Concentrated Photovoltaic Thermoelectric Hybrid
System: An Experimental and Machine Learning Study
Zeming He,^{1, 2,#} Ming Yang,^{ 1, 2,#}
Lei Wang,^{3} Ergude Bao^{3,}* and Hang
Zhang^{1, 2,}*
^{1 }Institute of
Engineering Thermophysics, Chinese Academy of
Sciences, Beijing, China.
^{2 }University of
Chinese Academy of Sciences, Beijing 100049, China.
^{3 }School
of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
^{#}These authors contributed to this
work equally.
*Email: zhanghang@iet.cn (H. Zhang), baoe@bjtu.edu.cn
(E. Bao)
Abstract
Applying
solar energy over a wider spectral range can lead to more efficient energy
conversion. The combination of a photovoltaic (PV) cell and a thermoelectric
generator (TEG) is a widely studied technology for effectively broadening the
use of the solar spectrum. In this paper, we select two kinds of photovoltaic
cells and combine them with a TEG to form different systems,
and analyze the overall performance of each system to provide a certain
reference for optimal use of photovoltaic cells and a TEG in a hybrid system.
Furthermore, we use machine learning to optimize the structural parameters of
the hybrid system, and predict the optimal output
power of the system when the area ratio of the TEG and PV module is 4.41. This
work provides an important reference for further research on the PVTEG hybrid
system and its applications.
Table of Content
Keywords: PVTEG hybrid system; Performance analysis; Machine learning.
1.
Introduction
The demand for renewable energy and waste heat
recovery is increasing as fossil energy consumption and the need for
environmental protection grow. To deal with this situation, it is necessary to
develop an environmentallyfriendly energy system by
taking advantage of renewable energy. The energy from the sun is considered to be renewable and sustainable.^{[13]} Solar energy has been used to generate electricity via solar
cells for many years. Photovoltaic energy has some advantages such as
inexhaustibility, which helps reduce pollution and conserves fossil resources,
but the use of solar energy is limited because it is difficult to fully use the
solar spectrum. The part of solar energy
that cannot be used by photovoltaic cells, such as infrared energy, is
converted into waste heat.^{[46]} Although the output power
and efficiency of photovoltaic (PV) cells are enhanced using optical
concentrators to increase the intensity of solar radiation,^{[79]}^{ }the PV cell temperature increases significantly in a concentrated
PV (CPV) system, which reduces efficiency. Therefore, the excess thermal energy
should be removed to overcome this drawback of a concentrated PV system.^{[10]}
Recently, much
attention has been given to a PV system with a thermoelectric generator (TEG) in
which the PV cell is cooled by converting part of its heat into electricity via
the Seebeck effect using thermoelectric materials.^{[1115]}^{ }The incident solar flux on the PV cell or thermoelectric
generator can be adjusted by selecting from several collectors such as Fresnel
lenses, parabolic troughs, and parabolic dishes.^{[16,17]} In this PVTEG hybrid
system, the TEG is directly connected to the PV cell to recover the waste heat
and improve the conversion efficiency.
The theory of PVTEG hybrid
systems has been extensively developed. Meanwhile, studies have been done on
enhancing the conversion efficiency of a concentrated PVTEG system and on its
practical feasibility.^{[18]} Van Sark presented a simple
model of a PVTEG hybrid system in which the TEG is connected to the back of
the PV cell to harvest and use its thermal waste, and theoretically found that this
enhanced the efficiency of the system by 8–23%.^{[19]} Lamba and Kaushik presented
a theoretical model of a CPVTEG hybrid system based on the first and second
laws of thermodynamics and analyzed the influence of different parameters like
concentration ratio, solar irradiance, and the number of TEG thermoelements on
system performance. The results showed that the efficiency of the hybrid PVTEG
system was 13.37% more than that of a PVonly system for a concentration ratio
of 3 with 127 thermoelements.^{[20]}
Mahmoudinezhad et al. developed a numerical model for the transient
response of a CPVTEG hybrid system, and found that the
efficiency of the PV decreased and that of the TEG increased with an enhanced
concentration ratio. The results showed that using a TEG in such a hybrid
system yielded a more stable output power.^{[21,22]} Rezania
and Rosendahl developed a thermally coupled model to predict the performance of
a CPVTEG hybrid system. Critical parameters were considered to evaluate the
economic viability of the hybrid system compared with that of commercial CPV
systems. The results showed that the efficiency of the hybrid system was higher
than that of a CPVonly system, and that the TEG significantly impacted power
generation by the hybrid system, especially at high solar concentrations.^{[23]} Wu et al. analyzed
and compared the performance of a standalone PV system, solar PVTEG hybrid
system, and solar PVTE cooling hybrid system and found the PVTEG hybrid
system had increased total output power and the PVTE cooling system had
reduced PV cell temperature.^{[24]} Kraemer et al.
