Supplementary Information
Leaf-architectured 3D Hierarchical Artificial Photosynthetic System of
Perovskite Titanates Towards CO2 Photoreduction Into Hydrocarbon Fuels
Han Zhou 1, 5, Jianjun Guo1, 3, Peng Li 1, 3, Tongxiang Fan 5, Di Zhang 5, and Jinhua Ye *1, 2 ,3, 4
1
International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for
Materials Science (NIMS), 1-1, Namiki, Tsukuba, Ibaraki 305-0044, Japan
*Email: [email protected]
2
Environmental Remediation Materials Unit, Catalytic Materials Group, National Institute for
Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan
3
Department of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Japan
4
TU−NIMS Joint Research Center, School of Materials Science and Engineering, Tianjin
University, 92 Weijin Road, Nankai District, Tianjin 300072, P. R. China
5
State Key lab of Composites, Shanghai Jiaotong University, Shanghai, 200240, China
1
Figure S1. FESEM images of (a) the upper epidermis of Cherry Blossom Leaf, with the inset of the
illustration of epidermal focusing by a lens mechanism and light distribution within the leaf (b) the
cross-section of the vein architecture, indicating the highly porous feature. (c) the cross-section of
Cherry Blossom leaf, indicating the differentiation of leaf mesophyll into palisade and spongy layers.
Right part: Possible pathway of light through the leaf.
The whole architecture of natural leaves strongly favors light harvesting: For example,
many kinds of leaves are evolved lens-like epidermal cells, which act as lenses that focus the
incident light via refraction 1 (Fig. S1a). As a result, refraction of light through the epidermis
of leaves can create significantly higher local irradiances within the leaf than that present in
the ambient light environment. Theoretically maximal focal intensification can approach 20
times the irradiance of incident light
2
when that light is collimated. Furthermore, internal
light-scattering can trap light so that internal fluence rates can exceed that of incident light.
Furthermore, the FESEM image (Fig. S1b) of the cross-section of the veins reveal the
hierarchical porous network. Light that enters leaf venation architectures becomes highly
2
scattered and increases light absorption. Another important factor is the differentiation of leaf
mesophyll into palisade and spongy layers (Fig. S1c). The columnar cells in palisade layer are
elongated and parallel to the direction of light, facilitating light channeling into the deeper
layers. 3 These cells act as light guides, propagating light through vacuoles and intercellular air
spaces. 3 The spongy mesophyll cells are less regularly arranged, leading to greater effective
light path length and light scattering 4. Such complex architectures bring about multiple
scattering and enhance light absorption within the leaves.
Figure S2 TGA curves of (a) pretreated original leaves Cherry Blossom (b) Sr(CH3COO)2 (c) as–
synthesized precursor for SrTiO3.
For the as-synthesized precursor for SrTiO3, below 120 °C, the weight loss is owing to volatile
species (including water, CH3COOH, and EtOH). Between 120 °C and 280 °C, a mass loss
arised from the oxidation of P123 template. At the temperatures between 280 to 550 °C, the
Sr(CH3COO)2 decomposed, some residual organic matter, such as amorphous carbon or
carboxylate species and possibly hydroxyl groups, was removed. Between 550 to 610 °C, a
rapid weight loss appeared. It may be explained by that the SrCO3, a decomposition product of
3
Sr(CH3COO)2, reacted with TiO2 for formation of crystalline SrTiO3, which is an exothermic
reaction. 5
Figure S3 TEM images of leaf-architectured (a) SrTiO3 (b) CaTiO3 (c) PbTiO3, indicating the
mesoporous features.
Table S1 Characterization of the Porosity of the synthesized samples
Sample name
SBET [m2 g-1]
Mesopore size [nm]
Vmeso [cm3 g-1]
APS STO
72.6
7.2
0.13
Referenced STO
26.4
7.6
0.05
APS CTO
45.9
7.4
0.09
Referenced CTO
27.9
7.1
0.05
APS PTO
11.8
7.7
0.023
Referenced PTO
1.7
7.9
0.003
4
Figure S4 Comparative hierarchical pore size distributions of CaTiO3 series derived from the N2 (BJH
model, red triangles) adsorption (the inset of (1), and mercury intrusion porosimetry, respectively. The
plots are offset for clarity. (1)leaf-architectured APSCTO, with the inset of sample’s optical image. (2) the
corresponding powder constituents of APSCTO, with the inset of sample’s optical image. (3) referenced
CTO synthesized without templates.
5
Figure S5 CO and CH4 evolution activities on bare CTO series.
Table S2 One-, two-, six-, and eight-electron reduction potentials (vs NHE) of some reactions involved
in CO2 photoreduction at pH 7 and unit activity. Adapted from
6
6
Figure S6 UV-vis absorption spectra of the as-synthesized APSATO series.
