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Electronic Supplementary Material
Multiaxial electrospun generation of hollow graphene
aerogelspheres for broadband high-performance microwave absorption
Tian Li1, Dandan Zhi1, Yao Chen1, Bing Li2, Zuowan Zhou1, and
Fanbin Meng1 ()
1 Key Laboratory of Advanced Technologies of Materials (Ministry
of Education), School of Materials Science and Engineering,
Southwest
Jiaotong University, Chengdu 610031, China 2 Shandong
Qiangjunwei Intelligent Equipment Co. LTD., Yantai 264000, China
Supporting information to
https://doi.org/10.1007/s12274-020-2632-0
Figure S1 Optical microscope view of HGAS2
Table S1 Specific surface area, total pore volume, and the most
probable pore size of the graphene aerogel in recent
literatures
Sample Specific surface
area (m2/g) Pore volume (cm3/g) Ref.
Graphene aerogel fibers 884 5.85 [S1] SnS2-ND@G-aerogel
GAF 132 584
- 2.27
[S2] [S3]
GA 358 0.44 [ S4] Ni–MnO/rGO aerogels CoOx/Graphene aerogel
carbon sphere-graphene polylithic aerogels N-doped graphene
aerogel
Ordered Mesoporous Carbon/Graphene aerogel GAS
109 1359
609.3
420
1030
1634.7
- 7.2
1.41
0.399
1.30 1.64
[ S5] [S6]
[S7]
[S8]
[S9]
This work HGAS2 2367.6 2.69 This work
[S1] Xu, Z.; Zhang, Y.; Li, P. G.; Gao, C. Strong, conductive,
lightweight, neat graphene aerogel fibers with aligned pores. ACS
Nano 2012, 6, 8
7,103–7113. [S2] Luo, L.; Chung, S. H.; Manthiram, A. A
three-dimensional self-assembled SnS2-nano-dots@graphene hybrid
aerogel as an efficient polysulfide
reservoir for high-performance lithium–sulfur batteries J.
Mater. Chem. A 2018, 6, 7659–7667. [S3] Li, G. Y.; Hong. G.; Dong,
D. P.; Song, W. H.; Zhang, X. T. Multiresponsive
graphene–aerogel–directed phase–change smart fibers. Adv.
Mater.
2018, 30, 1801754. [S4] Hu, S.; Han, T.; Lin, C.; Xiang, W. K.;
Zhao, Y. H.; Gao, P.; Du, F. P.; Li, X. P.; Sun, Y. H.. Enhanced
electrocatalysis via 3D graphene aerogel
engineered with a silver nanowire network for ultrahigh–rate
Zinc–Air Batteries. Adv. Funct. Mater. 2017, 27, 1700041.
Address correspondence to [email protected]
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[S5] Fu, G. T.; Yan, X. X.; Chen, Y. F.; Xu, L.; Sun, D. M.;
Lee, J. M.; Tang, Y. W. Boosting bifunctional oxygen
electrocatalysis with 3D graphene aerogel-supported Ni/MnO
particles. Adv. Mater. 2018, 30, 1704609.
[S6] Xia, W.; Qu, C.; Liang, Z. B.; Zhao, B. T; Dai, S, G,; Qiu,
B.; Jiao, Y.; Zhang, Q. B.; Huang, X. Y.; Guo, W. H.; Dang, D.;
Zou, R. Q.; Xia, D. G.; Xu, Q.; Liu, M. L. High-performance energy
storage and conversion materials derived from a single
metal–organic framework/graphene aerogel composite. Nano Lett.
2017, 17, 2788–2795.
[S7] Dong. D. P.; Guo, H. T.; Li, G. Y.; Yan, L. F.; Zhang, X.
T.; Song, W. H. Assembling hollow carbon sphere-graphene polylithic
aerogels for thermoelectric cells. Nano Energy 2017, 39,
470–477.
[S8] Wang, J.; Duan, X. G.; Dong, Q.; Meng. F. P.; Tan, X. Y.;
Liu, X. M.; Wang, S. B. Facile synthesis of N-doped 3D graphene
aerogel and its excellent performance in catalytic degradation of
antibiotic contaminants in water. Carbon 2019, 144, 781–790.
[S9] Liu, R. L.; Wan, l.; Liu, S. Q.; Pan, L. X.; Wu, D. Q.;
Zhao, D. Y. An interface-induced Co-assembly approach towards
ordered mesoporous carbon/ graphene aerogel for high-performance
supercapacitors. Adv. Funct. Mater. 2015, 25, 526–533.