developed and numerically analyzed a spectrumsplitting PVTEG hybrid system
and compared the efficiencies of hybrid systems with crystalline silicon,
amorphous silicon, and heterojunction thinfilm PV modules for different solar
TEGs. The results provided useful information for partitioning the solar
spectrum to yield maximum conversion efficiency of a PVTE hybrid system.^{[25]} Zhu et al. presented
a thermal CPVTEG hybrid system with optimized thermal management and
theoretically calculated its heat flow and temperature distribution. The hybrid
system used a copper plate serving as a thermal concentrator and conductor to
maintain a large temperature difference on both sides of the TEG, which made
the hybrid system achieve a high peak efficiency of 23% in an outdoor test.^{[26]} Liao et al.
presented and discussed the performance characteristics of a device consisting
of a weakly concentrated PV module and a TEG, calculated its maximum power
output, and determined the optimal electric resistances of the CPV and TEG. They
found optimum values for the thermal conductance between the CPV and the TEG,
the CPV current, the solar irradiation, the concentration ratio, and other important
parameters.^{[27]}
Machine learning is a
technology in which a computer learns from existing knowledge and experience to
model data to predict future results or trends. Deep learning is a special
machine learning that needs a lot of data support. It involves making decisions
according to input and output data by imitating the perception and organization
of the human brain. Deep learning has developed rapidly in recent years,
especially after a team led by Hinton, the “Father of Neural Networks,” won the
first prize for its deep learning algorithm in the 2012 ImageNet LargeScale Visual
Recognition Challenge (ILSVRC). As a result, deep learning has gradually become
the main focus of research in natural language processing,
computer vision, and other fields. Because deep learning can save experimental
time, money, and manpower, physics research has gradually begun to use deep
learning methods such as deep neural networks to achieve new physical signals
and improve background selection,^{[28]} and deep learning
algorithms to predict crystal enthalpy of formation.^{[29]} Deep learning has also been
applied to gas turbines, particulate matter, organic Rankine cycles, and
hydrogen array sensor detection.^{[30]}
However, most works on
CPVTEG hybrid performance and optimization focused on theoretical studies
rather than experimental studies. Machine learning plays an important role in
research on energy systems and devices. Hence, in this paper, we establish a
hybrid system consisting of a low concentrator, a PV module, and a TEG that can
use the waste heat from the CPV module. We analyze the parameter design and use
machine learning to optimize the system.
2.
Experimental and machine learning methods
2.1
Experimental
The schematic of the proposed CPVTEG hybrid system is
shown in Fig. 1. The system mainly consisted of
a concentrator, a PV module, a TEG, and a watercooling system.^{ }The
TEG modules were made of bismuth telluride (Bi_{2}Te_{3}). The
PV modules and the TEG were placed in a parallel arrangement.
Fig. 1 CPVTE hybrid system: (a) schematic; (b) photograph.
Fig. 2 Characteristic curves of PV modules at different concentration ratios at
G=1000 W/m^{2}: (a) monocrystalline silicon; (b) gallium arsenide.
These PV modules mainly absorb shorter wavelengths
(visible and UV regions), and the TEG absorbs longer wavelengths (IR region). The
PV module is usually configured as the upper component and the TEG as the lower
component, as shown in Fig. 1. The cooling
system is placed at the back of the TEG to keep the temperature low on the cold
side. Under sunlight, the PV module absorbs UV and visible light, while the
rest of the radiation is transmitted to the hot side of the TEG through the PV
module. The infrared radiation heats the top of the TEG, causing a temperature
difference with the cold side. In the hybrid system, the PV and TEG modules are
electrically isolated but thermally connected. Such a hybrid system makes the
solar spectral energy available for use, thus improving the performance of the
system. In the following, we will analyze the performances of the two components
and the influence of their size ratio, and then investigate the performance of
the hybrid system.
