Preparation of silica nanoparticles

Preparation of silica nanoparticles
Process: Sol - Gel - Synthesis - Precipitation
Chemical reactions: Hydrolysis - Polycondensation
Hydrolysis:
Suspension in ethanol
Si(OC2H5)4
+
4 H2O
Tetra ethyl orthosilicate (TEOS)
Si(OH)4
pH 11 - 12 (NH3)
+
Silicon tetra hydroxide
4 C2H5OH
Ethanol
Polycondensation:
Suspension in ethanol
Si(OH)4
nano- SiO2 (Sol)
(S l) +
pH 11 - 12 (NH3)
Silicon tetra hydroxide
Principles:
p
Silica
Nucleation, nucleus ggrowth, Ostwald ripening,
p
g (agglomeration)
gg
Controlled double jet precipitation (CDJP)
2 H2O
Preparation of silica nanoparticles - Experimental realisation
One-step-process - Two-step-process
G. Kolbe / W. Stöber / H. Giesche - H. Giesche
0,2 M tetraethylorthosilicate
ammonia / water
ethanol
ethanol
particles 50 nm – 10 μm
Transmission electron microscopy (TEM)
image of Stöber particles
tetraethylorthosilicate / ethanol
Products:
titanium (IV) –oxide, aluminium oxide, zirconium (IV)-oxide
nuclear power materials ThO2, UO2, PuO2
Advantages:
Disadvantages:
often mono disperse, spherical particles of controlled size
reaction has to be carried out with low particle concentrations, low produc- Scanning electron microscopy (SEM) imtion output
age of Stöber particles
G. Kolbe, Das Komplexchemische Verhalten der Kieselsaure, Dissertation, Friedrich-Schiller-Universität Jena, 1956
W. Stöber, A. Fink, E. Bohn, Controlled growth of monodispersed spheres in the micron size range, J. Colloid and Interface Sci. 26 (1968) 62-69
H. Giesche, Synthesis of monodispersed silica powders - 1. Particles properties and reaction kinetics, J. Eur. Ceram. Soc 14 (1994) 189-204
H. Giesche, Synthesis of monodispersed silica powders - 2. Controlled growth reaction and continuous production process, J. Eur. Ceram. Soc. 14 (1994), 205-214
T. Sugimoto: Fine particles-synthesis, characterization, and mechanism of growth, Surfactant Sci. Ser. Vol. 92, Marcel Dekker, New York, 2000
Model of LaMer and Dinegar
concentration of a low soluble component
+
+
+ +
+
+
+
nucleus
l
formation
f
i
critical
i i l super saturation
i C0
C0
super saturation
growth
saturation concentration CS
CS
growth
nucleus formation
reaction time
V.K. LaMer, R.H. Dinegar, Theory, production and mechanism of formation of monodispersed hydrosols, J. Amer. Chem. Soc.72(1950) 4847-4854
Partticle size d
distributio
on Q0 in %
Stöber process for generating monodisperse silica particles
100
80
Reaction time
2 min
4 min
15 min
i
60
40
Temperature :
20
50 °C
Supersaturation:
135
0
0
50
100
150
200
250
300
Particle diameter d in nm
Particle size distributions Q0 (d): nucleation and growth of silica
Scanning electron microscopy
(SEM) images of silica particles
T. Günther, J. Jupesta, W. Hintz, J. Tomas, Untersuchung der Einflußgrößen auf das Wachstum von Siliziumdioxidpartikeln, Vortrag, GVC-Fachausschußtagung
Kristallisation, Boppard, 17.-18.03.2005
Particlee size distriibution Q0 in %
Stöber p
process for
f g
generating
g monodisperse
p
silica p
particles
100
Reaction temperature
80
20 °C
30 °C
40 °C
50 °C
60 °C
60
40
Supersaturation S = 135
20
0
100
200
300
400
500
600
Particle diameter d in nm
Particle size distributions Q0 (d): temperature influence on particle growth
Scanning electron microscopy
(SEM) images of silica particles
T. Günther, J. Jupesta, W. Hintz, J. Tomas, Untersuchung der Einflußgrößen auf das Wachstum von Siliziumdioxidpartikeln, Vortrag, GVC-Fachausschußtagung