Figure S7 (a) SEM image of leaf-framed STO after grounding and sonication (b, c) TEM images of
leaf-framed STO after grounding and sonication, indicating the existence of a part of macropores after
a series of treatment.
7
Detailed calculations and explanations of gas diffusion in the samples
Mean free path of a molecule is the average distance that the molecule travels between
collisions.
L=
Where K equals to 1.38 ×10
-23
KT
2
2D0 P
(1)
, T is assumed to be 323K in our experiment, D0 refers to the
diameters of gas molecules (nm). Table S3 listed the values of D0 and L of six gas molecules.
Table S3 Values of D0 and mean free path (L) of six typical gas molecules.
Gases
CO2
CH4
CO
O2
H2
H2O
D0 (nm)
0.39
0.38
0.38
0.35
0.29
0.27
L (nm)
73.7
70.2
71.7
63.7
121.4
139.0
K
d
L
(2)
d is mean diameters of pores, L is the mean free path of a molecule. There are three main gas
diffusion types (Knudsen diffusion, molecular diffusion and surface diffusion) according to
different values of K as shown in Figure S8.
Figure S8. Schematic illustration of three main gas diffusion types according to different sizes of pores.
8
(1) K<0.1, Knudsen Diffusion
In many applications involving gases in mesoporous materials, which are materials with most
pore sizes between 2 and 50 nm, Knudsen diffusion is the predominant transport mechanism.
Knudsen diffusion is a result of collisions of gas molecules with the pore walls, rather than
intramolecular collisions (Brownian motion). According to gas diffusion theories, diffusion of
gas molecules in mesopores smaller than a mean free path is described with Knudsen
equation7
D
2 8RT 1 / 2
rp (
)
3
M
(3)
Where D, rp, R, T, and M are diffusion coefficient in pores, pore radius, gas constant,
temperature, and molecular weight of gas molecules, respectively. The smaller the mesopore
size is, the smaller the diffusion coefficient becomes. A time, t, necessary to diffuse particular
length, l, is roughly estimated by the relation
t  l 2 / 2D
(4)
Therefore, long time is required for reactants (CO2 and H2O vapor) to move into the deeper
layer of photocatalysts with small mesopores in which diffusion coefficient is also small. Then,
the deeper layer of photocatalysts does not effectively work in reaction. Similarly, long time is
also needed for products (e.g. CO, CH4, etc) to move from the deeper layer into the
atmosphere. An excellent catalyst is expected to suffer a minimum effect of diffusion.
(2) K>10, Molecular (Fick) diffusion
The transport diffusivity relates the macroscopic flux of molecules in a system to a driving
force in the concentration. This diffusion mode is applicable to Brownian motion, where the
movement of each particle is random and not dependent on its previous motion. It is clear that
9
the size of macropores in is much larger than average free path of gas molecules, typically in
the order of μm. Therefore, the diffusion coefficient is evaluated assuming the molecular
diffusion. For the estimation of diffusion coefficient in macroporous samples, we used the
following equation for molecular diffusion. The binary diffusion coefficient is calculated
using the Chapman-Enskog equation 7:
Dm  0.0018583
T 3 / 2 (1 / M A  1 / M B )1 / 2
P 2 AB AB
(5)
Where P, σ, Ω are respectively total pressure, lennard-Jones potential parameter, and collision
integral.
 AB  ( A   B ) / 2
(6)
 AB is the collision diameter, M is the molecular weight of species.
D pore =

D
 ideal
(7)
ε is the porosity, ζ is the tortuosity which is 3-6 for usual porous materials 7. For this example,
the following temperature and pressure conditions have been chosen: T=323K, P=0.85 atm.
For ease of representation, the components are numbered as 1:CO2, 2:H2O, 3: CH4, 4: CO.
The molecular weights are: M1=44 g/mol M2=18 g/mol M3=16 g/mol M4=28 g/mol. The
values of σ of the components are 8: σ1=3.941, σ2=2.641, σ3=3.758, σ4=3.69. So according to
equation 2, σ12=3.291, σ13=3.849, σ14=3.816, σ23=3.199, σ24=3.166, σ34=3.724. According to
the porosity values of these samples measured by N2 adsorption and mercury intrusion
porosimetry, we calculated the Dk (diffusion coefficient in mesopores) and Dm (diffusion
coefficient in macropores) of some typical samples. The diffusion coefficients of the four
typical gases calculated for mesopores in leaf-architectured STO are as follows:
10
D1k =0.0189 cm2/s,
D2k =0.0296 cm2/s
D3k =0.0314 cm2/s
D4k =0.0237 cm2/s
Table S4 summarizes diffusion coefficients calculated for macropores. Ideally, coefficient of
molecular diffusion is about 20 times larger than that of Knudsen diffusion. In addition, we
have to take into account the contribution of structural factors such as porosity, ε, and
tortuosity, ζ, to diffusion in a real porous materials 7. The real diffusion coefficients in
macropores are at least 5 times larger than that of Knudsen diffusion in mesopores.