The experiments were
conducted under indoor simulated light conditions to obtain steady results. A
sun simulator was used as a light source to provide and concentrate sunlight; this
consisted of a xenon lamp, reflector, and concentrator. The light intensity could
be adjusted by changing the working power of the xenon lamp. The hybrid system was
placed under the lamp, and the cooling temperature was controlled with the
watercooling system. The connecting interfaces between the PV, TEG, and
cooling block were covered with a thin layer of thermal grease to reduce the
thermal contact resistance.
The total input solar power,
temperature, and output power of the system were measured during the
experiment. A highlight power meter was used to measure the total input power
of the system. The temperature was measured with Ktype thermocouples. In
a hybrid system, connecting the PV module and TEG in series or parallel may
cause the total output power to decrease owing to the mismatch of the output
characteristics between the PV and TEG.^{[31,32]}^{ }Thus, the PV and TEG worked
separately and were measured with two electronic loads. The output power of the
PV or TEG was obtained from the IV curve measured with the electronic loads. Two types of solar cells, monocrystalline silicon and gallium
arsenide PV cells, were used in the experiment. Both were purchased
commercially and not encapsulated to ensure better contact with the TEG to
achieve better test results. The sizes of the PV and TEG are shown in Table 1.
Table 1. Device sizes.
Items 
Size of PV 
Size of TEG 
n value 
A represented as the crosssection
area of TEG or PV 
A_{PV} 
A_{TEG} 
A_{TEG }/A_{PV} 
10 mm × 10 mm 
10 mm × 10 mm 
1 

10 mm × 10 mm 
20 mm × 20 mm 
4 

10 mm × 10 mm 
30 mm × 30 mm 
9 

10 mm × 10 mm 
40 mm × 40 mm 
16 
The monocrystalline silicon
and gallium arsenide PV cells were selected to compare performances of
different photovoltaic cells. The characteristic curve of each PV cell is shown
in Fig. 2. The curves show that the maximum
powers of both PV cells increase with increasing concentration ratio. The
gallium arsenide cell shows a higher power output than that of the
monocrystalline silicon, especially under a high concentration ratio. The TEG
modules consisted of semiconductor elements and ceramic materials with high
thermal conductivity that were connected electrically in series and thermally
in parallel, and operated under steady conditions with
negligible radiative and convective heat transfer from the sides of the TEG
modules. The commercial Bi_{2}Te_{3} TEG modules had sizes of
10 × 10, 20 × 20, 30 × 30, and 40 × 40 mm^{2}.
The TEG modules differed mainly in size, while the amount of internal
semiconductor materials, Seebeck coefficient, and
thermal conductivity of the semiconductor materials were the same in all
selected TEGs. In other words, the types, sizes, and properties of
semiconductor materials were the same in all TEGs, and the differences in TEG
sizes were only due to the differences in the number of thermoelectric pairs. This
ensured that the experimental results would not be affected by differences in other
properties of the semiconductor materials.
The dimensions of the PV module were 10 × 10 mm^{2},
and we assumed that n=A_{TEG}/A_{PV}=1, 4, 9 and 16 for the
respective TEG sizes listed earlier, where A is the crosssectional area of the
TEG or PV. We investigated the performance of the hybrid system for each value
of n. The area of the TEG is larger than that of the PV cell when n is greater
than 1. For the experiment, we set up an insulation layer on the surface of the
TEG to ensure that the heat received by the hot side of the TEG was only from
the back of the PV module and not from the sunlight or other source.
2.2 Power
output and efficiency of the hybrid system
The output power of the PV module is given by
P_{PV }= I_{PV} ‧V_{PV}, (1)
where I_{PV}
and V_{PV} are the output
current and output voltage of the PV module, respectively.
The output power of the TEG is calculated from the
measured data via
P_{TEG }= I_{TEG} ‧V_{TEG}, (2)
where I_{TEG}
and V_{TEG} are the output
current and output voltage of the TEG module, respectively.
The electrical efficiency of the PV can be expressed
as
where A_{PV}
is the crosssectional area of the PV module, C is the concentration ratio, and G is the irradiation intensity equal to 1000 W/m^{2}. The
product CG gives the solar radiation.