Kristallisation, Boppard, 17.-18.03.2005
Particcle size disstribution
n Q0 in %
Stöber process for generating monodisperse silica particles
100
80
Reaction time
60
a) Addition of TEOS
5 min
30 min
60 min
b)) Addition off TEOS
5 min
40
20
0
0
50
100
150
200
250
Particle diameter d in nm
Particle size distributions Q0 (d): Particle growth after further
Addition of TEOS
(Temperature: 50 °C, Supersaturation : 35.7, Critical radius : 0.5
Scanning electron microscopy
(SEM) images of silica particles
T. Günther, J. Jupesta, W. Hintz, J. Tomas, Untersuchung der Einflußgrößen auf das Wachstum von Siliziumdioxidpartikeln, Vortrag, GVC-Fachausschußtagung
Kristallisation, Boppard, 17.-18.03.2005
Growth mechanisms of particles
Reaction – limited cluster aggregation RLCA
reaction rate
:
pH of suspension :
Hydrolysis >> polycondensation
pH in an acid range
Formation of polymer - like networks, porous particle with small pores
Reaction – limited monomer cluster growth RLMC (Eden growth)
reaction rate
:
pH of suspension :
Hydrolysis << polycondensation
pH in an alkaline range
Formation of large, nonporous particles, colloidal gel with large pores
Morphology of silica nanoparticles
Si(OH)4
Dimers
pH < 7 or
pH 7 - 10 with salts
Cycles
Particles
pH 7 - 10 without salt
p
1 nm
5 nm
10 nm
30 nm
3 – dimensional gel network
100 nm
Sol (Stöber – Particles)
Brinker, C.J.; Scherer, G.W. : Sol-Gel-Science, The Physics and Chemistry of Sol-Gel-Science, Academic Press, San Diego, 1990
Stöber process for generating monodisperse silica particles
particle fformation models
p
Model according LaMer and Dinegar (1950)
concentrations of sparely soluble compounds
+
sparely soluble monomers
and oligomers
+
+ +
+
+
+
fast homogenuous nucleation
nucleation
fractal structures of oligomers
critical supersaturation C0
C0
growth process by diffusion
growth
supersaturation
i
saturation concentration CS
CS
monomer addition onto
particle surface
growth
nucleation
Model according Bogush and Zukoski (1992)
“explosive” nucleation process
+
+
process time
rearrangement and disaggregation stable spherical particles
+
+ +
particle formation by
densification of fractals
stable spherical particles
Model according Bailey (1992)
+
+
“fluffy” larger aggregates
process time
V.K. LaMer,, R.H. Dinegar,
g , Theory,
y, production
p
and mechanism off formation
f
off monodispersed
p
hydrosols,
y
, J. Amer. Chem. Soc.72(1950)
(
) 4847-4854
J.K. Bailey, M.L. Mecartney, Formation of colloidal silica particles from alkoxides, Colloids and Surfaces 63 (1992) 151-161
G.H. Bogush, C.F. Zukoski, Uniform silica particle precipitation: an aggregative growth model, J. Colloid Interface Sci. 142 (1992) 19 -34
Parrticle size d
distribution
n Q0(d) in %
Stöber process for generating monodisperse silica particles
S d d particle
Seeded
i l - growth
h process
100
80
Seeded particles
60
Reaction time of particle formation
12 hours
25 hours
36 hours
50 hours
72 hours
40
20
20 μm
0
0
1
2
3
4
5
P ti l diameter
Particle
di
t d in
i μm
Light microscopy image of Stöber
particles
i l (d50,0 = 2,1
2 1 μm))
Particle size distributions Q0(d) for the seed particle - growth process of silica particles,
Seeded particles d50,0 = 344 nm, Zeta-potential -58,9 mV, end particles d50,0 = 2,1 μm, Zeta-potential -83,5 mV
a „critical specific particle surface area“ is necessary during the growth process to avoid secondary nucleation,
r (hydrolysis) < r (condensation onto the particle surface)
