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S1 Growth and Aggregation Regulate Clusters Structural Properties and Gel Time Electronic Supplementary Information Stefano Lazzari 1* , Marco Lattuada 2* 1 Department of Chemical Engineering, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, Massachusetts 02139, USA 2 Université de Fribourg, Adolphe Merkle Institute, Chemin des Verdiers 4, CH-1700 Fribourg, Switzerland Corresponding authors [email protected] ; [email protected]

Growth and Aggregation Regulate Clusters Structural Properties … · 2017-05-03 · Growth and Aggregation Regulate Clusters Structural Properties and Gel Time Electronic Supplementary

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S1

Growth and Aggregation Regulate Clusters Structural Properties and Gel Time

Electronic Supplementary Information

Stefano Lazzari1*, Marco Lattuada2*

1Department of Chemical Engineering, Massachusetts Institute of Technology, 77, Massachusetts

Avenue, Cambridge, Massachusetts 02139, USA

2Université de Fribourg, Adolphe Merkle Institute, Chemin des Verdiers 4, CH-1700 Fribourg,

Switzerland

Corresponding authors

[email protected] ; [email protected]

S2

Calculation of the hydrodynamic radius of a cluster made of interpenetrating particles having

different sizes

In the following, we will use Kirkwood-Riseman (KR) theory extended to the case of clusters

with partially-overlapping primary particles having different size.

We will begin by considering the general formalism of KR theory. For colloidal particles, the

interactions with the surrounding fluid can be described in the framework of Stokes equations,

according to which is the linear relationship between the hydrodynamic force experience by a particle

and its relative velocity. The KR approach consists in writing that for each particle in a cluster the

hydrodynamic force equals:

Fi = 6πηRP,i ui − v i( ) (1)

where Fi is the force acting on the ith particle, ui is the ith particle velocity and vi is the fluid velocity at

the center of the ith particle. The fluid velocity is written as a combination of the unperturbed fluid

velocity and of the perturbations due to all other particles in the cluster. Assuming that there the

unperturbed velocity of the fluid is zero, one can write:

v i = v i ' = Tij ⋅Fj

j=1≠ì

N

∑ (2)

In Equation(2), the tensor Tij provides the relationship between the force acting on particle j and the

corresponding velocity perturbation caused by it at the center of particle i 1. By combining the two

previous equations, we obtain the following result:

Fi = 6πηRP,i ui − Tij ⋅Fj

j=1≠ì

N

∑⎛

⎝⎜⎞

⎠⎟ (3)

S3

By taking the angular average of Equation (3) (which amounts to considering the cluster as an isotropic

object), and by summing over the total number of particles N in a cluster, we can obtain the following

expression for the total hydrodynamic force acting on it:

FT = Fi

i=1

N

∑ = 6πηRP,i ui − Tij ⋅Fjj=1≠ì

N

∑⎛

⎝⎜⎞

⎠⎟⎡

⎣⎢⎢

⎦⎥⎥i=1

N

∑ = 6πηRP,iu− 1N

6πηRP,i Tijj=1≠ì

N

∑i=1

N

∑ FTi=1

N

∑ (4)

The latter equation can be rearranged as follows:

FT =6πη RP,i

i=1

N

1+ 1N

6πηRP,i Tijj=1≠ì

N

∑i=1

N

∑u (5)

This leads to the following expression for the hydrodynamic radius of a cluster:

RH =RP,i

i=1

N

1+ 1N

6πηRP,i Tijj=1≠ì

N

∑i=1

N

∑ (6)

The only open question about the above equation, is the necessity to use a suitable expression for the

tensor Tij, valid for partially overlapping spheres with different sizes. This requires an extension of

Rotne-Prager-Yamakawa tensor, valid for equal size particles 2. Such an expression has been developed

by Zuk et al.3:

S4

Tij =

18πηRij

1+Rp,i

2 + Rp, j2

3Rij2

⎝⎜

⎠⎟ I + 1−

Rp,i2 + Rp, j

2

Rij2

⎝⎜

⎠⎟

R iR j

Rij2

⎝⎜

⎠⎟ for Rij ≥ Rp.i + Rp, j

16πηRp,i Rp, j

16Rij3 Rp,i + Rp, j( )− Rp,i − Rp, j( )2

+ 3Rij2⎛

⎝⎞⎠

2

32Rij3

⎜⎜⎜⎜

⎟⎟⎟⎟

I

+3 Rp,i − Rp, j( )2

− Rij2⎛

⎝⎞⎠

2

32Rij3

⎜⎜⎜⎜

⎟⎟⎟⎟

R iR j

Rij2

⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟

for Rp,> − Rp,< ≤ Rij ≤ Rp,i + Rp, j

16πηRp,>

I for Rij ≤ Rp,> − Rp,<

⎪⎪⎪⎪⎪⎪⎪⎪⎪

⎪⎪⎪⎪⎪⎪⎪⎪⎪

(7)

By performing the orientation average, we obtain the following final result:

Tij =

16πηRij

I for Rij ≥ Rp.i + Rp, j

16πηRp,i Rp, j

Rp,i + Rp, j( )2

−Rij

4−

Rp,i − Rp, j( )2

4Rij

⎜⎜

⎟⎟

I for Rp,> − Rp,< ≤ Rij ≤ Rp,i + Rp, j

16πηRp,>

I for Rij ≤ Rp,> − Rp,<

⎪⎪⎪⎪

⎪⎪⎪⎪

(8)

When Equation (8) is substituted in Equation (6), we obtain the general expression used for the

calculation of the hydrodynamic radius of the clusters generated by the Monte Carlo code developed in

this work.

S5

Convergence on MAXN

Figure S1 dm vs Nτ in DLCA at φ0 = 1% and 1.00%Rg = for different values of MAXi . Color code:

violet 1000MAXi = ; blue 2000MAXi = ; green 3000MAXi = ; yellow 4000MAXi = ; red 5000MAXi = .

As 4000MAXi = and 5000MAXi = are superimposed, 5000MAXi = has been selected as the upper

boundary of cluster mass to be considered for the calculation of dm

S6

DLCA case – kinetic information

i) φ against Nτ

Figure S2 φ vs Nτ in DLCA at different 0φ and Rg . Color code: violet 0.00%Rg = ; blue 0.25%Rg =

; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S7

ii) AVEN against Nτ

Figure S3 AVEN vs Nτ in DLCA at different 0φ and Rg . Color code: violet 0.00%Rg = ; blue

0.25%Rg = ; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S8

iii) φP against Nτ

Figure S4 φP vs Nτ in DLCA at different 0φ and Rg . Color code: violet 0.00%Rg = ; blue

0.25%Rg = ; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S9

RLCA case – kinetic information

i) φ against Nτ

Figure S5 φ vs Nτ in RLCA with 0.1Sp = at different 0φ and Rg . Color code: violet 0.00%Rg = ;

blue 0.25%Rg = ; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S10

ii) AVEN against Nτ

Figure S6 AVEN vs Nτ in RLCA with 0.1Sp = at different 0φ and Rg . Color code: violet 0.00%Rg = ;

blue 0.25%Rg = ; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S11

iii) φP against Nτ

Figure S7 φP vs Nτ in RLCA at different 0φ and Rg . Color code: violet 0.00%Rg = ; blue

0.25%Rg = ; green 0.50%Rg = ; yellow 0.75%Rg = ; red 1.00%Rg =

S12

References

1. Lattuada, M.; Wu, H.; Morbidelli, M. Hydrodynamic radius of fractal clusters. Journal of Colloid and Interface Science 2003, 268 (1), 96-105. 2. Lazzari, S.; Jaquet, B.; Colonna, L.; Storti, G.; Lattuada, M.; Morbidelli, M. Interplay between Aggregation and Coalescence of Polymeric Particles: Experimental and Modeling Insights. Langmuir 2015, 31 (34), 9296-9305. 3. Zuk, P. J.; Wajnryb, E.; Mizerski, K. A.; Szymczak, P. Rotne-Prager-Yamakawa approximation for different-sized particles in application to macromolecular bead models. Journal of Fluid Mechanics 2014, 741.