The conversion efficiency of the TEG can be evaluated
as^{[33]}
where
The overall efficiency of
the PVTEG hybrid system can be express as
and the output power of the PVTEG hybrid system as
P_{o }= P_{PV }+ P_{TEG}. (6)
2.3 Machine
learning method
We used a deep neural network (DNN) and a long
shortterm memory network without and with an attention mechanism (LSTM and
LSTMA, respectively) to make a prediction. The power was predicted by inputting
the external load, concentration ratio, coldend temperature, material, and
size to each model. All the models were trained with 80 epochs, a batch size of
1, and a learning rate of 10^{–3}. This was implemented in the
framework PyTorch. Below are the detailed
descriptions of the models.
2.3.1 DNN
model
The neural network in the DNN includes the input
layer, the output layer, and a large number of hidden
layers. The layers are fully connected; that is, every neuron in the nth layer is connected with every
neuron in the (n+1)th layer. For each layer, the DNN
obtains the intermediate result z from the linear relationship z=wx+b, where w is the layer’s weight, x is the input, and b is an offset. Following this equation, there
is an activation function
In our experiment, we set
three layers for the DNN. All the features were inputted to the first layer,
which outputted 256 internal features with the ReLu
function. Then, 256 internal features were inputted to the second layer, which
outputted 128 internal features with the ReLu
function. Finally, 128 internal features were inputted to the third layer,
which outputted the prediction.
2.3.2 LSTM
model
The LSTM consists of several layers of cells, and
sequential data are inputted to cells in the same layer. The LSTM considers
sequential information in the data. Each cell has three gates: the input gate,
which determines how much input information can enter the network to
participate in updating the cell; the forget gate, which determines which
historical information can be retained; and the output gate, which determines
how much information can be outputted by the cell. LSTM can also avoid gradient
disappearance with a complex model.
In our experiment, we set
one layer of 5 cells for the LSTM, and each cell outputted 256 internal
features. The features were inputted to a fully connected layer to output the
prediction. We also biconnected the cells because the data inputted to them
depended on each other.
2.3.3 LSTMA
model
In an LSTMA model, the attention mechanism calculates the
importance of cells in the LSTM and assigns large weights to the important ones
to calculate the output. The attention mechanism assigns a weight a_{i} to a block of output h_{i} and calculates the output v with
2.3.4 Model
selection and validation
We used the root mean squared error (RMSE), mean absolute
percentage error (MAPE), and coefficient of determination (R^{2}) to
analyze the prediction results. The RMSE measures the deviation between predicted
and real values, the MAPE measures the deviation between predicted and real
values normalized by the predicted values, and R^{2} measures how well
a model fits a set of data. A small RMSE and MAPE and an R^{2} close to
1 indicate an accurate prediction.
The DNN, LSTM, and LSTMA results
are listed in Table 2. Generally, the RMSE and
MAPE of the various prediction models are small and R^{2} is close to 1,
indicating high prediction accuracy. LSTM achieves the smallest RMSE and lowest
R^{2}, while LSTMA has the smallest MAPE. This indicates the LSTM and
LSTMA models are advantageous over DNN.
Table 2. DNN, LSTM and LSTMA model results.
Model 
RMSE 
MAPE 
R^{2} 
DNN 
1.108 
4.420 
0.997 
LSTM 
0.788 
5.239 
0.998 
LSTM (Attention) 
0.990 
4.189 
0.997 
Fig. 3 shows the comparison between the model predictions and the
experimental results. The relative error between the LSTM predictions and the
experimental results is not more than 4^{ }%, which is the best accuracy
among the three models; therefore, the LSTM model should be selected as the
prediction model.
Fig. 3 Comparison between the model predictions and the
experimental results of output power off TEG. Plots a and b are for DNN, c and d
are for LSTM, and e and f are for LSTMA. The materials are monocrystalline
silicon (a, c, and e) and gallium arsenide (b, d, and f). The external load is
3.9, the coldend temperature is 25 °C, and the size is 20 × 20 mm^{2}.
Fig. 4 Variation of (a) output power and (b)
efficiency of PV module with different concentration ratios for T_{c}=20
°C and n=4.