Table S4 Diffusion coefficients in macropores (Dm)
Dpore (cm2/s)
Dm values
Dideal (cm2/s)
Leaf-architectured STO
Powdered form
D12
0.33
0.097
0.077
D13
0.25
0.074
0.058
D14
0.21
0.062
0.049
D23
0.42
0.124
0.098
D24
0.38
0.112
0.089
D34
0.29
0.085
0.067
11
Figure S9 Schematic illustration of the comparison between (a) 3D APS and (b) corresponding powder
constituents and referenced ATO.
Figure S10 UV-vis absorption spectra of the as-synthesized noble metal loaded SrTiO3.
12
Figure S11. TEM images of 1 wt % noble metal loaded leaf-framed STO (a) Pt (b) Au, (c) Ag, (d) Cu.
The loading method is photodeposition method.
Table S5 CO2 photoreduction over APS STO photocatalyst with various cocatalysts
Activity /nmol h-1g-1
Cocatalyst (1wt %)
Loading method
None
CO
CH4
84.8
9.5
Pt
Photodeposition
0
7.7
Au
Precipitation
349
231
Au
Photodeposition
154.1
82.2
Ag
Photodeposition
131.8
44.7
Cu
Photodeposition
56.1
36.1
RuO2
Impregnation
18.2
9.6
NiOx
Impregnation+H2 reduction+O2 oxidation
87.1
12.4
13
Figure S12. CO2 photoreduction activities on (a) APSCTO with and without Au cocatalyst under full arc
Xe lamp. (b) Au (1wt%)-APSPTO under full arc Xe lamp and under λ>420nm, respectively.
The edge of the valence band (EVB) of PTO was determined to be 2.6 V (vs normal
hydrogen electrode, NHE), more positive than that of E°(H2O/H+) (H2O→1/2O2 + 2H+ + 2e-,
E° redox = 0.82 V vs NHE), and the edge of the conduction band was estimated to be -0.3 V,
more negative than that of E° (CO2/CH4) (CO2 + 8e- + 8H+ → CH4 + 2H2O, E° redox = -0.24
V vs NHE), but positive than that of E° (CO2/CO) (CO2 + 2e- + 2H+ → CO+H2O, E° redox = 0.53 V vs NHE). This indicates that the photogenerated electrons and holes on the irradiated
PTO can react with adsorbed CO2 and H2O to produce CH4, but no CO.
14
Figure S13 Reaction setup for evaluation of conversion rate of CO 2.
References
1. M. E. Poulson, T. C. Vogelmann, Epidermal focusing and effects upon photosynthetic lightharvesting in leaves of Oxalis, Plant Cell Environ. 1990, 13, 803-811.
2. R. A. Bone, D. W. Lee, J. M. Norman, Epidermal cells functioning as lenses in leaves of tropical
rain-forest shade plants, Appl. Opt. 1985, 24, 1408-1412.
3. T. C. Vogelmann, G. Martin, The functional significance of palisade tissue: penetration of
directional versus diffuse light, Plant Cell Environ. 1993, 16, 65-72.
4. E. H. Delucia, K. Nelson, T. C. Vogelmann, W. K. Smith, Contribution of intercellular reflectance
to photosynthesis in shade leaves, Plant Cell Environ. 1996, 19, 159-170.
15
5. X. Fan, Y. Wang, X. Chen, L. Gao, W. Luo, Y. Yuan, Z. Li, T. Yu, J. Zhu, Z. Zou, Facile method to
synthesize mesoporous multimetal oxides (ATiO3, A=Sr, Ba) with large specific surface areas and
crystalline pore walls, Chem. Mater. 2010, 22, 1276-1278.
6. V. P. Indrakanti, J. D. Kubicki, H. H. Schobert, Photoinduced activation of CO2 on Ti-based
heterogeneous catalysts: current state, chemical physics-based insights and outlook, Energy Environ.
Sci. 2009, 2, 745-758.
7. J. M Smith, in Chemical Engineering kinetics, third ed., McGraw-Hill Book Co, New York, 1981,
Ch. 11.
8. P. Chinda, S. Chanchaona, P. Brault, W. Wechsatol, Mathematical modeling of a solid oxide fuel cell
with nearly spherical-shaped electrode particles, Journal of sustainable energy and environment, 2010,
1, 185-196.
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