Ak =
f ( cTEOS , k hydrolysis
y
y , k condensation )
2 D ( CS − C )
d
(Chen 1996)
(Chen,
Population balance model for particle formation and aggregation (Nagao)
Classical kinetic theory:
kij
Particle formation: B0(t)
i - mer + j - mer
i + j - mer = k - mer,, i,, j ≥ 1 ….max
Aggregation:
Particle formation:
max
d Ck 1 k−1
= ∑( 1 + δi ,k−i ) ki ,k−i Ci Ck−i − Ck ∑( 1 + δi ,k )kik Ci
d t 2 i=1
i =1
+ δk1 ⋅ B0 ( t ) mit δi , j = 1 für i = j und δi , j = 0
für i ≠ j
0
B0 ( t ) ∝ k Hydrolysis ⋅ CTEOS
exp( − k Hydrolysis ⋅ t )
Smoluchowski process:
Increase of k - mers by aggregation of particles of size i and k – i with i = 1, 2 ... k - 1
Decrease of k - mers by aggregation with particles of size i = 1, 2 .... max
Introduction of the particle - particle interactions:
k ij = f ( k ijD , k ijR )
aggregation kernel in the model depends on the diffusion process and the surface reaction
2
∞
1 2 k T ( ri + r j )
k ij0
D
⎛ ϕ ( a ) ⎞ da
k
=
W ij =
= ( ri + r j ) ∫ exp ⎜ total
⎟ 2 and ϕ total ( a ) = ϕ attr ( a ) + ϕ rep ( a )
Diffusion: ij W
with
3η r r
kT
k ⋅
a
ij
van-der-Waals
van
de Waals att
attraction:
action:
i
j
ϕ attr ( a ) = −
ij
AH
6
ri + r j
⎝
⎠
⎛
2ri r j
2ri r j
a 2 − ( ri + r j )2 ⎞
⎜
⎟
+
+
ln
⎜ a 2 − ( r + r )2 a 2 − ( r − r )2
a 2 − ( ri − r j )2 ⎟⎠
i
j
i
j
⎝
electrostatical repulsion: ϕ rep ( a ) = 4π ε 0 ε r
ri r j
a
ψ 02 ln (1 + exp ( − κ ( a − ri − r j )
; su
surface
face reaction:
eaction:
⎛ ϕ total ( ri + r j
k ijR = k S0 exp ⎜⎜ −
kT
⎝
)⎞
⎟⎟ ( ri + r j )2
⎠
D. Nagao, T. Satoh, M. Konno, A generalized model for describing particle formation in the synthesis of monodisperse oxide particles based on the hydrolysis and
condensation of tetraethyl orthosilicate, J. Colloid Interface Sci. 232 (2000) 102-110
100
Experimental results:
80
90 s Reaction time
20 min Reaction time
120 min Reaction time
60
Modelled distributions (Nagao)
40
90 s Reaction time
20 min Reaction time
120 min Reaction time
20
0
10
100
Particle diameter d in nm
1000
Particle sizze frequency ddistribution q0(d) in nm-1
Partiicle size distrribution Q0(dd) in %
Stöber process for generating monodisperse silica particles
Modelling of the particle growth process
0,05
Experimental results
90 s Reaction time
0 04
0,04
Modelled distribution
90 s Reaction time
0,03
0,02
0,01
0,00
10
100
Particle diameter d in nm
Comparison
p
off experimental
p
p
particle size
z distributions Q0((d)) with simulated p
particle size
z distributions Q0((d),
),
-1
( C = 0.34 mol L ), and particle size frequency distribution q0(d) for a reaction time of 90 s, respectively
NH 3
Influence of pH and drying conditions on the morphology of silica particles
sols
gels
agglutination
drying
coagulation
T < TF
T > TC
T < TC
cryogel
aerogel
xerogel
coacervation
peptisation
silica
silica
Preparation of titania nanoparticles
Process: Sol - Gel - Synthesis - Redispersion
Chemical reactions: Hydrolysis - Polycondensation - Redispersion
Hydrolysis :
Ti(OC3H7)4
+
aqueous suspension, 50 °C
C
4 H2O
Titanium tetra isopropoxide TTIP
pH 1,3 (0,1 M HNO3)
Ti(OH)4
+
4 C3H7OH
Titanium tetra hydroxide
Isopropanol
Polycondensation :
Ti(OH)4
Titanium tetra hydroxide
aqueous suspension, 50 °C
pH 1,3
1 3 (0,1
(0 1 M HNO3)
TiO2
+
2 H2O
Titania
Redispersion :
TiO2 (Gel)
aqueous suspension, 50 °C
nano - TiO2 (Sol)
H 11,3
3 (0,1
(0 1 M HNO3)
pH
Titania
Titania
Parrticle size ffrequencyy distributtion q0 (logg d) in nm
m-1