3. Results and
Discussion
The hybrid system performance is related to parameters
such as the solar irradiation, concentration ratio, and temperatures of the hot
and cold sides of the TEG. Fig. 4 shows the
variation of the output power and efficiency of different PV modules with
different concentration ratios. It is clear from this figure that the power
output increases with increasing concentration ratio. The output power of the gallium
arsenide solar cell is much higher than that of the monocrystalline silicon
cell. The output power and efficiency of the gallium arsenide PV module are
6.25 times and 6.26 times higher than those of the monocrystalline silicon PV
module, respectively, at C=20 and T_{c}=20 °C. However,
the efficiency of the gallium arsenide solar cell for C=5 reaches a maximum and
then decreases with further increase in the concentration ratio, and that of the
monocrystalline silicon cell decreases with increasing concentration ratio. Therefore,
there is an optimum concentration ratio at which output power or efficiency is
maximum.
Fig. 5 Variation
of output power and efficiency of TEG with concentration ratios at different values
of n for T_{c}=20 °C. (a) and (c) are the output power and efficiency
of the TEG with a monocrystalline silicon PV module. (b) and (d) are the
output power and efficiency of a TEG with a gallium arsenide PV module.
We considered the cell area differences between the PV and
TEG devices and their effect on the performance of the hybrid system. TEGs with
different areas have different numbers of thermoelement pairs within them,
which is one of the factors that affect output power and efficiency. In
addition, when the heat transferred to the TEG through the PV solar cell is the
same, a larger TEG area reduces the average temperature difference between the
hot and cold sides of the TEG, which decreases the output and efficiency of the
TEG in the hybrid system. Fig. 5 shows the
influence of solar radiation and the value of n on TEG performance with T_{c}=20
°C. It is clear from Fig. 5 (a) and (b) that the output power of the TEG first increases
and then decreases with increasing n, but always increases with increasing
solar radiation. The output power and efficiency of the TEG
increase with increasing concentration ratio because more of the incident solar
radiation is converted into heat, which increases the temperature of the hot
side of the TEG. However, increasing the size
of the TEG module decreases the temperature difference between its hot and cold
sides caused by the heat conduction through the PV module, which is the main cause
of the decline in output power and efficiency.
Fig. 6 shows the variation in output power and efficiency with
concentration ratio and cooling temperature for n=4. The variation in output
power of the TEG with concentration ratio and T_{c} is similar to that of efficiency for both the monocrystalline
silicon and gallium arsenide PV hybrid systems. The output power of the TEG
decreases with increasing system cooling temperature. The reason is the same as
explained earlier, that lower cooling temperature results in a larger temperature
difference between the hot and cold sides of the TEG. In the monocrystalline
silicon PV hybrid system, the output power and efficiency of the TEG at T_{c}=5
°C are 1.2 times and 1.13 times higher, respectively, than at T_{c}=25 °C,
and correspondingly 1.11 times and 1.17 times higher in the gallium arsenide PV
system. The output power and efficiency of the TEG with the gallium arsenide PV
system at T_{c}=5 °C are 1.19 times and 1.16 times higher than those of
the monocrystalline silicon PV hybrid system. It is obvious that a gallium
arsenide solar cell in a hybrid system can achieve greater output power and
efficiency than those of monocrystalline silicon.
Fig. 6 Variation of output power
and efficiency of TEG with cooling temperature at different concentration
ratios for n=4. (a) and (c) are the output power and efficiency of the TEG with
the monocrystalline silicon PV module. (b) and (d) are the output power and
efficiency of the TEG with the gallium arsenide PV module.
Fig.
7 Comparison of output power and efficiency of TEG, PV and hybrid system as functions with different
concentration ratios for T_{c}=5 °C and n=4. (a)
and (c) are the output power and efficiency of hybrid system which the TEG with
the monocrystalline silicon PV module. (b) and (d) are the output power and
efficiency of hybrid system which the TEG with the gallium arsenide PV module.
We plot the efficiencies and
output powers of the PV module, TEG, and hybrid system as functions of the
concentration ratio in Fig. 7, where n=4 and T_{c}=5
°C. However, the electricity generated by the hybrid system is slightly higher
than that by the PV owing to the additional contribution of the TEG to the
whole generation power. However, the operating temperature of the PV is lower at
n=4 than at n=1 because the larger TEG area at n=4 dissipates more heat. This
is a factor that makes the total conversion efficiency of the hybrid system greater
than that of a bare PV module. The contribution of the TEG power output to the
overall power output of the hybrid system is much less than that of a PV,
especially the gallium arsenide PV. Fig. 7(d)
shows that the efficiency at the maximum power output first increases and then
decreases as CG is increased. This
clearly shows that there is an optimum value for CG at which η_{o} reaches
its maximum. As a result of the tradeoff between the PV and TEG performances
with different TEG areas in the hybrid system, a maximum total conversion
efficiency ratio of 32.2% was achieved with a TEG area of 20 × 20 mm^{2}
in the gallium arsenide PV hybrid system.