Figure S2 TEM image of HGAS2
Figure S3 Optical microscope view of HGAS1 with a diameter
of about 1.5 mm (a), HGAS3 with a diameter of about 2.5 mm (b) and
HGAS4 with a diameter of about 3.0 mm
Figure S4 Optical microscope view of BGAS1 with a diameter
of about 2.0 mm (a), BGAS3 with a diameter of about 3.0 mm (b) and
HGAS4 with a diameter of about 3.5 mm
Figure S5 3D representation of RL values at different
thickness (a-e), effective bandwidth at 2.1, 2.3, 2.6, 2.8, 3.0 and
3.4 mm respectively (f), 3D plots of minimum reflection loss
evaluation of HGAS1, HGAS3, HGAS4, BGAS1 and BGAS3 respectively
(g), effective bandwidth of BGAS3 at 2.7 mm and reflection loss at
2.1 mm.
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Nano Res. 2020, 13(2): 477–484
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Figure S6 Plots of εc″ and εp″ vs frequency of graphene
aerogel spheres
Appendix: For competition of both relaxation and conductance
losses, Non-linear least squares fitting method is applied to fit a
curve. According to this theory, the model function in this work is
ε=ε∞+(εs-ε∞)/(1+ω2τ2)- i ( (εs-ε∞) ωτ/(1+ω2τ2)+σ/(ωε0)), where ε∞
is optical dielectric constant, εs is static dielectric constant,
ε0 is free space dielectric constant, τ is the relaxation time and
σ is the conductivity.
To obtain εc" and εp", the εs, ε∞, σ and τ should be fitted
firstly, which are signed as a group β. The sum of squares is 2
1
mii
S r=
=å , where ri presents εfit-ε. Our goal is minimizing the S by
adjusting β. In order to fit these data as precise as possible, the
original data are divided into twenty parts and fitted separately.
The average value is used as the final result. As well, we use a
Python package Scipy* to simplify our code.
import numpy as np from scipy.optimize import least_squares
import xlrd def realimag(array): return np.array([(x.real, -x.imag)
for x in array]) def func(x,p):
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s,u,sigma,t=p #s:static dielectric constant;u:optical dielectric
constant;sigma:conductivity;t:relaxation time d=complex(0,1)
o=8.854187817*10**(-12) return realimag( u+(s-u)/(1+x*x**t*t)
-np.dot(d,np.dot(np.dot(x,t),np.divide((s-u),(1+np.dot(np.dot(np.dot(x,x),t),t)))))
-np.dot(d,np.divide(sigma,np.dot(x,o))) ) def
condut_loss_result(x,p): s,u,sigma,t=p o=8.854187817*10**(-12)
return np.divide(sigma,np.dot(x,o)) def residuals(p,y,x): return
(realimag(np.array(y)) - func(x, p)).flatten() p0 =
[20,10,1,10**-12] data=xlrd.open_workbook('original_data.xlsx')
data_number=200 groups=20 table=data.sheets()[3] fcost=0 fsig=0
#fitting conductivity ft=0 #fitting relaxation time fplsq=[] fs=0
#fitting static dielectric constant fu=0 #fitting optical
dielectric constant fconduct_loss=0 #fitting conducting loss
frelaxation_loss=0 #fitting polarization loss for i in
range(1,int(groups)+1): end=i*(int(data_number/groups))
start=end-int(data_number/groups) #print("Reading data group",i,":
from ",start," to ",end) xdata=table.col_values(0)[start:end]
ydata_1=table.col_values(2)[start:end] #imaginary permitivity
ydata_2=table.col_values(1)[start:end] #real permitivity ydata=[]
for m in range(int(data_number/groups)): xdata[m]=xdata[m]*10**9 #
GHz ydata.append(complex(ydata_2[m],-ydata_1[m])) plsq =
least_squares(residuals, p0,
bounds=([0,0,0,0],[200,200,200,10**-1]),args=(ydata,
xdata),max_nfev=100000) fplsq.append(plsq) print(plsq.x[0],"
",plsq.x[1]," ",plsq.x[2]," ",plsq.x[3]," ")
conduct_loss=np.mean(condut_loss_result(xdata,plsq.x))
#print("Conduct_loss ",i,":",conduct_loss) fconduct_loss +=
conduct_loss relaxation_loss=np.mean(ydata_1)-conduct_loss
#print("Relaxation_loss",i,":",relaxation_loss)
#print("Imaginary:",np.mean(ydata_1),", Conduct
Loss:",conduct_loss,", Relaxation Loss:",relaxation_loss)
#print(np.mean(xdata)/10**9," ",np.mean(ydata_1)," ",conduct_loss,"
",relaxation_loss)
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Nano Res. 2020, 13(2): 477–484
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frelaxation_loss += relaxation_loss fsig=fsig+plsq.x[2]
fs=fs+plsq.x[0] fu=fu+plsq.x[1] ft=ft+plsq.x[3]
fcost=plsq.cost+fcost
Figure S7 the calculated delta value maps of HGAS1 (a),
HGAS3 (b), HGAS4 (c), BGAS1 (d), and BGAS3 (e), respectively.