Particle size distribution of titania nanoparticles
Method : Dynamic light scattering DLS
Instrument :
Zetamaster (Malvern)
4.0
Particle size distribution of titania suspension
after the redispersion process of 24 hours
3.0
•
2.0
Characteristic shear rate γ :
437 s-1
Reynolds number Re:
6,650
Specific power consumption PV:
0.1 kW m-3
Mean particle diameter:
1.0
dm,
(volume mean))
m 3 = 18.6 nm (
dm, 0 = 12.0 nm (number mean)
5 10
50
Particle diameter in nm
100
Stabilisation of titania nanoparticles in suspension
Zetta - Poteential in
n mV
40
Zeta - Potential in mV
30
20
OH2+
+ H+
10
Ti
OH2+
OH2+
OH
0,5
Ti
OH
acid
OH2+
0
0,0
O-
OH
+OHO-
base
O-
OH
1,0
1,5
Ti
2,0
2,5
pH - value of suspension
Zeta potential of TiO2 ranging from + 20 mV to + 40 mV for a pH < 3.0
O-
Experimental Design
Experimental
p
setup
p
Stirred tank reactor
with a six-blade disk turbine
•
DIN 28139 T2 standard
DIN 28131 standard
Conc. Nitric acid HNO3:
437 s-1 … 3426 s-1
Reynolds number Re:
6,650 … 25,250
turbulent fluid flow
Specific power consumption / dissipation rate ε :
Optimal reaction parameters
Reaction temperature:
Characteristic shear rate γ :
0 1 kW m-3… 7.0
0.1
7 0 kW m-3
50 °C
0.1 M HNO3 (pH 1.3)
Conc. Titanium tetra isopropoxide (TTIP):
0.23 M
Conc. of solid particles TiO2: ≈ 1.8 % w/w in suspension
⎛ ⎞
Characteristic shear rate: γ = ⎜ ε ⎟
⎜ν ⎟
⎝ ⎠
•
Reynolds number:
Number of revolutions n:
500 rpm … 1900 rpm
Circumferential speed:
0.58 m s-1… 2.2 m s-1
Re =
1
2
( ref . Camp , Stein )
n D2
ν
Specific power consumption PV of stirrer:
P N P ρ n3 D5
PV =
=
V
V
Camp, T.R.; Stein, P.C.: Velocity Gradients and Internal Work in Fluid Motion, Journal of the Boston Society of Civil Engineers 30, 219 - 237 (1943)
Particle size distribution of titania nanoparticles
Agglom
merate size distribution
n Q3 in %
Method :
Instrument : Mastersizer 2000
-1
shear rate 437 s
-1
shear rate 1309 s
-1
shear rate 2403 s
-1
shear rate 3426 s
100
80
60
(Malvern)
Mean agglomerate diameter :
40
20
0
1
10
Laser diffraction
100
1000
γ& = 437 s-1
d50,3 = 214.5μm
γ& = 1309 s-1
d50,3 = 175.1 μm
γ& = 2403 s-1
d50,3 = 114.9 μm
γ& = 3426 s-1
d50,3 = 87.6 μm
Agglomerate diameter d in μm
Agglomerate size distributions Q3(d) for different shear rates in the initial state of redispersion
The sequence of applied shear rates in relation to its effects of creating smaller particle size is
3426 s-11 > 2403 s-11 > 1309 s-11 > 437 s-11
Particle size distributions during redispersion
Ag
gglomerrate sizees d in μm
quantiles
600
500
d10,3
10 3
with
Q3 (d10,3
) = 10 %
10 3
d50,3
Q3 (d50,3) = 50 %
d90,3
Q3 (d90,3) = 90 %
Method :
Laser diffraction
Instrument : Mastersizer 2000
(Malvern)
Hydrodynamics :
400
300
Shear rate:
γ& = 1309 s-1
200
Reynolds number: Re = 13290
100
Specific power consumption:
PV = 1.01 kW m-3
0
0
50
100
150
200
Redispersion time in min
Agglomerate sizes during the redispersion process (shear rate γ& = 1309 s-1)
Aggglomeratte size disstribution
n Q0 in %
Particle size distributions during
g redispersion
p
100
reaction time of redispersion
80
6 hours
7 hours
h
8 hours
9 hours
10 hours
60
40
20
0
0
10
20
30
40
50
60
70
80
Agglomerate diameter d in nm
Agglomerate size distributions Q0 (d) during the redispersion process (shear rate γ& = 437 s-11)
Morphology of silica nanoparticles
Si(OH)4
Dimers
pH < 7 or
pH 7 - 10 with salts
p
Cycles
Particles
pH 7 - 10 without salt
1 nm
5 nm
10 nm
30 nm
3 – dimensional gel network
100 nm
Sol ((Stöber – Particles))