Fig. 8 shows the resulting optimization of the system component size
using machine learning. The experimental results show that as n varies, the
output power of the TEG reaches a maximum under the same conditions (e.g., concentration ratio, cooling
temperature) predicted by machine learning. The maximum output power of the TEG
is obtained with n=4.41. Therefore, the system can achieve maximum output power
when the ratio of the PV and TEG areas in the hybrid system is 4.41 because the
TEG performance is best at this value of n.
Fig. 8 Optimization of system component size based on machine learning.
4. Conclusion
In this work, we analyzed the factors that affect the
performance of a CPVTEG hybrid system and used experiment and machine learning
to find the structural parameters that optimize the output power of the system.
The results showed that the maximum output power of this hybrid system can be
obtained under the condition A_{TEG}/A_{PV}=4.41. Our research
provides a reference for structural optimization of CPVTEG hybrid systems.
Acknowledgements
This work was supported by the Basic Science Center
Program for Ordered Energy Conversion of the National Natural Science
Foundation of China (No. 51888103), the CAS Pioneer Hundred
Talents Program, and the Beijing Natural Science Foundation (No. 4192044).
Supporting information
Not applicable
Conflict of interest
There are no conflicts to declare.
References
[1] N.L. Panwar, S.C. Kaushik, and S. Kothari, Renew. Sust. Energ. Rev., 2011, 15,
15131524, doi: 10.1016/j.rser.2010.11.037.
[2] N.
Armaroli and V. Balzani, Angew. Chem., 2007, 46, 5266, doi: 10.1002/anie.200602373.
[3] T.T.
Chow, Appl.
Energ., 2010, 87, 365379, doi: 10.1016/j.apenergy.2009.06.037.
[4] H.
L. Zhang, J. Baeyens, J. Degrève, and G. Cacères, Renew. Sust. Energ. Rev., 2013, 22,
466481, doi: 10.1016/j.rser.2013.01.032.
[5] Z.S.
Xu, M.M. Meyers, B.G. Sammakia, B.T. Murray, J. Electron. Packag., 2014, 136,
041004, doi: 10.1115/1.4028060.
[6] Z.
Jiang, R. Li, S.C. Zhang, and W. Liu, Phys. Rev. B, 2005, 72, doi: 10.1103/PhysRevB.72.045201.
[7] L.C.
Hirst, and N.J. EkinsDaukes, Fundamental losses in solar cells, Prog. Photovoltaics, 2011, 19, 286293, doi:
10.1002/pip.1024.
[8] W. Lai, Y.Q. Ma, L. Zhuang, and
W.M. Liu, Phys. Rev. Lett., 2019, 122,
223202, doi: 10.1103/PhysRevLett.122.223202.
[9] L. Zhang, L.F. Liu, and W.M.
Liu, Sci. RepUK.,
2013, 3, 2908, doi: 10.1038/srep02908.
[10] E. Skoplaki and J.A. Palyvos, Sol. Energy, 2009, 83, 614624, doi: 10.1016/j.solener.2008.10.008.
[11] Y.X. Zhen, M. Yang, H. Zhang, G.S. Fu, J.L. Wang, S.F. Wang, and
R.N. Wang, Sci.
Bull., 2017, 62, 15301537, doi: 10.1016/j.scib.2017.10.022.
[12] L. Tayebi, Z. Zamanipour, and D. Vashaee, Renew. Energ., 2014, 69,
166173, doi: 10.1016/j.renene.2014.02.055.
[13] Y. Deng, W. Zhu, Y. Wang, and Y. Shi, Sol. Energy, 2013, 88,
182191, doi: 10.1016/j.solener.2012.12.002.
