100 nm) would produce thermoreversible gelation. However, at the time of publication we have been unable to find conditions under which sub-100 nm droplets can be produced. We believe this inability to arise from a combination of multiple effects, including the high viscosity and water/PEGDA phase solubility of the fatty acid oils relative to PDMS. The former has been shown to inhibit nanoscale droplet formation [McClements, Soft Matter 7:2297-2316, 2011], whereas the latter has been shown to increase the rate of Ostwald ripening thereby destabilizing the nanoemulsions [Wooster et al., Langmuir 24(22):12758-12765, 2008]. Thus, it is reasonable to expect that a biocompatible oil with sufficiently lower viscosity and continuous phase solubility should allow for the preparation of nanoemulsions which exhibit thermoreversible gelation. S6. Cryo-TEM imaging of organohydrogel microstructure In order to qualitatively confirm the microstructure of the nanoemulsion organohydrogels inferred from combined/USANS measurements, specifically a mesoporous percolated droplet network, cryo-TEM images were obtained of the nanoemulsion sample for which Figure 2 was obtained. We produced two different temperature samples at 25 °C and 35 °C using a Controlled Environmental Vitrification System (CEVS). The vitrication chamber was first set at 25 °C and 100% humidity for both samples to avoid sample evaporation artifacts. At 25°C, the polymeric solution was liquid. On a lacey carbon grid (Ted Pella, Inc), 3 μl of the sample was deposited using micro pipette. The excess volume of the sample was removed by the blotting step, absorbing the excess sample by filter paper to form thin lms of liquid spanning the grid holes. The 35 °C sample was prepared by increasing the temperature after the blotting. The sample-bearing grid was then plunged into a liquid ethane reservoir, close to its freezing point. Contact with the cryogen vitries the sample and preserves all of the microstructures in their native hydrated states. Then the grid was transferred under liquid nitrogen to the cooled tip of a cryo transfer stage (Gatan 626 DH). Finally, the stage was inserted under positive dry nitrogen pressure into a JEOL 2100 TEM and imaged at slight under focus. We acknowledge Jinkee Lee and Anubhav Tripathi (Brown U.) for assistance with cryo-TEM sample preparation and imaging. Representative images of the sample prepared at both temperatures are shown in Figure S15. Below the gel temperature (25 °C), droplets are apparent as dark circular features (white arrows). The large extended structures are portions of the lacey carbon grid. The droplets appear well-dispersed, with several interdroplet clusters. Since these clusters are not apparent from the SANS data, we conclude that they are either transient in nature, or arise due to the sample preparation. Near the gel temperature (35 °C), a dense, sample-spanning structure is formed. Individual droplets are apparent, which are configured into droplet-rich and droplet-poor regions with a characteristic size on the order of several droplets. Within this percolated structure, large regions without red droplets are apparent (red circles), with a characteristic dimension of multiple hundreds of nanometers. We conclude that these features are the mesoporous features deduced from USANS experiments, thereby confirming the microstructural picture described in Figure 2.





Figure S14. Cryo-TEM images of a nanoemulsion with φ = 0.33, P = 0.33 PEGDA, and Cs = 200 mM SDS taken at 25 °C (left) and 35 °C (right).





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