Brinker, C.J.; Scherer, G.W. : Sol-Gel-Science, The Physics and Chemistry of Sol-Gel-Science, Academic Press, San Diego, 1990
Granulometric properties of solid titania powder
Solid titania powder:
Redispersion
p
ffor 24 hours in 0.1 M HNO3,
Freeze-drying
z
y g for
f 48 hours
Drying for 24 hours at 80 °C
Calcinations for 2 hours at 250 °C and 500 °C
polydisperse particle size distribution (d50,3 : 210 μm - 450 μm)
not treated
3.2 g cm-33
solid particle density ρS
specific surface area (BET)
drying
calcinations
80 °C
250 °C
500 °C
4.0 g cm-33
4.0 g cm-33
4.0 g cm-33
185 m2 g-1
188 m2 g-1
84 m2 g-1
7 8 nm
7.8
7 9 nm
7.9
18 3 nm
18.3
surface
f
weighted
i ht d diameter
di
t d32
Differential Scanning Calorimetry / Thermo Gravimetry (crystalline modifications)
244 °C
C
TiO2 (amorph)
380 °C
C
TiO2 (anatase)
TiO2 (rutile)
Pore volume ffrequency
q
y distribution inside the titania agglomerates
gg
Pore volume frequency distribution
Porre volumee in cm3//g adsorbbent
/nm ppore diameter
0 04
0,04
Pore volume frequency distribution
B
Barrett-Joyner-Halenda
tt J
H l d (BJH) method
th d
0,03
applying Kelvin equation:
0,02
rk = −
2 ⋅ σ ⋅ V mol ⋅ cos Θ
P
R ⋅ T ⋅ ln
P0
0,01
0,00
Solid titania powder:
2
4
6
8
Pore diameter in nm
10
Redispersion for 24 hours in 00.1
1 M HNO3 , Freeze-drying for 48 hours
hours,
Drying for 24 hours at 80 °C
Structure of agglomerates in the suspension
Model of agglomerate structure
Image of scanning electron microscopy
of titania nanoparticles produced in a 0.1 M
agglomerate (porous)
DLS
HNO3 suspension (pH 1.3)
primary particles (nonporous)
BET
(solid agglomerates obtained by freeze-drying)
Kinetics of particle agglomeration and redispersion
Agglomeration
Redispersion
C1
Agglomerate
colloidal particle
k14
k11
b13
C4
Ci, Cj, Ck
Particle concentration of i, j, k - mers
kij
Agglomeration
gg
rate constant off i - mer + j - mer
bij
Redispersion rate constant of k - mer to i - mer + j - mer
Population balance model of the particle agglomeration and redispersion
Classical kinetic theory:
kij
i - mer
+ j - mer
i + j - mer = k - mer, i, j ≥ 1 ….max
bij
Agglomeration: Smoluchowski - process
max
d Ck 1 k−1
= ∑( 1 + δ i ,k−i ) ki ,k−i Ci Ck−i − Ck ∑( 1 + δ i ,k )kik Ci
d t 2 i=1
i =1
with δi , j = 1 for i = j and δi , j = 0
Redispersion: reverse Smoluchowski - process
max
d Ck
1 k −1
= − Ck ∑ ( 1 + δ i ,k −i ) bi ,k −i + ∑ ( 1 + δ ik ) bik Ci +k
dt
2
i =1
i =1
for i ≠ j
Smoluchowski - process :
Increase of k - mers by agglomeration of particles of size i and k – i with i = 1, 2 ... k - 1
Decrease of k - mers by agglomeration with particles of size i = 1, 2 ... max
Reverse Smoluchowski - process :
Decrease of k - mers by redispersion to particles of size i and k – i with i = 1, 2 ... k - 1
Increase of k - mers by redispersion to particles of size k and i = 1, 2 ... max
von Smoluchowski, M : Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen, Z. Phys. Chem. 92 (1918) 129 - 168
Population balance model of the particle agglomeration and redispersion
To calculate the change in the particle number concentration, a nonlinear differential
equation system,
system which includes all particles with 1 … ∞ primary particles
particles, has to be solved.
solved
Kernels of convection - controlled (orthokinetic) system:
Agglomeration rate constant:
k ij = k D ( ri + r j ) 3
Redispersion rate constant:
bij = bD ri3+ j
with ri and rj radii of agglomerating and redispersing particles.