[14] A. Pereira, T. Caroff, G. Lorin, T. Baffie, K. Romanjek, S. Vesin,
K. Kusiaku, H. Duchemin, V. Salvador, N. MiloudAli, L. Aixala, and J. Simon, Energy,
2015, 84, 485492, doi: 10.1016/j.energy.2015.03.053.
[15] Y.X. Zhen, M. Yang, and R.N. Wang, Front. PhysBeijing, 2018, 14,
doi: 10.1007/s1146701808650.
[16] Z. He, M.X. Foo, D. Yong, T. Ma, Y. Hao, H. Zhang, and D. Ding, ES Energy Environ.,
2019, doi: 10.30919/esee8c335.
[17] Y.H. Chen, H.S. Tao, D.X. Yao, and W.M. Liu, Phys. Rev. Lett., 2012, 108,
246402, doi: 10.1103/PhysRevLett.108.246402.
[18] Y. Li, S. Witharana, H. Cao, M. Lasfargues, Y. Huang, and Y. Ding, Particuology,
2014, 15, 3944, doi: 10.1016/j.partic.2013.08.003.
[19] W. G. J. H. M. v. Sark, Appl. Energ., 2011, 88,
27852790, doi: 10.1016/j.apenergy.2011.02.008.
[20] R. Lamba and S.C. Kaushik, Energ. Convers.
Manage., 2016, 115, 288298, doi: 10.1016/j.enconman.2016.02.061.
[21] S. Mahmoudinezhad, A. Rezania, and L. A. Rosendahl, Energ. Convers. Manage.,
2018, 164, 443452, doi: 10.1016/j.enconman.2018.03.025.
[22] S. Mahmoudinezhad, S.W. Qing, A. Rezaniakolaei, L. A. Rosendahl, Energ. Procedia,
2017, 142, 564569.doi: 10.1016/j.egypro.2017.12.088.
[23] A. Rezania and L.A. Rosendahl, Appl. Energ., 2017, 187, 380389, doi: 10.1016/j.apenergy.2016.11.064.
[24] S.Y. Wu, Y.C. Zhang, L. Xiao, and Z.G. Shen, J. Sust. Energ., 2017, 37,
533548, doi: 10.1080/14786451.2017.1345906.
[25] D. Kraemer, L. Hu, A. Muto, X. Chen, G. Chen, and M. Chiesa, Appl. Phys. Lett.,
2008, 92, 243503, doi: 10.1063/1.2947591.
[26] W. Zhu, Y. Deng, Y. Wang, S. Shen, and R. Gulfam, Energy,
2016, 100, 91101, doi: 10.1016/j.energy.2016.01.055.
[27] T. Liao, B. Lin, and Z. Yang, Int. J. Therm. Sci., 2014, 77, 158164, doi:
10.1016/j.ijthermalsci.2013.10.013.
[28] P. Baldi, P. Sadowski, and D. Whiteson, Nat. Commun., 2014, 5, doi: 10.1038/ncomms5308.
[29] L. Ti, Adv. Cond. Matter. Phys., 2020, 09, 1119,
doi: 10.12677/cmp.2020.92002.
[30] Z.H. Zheng, X.D. Lin, M. Yang, Z.M. He, E. Bao, H. Zhang, and Z.Y.
Tian, ES Energy
Environ., 2020, doi: 10.30919/esee8c795.
[31] J. Zhang and Y. Xuan, Energy, 2019, 181, 387394, doi: 10.1016/j.energy.2019.05.155.
[32] K.T. Park, S.M. Shin, A.S. Tazebay, H.D. Um, J.Y. Jung, S.W. Jee,
M.W. Oh, S.D. Park, B. Yoo, C. Yu, and J. H. Lee, Sci. RepUK., 2013, 3, doi: 10.1038/srep02123.
[33] C. Lertsatitthanakorn, S. Soponronnarit, J. Jamradloedluk, M.
Rungsiyopas, and R. Sarachitti, J. Electron. Mater.,
2013, 43, 20402046, doi: 10.1007/s1166401329459.
[34] B. Zhu, Z.Y. Huang, X.Y. Wang, Y. YU, N. Gao, F.Q. Zu, Scripta Mater.,
2018, 146, 192195, doi: 10.1016/j.scriptamat.2017.11.045.
Author
information
Publisher’s
Note: Engineered Science Publisher remains neutral with regard to
jurisdictional claims in published maps and institutional affiliation