Approach for a convection - controlled agglomeration kernel kD
kD =
1
Wij
8π
15
⎛ε
⎜
⎜ν
⎝
⎞
⎟
⎟
⎠
1
2
turbulent shear–induced agglomeration, in absence of
viscous
i
retardation
d i andd agglomerate
l
structurall effects
ff
Wij stability factor, agglomerate interactions taken into account
Reference: Saffman, P.G.; Turner, J.S.: On the collision of drops in turbulent clouds, J. Fluid Mech., 1, 16-30 (1956)
Approach for the particle size frequency calculation
Number of primary particles NPP
Number of agglomerates N0
in a single agglomerate (k-mer):
in the suspension:
N PP
qr(d) :
3
d DLS
= (1−ε ) 3
d BET
q3 ( d ) N0 ρ S π 3
=
⋅ d DLS
q0 ( d ) mti tan a 6
Particle size frequency distribution based on quantity r : mass (3),
(3) number (0)
N b off all
Number
ll agglomerates
l
t N0 in
i the
th suspension
i and
d th
the number
b based
b d concentrations Ci, Cj, Ck ... of the i, j, k - mers can be calculated
Particle size distributions during redispersion
Particle n
P
number ffraction μ in %
0
40
reaction time off redispersion
p
35
6
7
8
9
10
30
25
20
hours
hours
ou s
hours
hours
hours
15
10
5
1
2
4
8
16
32
Number of primary particles per single aggregate
Distributions of particle number fractions µ0 vs.
vs number of primary particles
per single aggregate (shear rate γ& = 437 s-1)
Optimisation problem of the population balance equation
for the reaction system in the equilibrium state :
kD
K
=
as well as C b
D
dC k
= 0
dt
KC =
with KC equilibrium constant :
max
1 k−1
3
Ck ∑( 1 + δ i ,k−i ) rk − ∑( 1 + δ ik ) ri3+k Ci +k
2 i=1
i =1
max
1 k−1
3
( 1 + δ i ,k−i ) ( ri + rk−i ) Ci Ck−i − Ck ∑( 1 + δ ik ) ( ri + rk )3 Ci
∑
2 i =1
i =1
for the reaction system in the non-equilibrium state :
kD
Agglomeration rate constant
bD
Redispersion
p
rate constant
dCk
+ f [ Ck ( bD , kD , ri , rj , t )] = 0
dt
Ci, Cj, Ck ... Particle concentrations of i,
i j,
j k - mers,
mers experimentally obtained from DLS
Kinetic constants of the agglomeration / redispersion process
Polydisperse system:
Agglomeration
kD = 3.0 10-3 s-1
Redispersion
bD = 6.9 10-13 cm-3 s-1
with
kij = k D ( ri + r j )3
bij = bD ri3+ j
Turbulent agglomeration kernel without particle interactions
8π
15
k D ,0 =
•
γ
St bilit factor
Stability
f t Wij
Wij =
k D ,0
kD
1
2
⎛ε ⎞
γ = ⎜⎜ ⎟⎟ = 437 s −1
with
⎝ν ⎠
•
k D ,0 = 565 s −1
agglomerate
l
t interactions
i t
ti
l d to
lead
t slow
l coagulation
l ti
from experiment Wij = 1.9 105 (corresponding energy barrier ≈ 15 kT)
Monodisperse system: ri = rj = 3,9 nm (only primary particles)
Agglomeration
kij = 14.6 10-22 cm3/s
(ref. Vorkapic kij = 7,9 10-22 cm3/s)
Redispersion
bij = 8.4 10-6 s-1
(ref. Vorkapic bij = 4,6 10-6 s-1)
Equilibrium constant
KC = 1.7 10
-16
cm
3
with
KC =
k ij
bij
Reference : Vorkapic, D.; Matsoukas,T. : J. Colloid Interface Sci. 214, 283-291 (1999)
Sol - gel processing
dehydratisation
chemical reaction
chemical reaction
Precursor
Sol
coating
dipping
Cryogel
drying
Gel
organic
i suspension
i
Thin layer structure
drying
surfactants
spherical particle in gel structure
Calcination
Aerogel
Xerogel
Calcination
Calcination
Powder
Ceramics
C.J. Brinker, G.W. Scherer: Sol-Gel Science, The Physics and Chemistry of Sol-Gel Processing, Academic press, San Diego, 1990
precursor
SiO 2
nano particle
TiO nanoparticle
2
SiO 2
nano particle
TiO agglomerate
2
SiO 2
nano particle
dense, closed homogeneous layer of heterogeneous layer of
mono-disperse TiO2 polydisperse TiO2 particles
TiO2 layer
Schematic representation of possible coating processes
Application of titania as a photocatalyst *
Project in cooperation with the Bulgarian Academy of Sciences, Sofia
Titania generates with UVA irradiation (320 - 400 nm): activated electrons and holes (h+ + e-)
TiO2 + hν
TiO2 (h+ + e-)
h+
OH●
+ OH-
OH● acts as an oxidising agent for organic compounds:
 manufacturing of thin layers on a substrate
 modification of thin layers by laser irradiation or chemical doping
 characterisation of the modified thin layers
 evaluation of the photo catalytic properties for degradation of
• organic compounds hazardous to environment
• biological materials
*Yordanova,V.; Starbova, K.; Hintz,W.; Tomas, J.;Wendt,U.: Excimer laser induced photo-thermal changes of sol-gel
TiO2 thin film, Journal of optoelectronics and advanced materials (Bukarest) 7 (2005) 2601-2606
*Starbova, K.; Yordanova, V.; Nihtianova, D.; Hintz, W.; Tomas, J.; Starbov, N.: Excimer laser processing as a tool for photocatalytic
design of sol-gel TiO2 thin films, Applied Surface Science, 254 (2008) 4044-4051
Hetero-agglomeration process for coating silica particles with titania
Zeta-potential in mV
40
stable
silica particles
titania particles
20
p.z.c. of TiO2 at pH 6.8
0
instable
-20
p.z.c. of SiO2 at pH 2.3
stable
-40
2
4
Surface modification
6
8
pH value
10
12
Zeta-potential of silica and titania particles in dependence of the pH value
Schematic representation of experimental procedure
Tetraethylorthosilicate
HNO3
Ammonia, Isopropanol, Water
(pH 11. 0 - 12.0)
0.1 M Nitric acid (pH 1.0)
Tetraisopropylorthotitanat
Hydrolysis, Polycondensation
Hydrolysis, Polycondensation, Redispersion
Silica (Stöber) particles dm,0 = 332 nm, 500 nm, 800 nm
Nano-scaled titania particles - dm,0 = 12.0 nm
Amorphous titania sol at pH 1.3
SiO2 suspension centrifugation - 10 min, 1000 g
drying at 80 °C for 3 hours
conversion to crystalline modification Anatase
( temperature > 220 °C)
SiO2 particles redispersion in water (1 % wt/wt)
adjusting to pH 1.6 with HNO
addition of titania suspension to SiO2
Titania coated silica particles
Zeta-potential in mV
Zeta-potential of coated and non-coated silica particles
40
silica particles
TiO2/SiO2 2 wt%
TiO2/SiO2 10 wt%
20
TiO2/SiO2 20 wt%
0
-20
-40
1
2
3
4
5
6
7
pH value
Zeta-potential of coated and non-coated 332 nm silica particles
- coating with 2 % wt/wt, 10 % wt/wt, 20 % wt/wt TiO2 on SiO2
Apparent surface coverage of TiO2 particles on silica surface
Apparent surface coverage of TiO2 on SiO2 surface
Apparent surface coverage of TiO2 in%
70
60
50
40
30
apparent surface cov erage =
M SiO2 ( p .z .c SiO2 − p .c .z .TiO2 / SiO2 )
20
10
M SiO2 ( p .z .cTiO2 / SiO2 − p .c .z .SiO2 ) − M SiO2 ( p .z .c SiO2 − p .c .z .TiO2 / SiO2
0
0
5
10
15
20
25
30
35
Mass of TiO2 in % (wt/wt) of SiO2 in suspension
Apparent surface coverage of TiO2 on 332 nm silica particle surface
relating to the mass percentage of TiO2 to SiO2 in suspension
Surface morphology of Titania modified Silica particles
SEM image for coated 332 nm silica with 2 % (wt/wt)
i i
SEM image for coated 332 nm silica with 20 % (wt/wt)
SEM image for coated 332 nm silica with 10 % (wt/wt)
i i
SEM image for coated 332 nm silica with 30 % (wt/wt)