Construction of highly ordered ZnO microrod@SnO2 nanowire heterojunction hybrid with a test-tube brush-like structure for high performance lithium-ion batteries: experimental and theoretical study

Yan, Junfeng; Xu, Panpan; Chen, Sifan; Wang, Gang; Zhang, Fuchun; Zhao, Wenguang; Zhang, Zhiyong; Xu, Manzhang; Yun, Jiangni; Zhang, Yunyao

doi:10.1016/j.electacta.2019.135312

Cited by 33 publications

(25 citation statements)

References 35 publications

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“…The TEM image of the Ag3PO4/SnO2/CC is shown in Figure 4a, which clearly indicates that the Ag3PO4 particles are uniformly distributed on SnO2 nanorods. The HRTEM image of Ag3PO4/SnO2/CC is shown in Figure 4b, which clearly uncovers a set of fringes with an interplanar spacing of 0.246 nm ascribed to the (211) plane of Ag3PO4, and the other lattice fringe of 0.336 nm consistent with the (110) plane of SnO2 [48,51]. The results above confirm the formation of the Ag3PO4/SnO2 heterojunction.…”

Section: Microstructure and Morphologysupporting

confidence: 59%

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Construction of Ag3PO4/SnO2 Heterojunction on Carbon Cloth with Enhanced Visible Light Photocatalytic Degradation

Liu

Wang

et al. 2020

Applied Sciences

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In this study, the Ag3PO4/SnO2 heterojunction on carbon cloth (Ag3PO4/SnO2/CC) was successfully fabricated via a facile two-step process. The results showed that the Ag3PO4/SnO2/CC heterojunction exhibited a remarkable photocatalytic performance for the degradation of Rhodamine B (RhB) and methylene blue (MB), under visible light irradiation. The calculated k values for the degradation of RhB and MB over Ag3PO4/SnO2/CC are 0.04716 min−1 and 0.04916 min−1, which are higher than those calculated for the reactions over Ag3PO4/SnO2, Ag3PO4/CC and SnO2/CC, respectively. The enhanced photocatalytic activity could mainly be attributed to the improved separation efficiency of photogenerated electron-hole pairs, after the formation of the Ag3PO4/SnO2/CC heterojunction. Moreover, carbon cloth with a large specific surface area and excellent conductivity was used as the substrate, which helped to increase the contact area of dye solution with photocatalysts and the rapid transfer of photogenerated electrons. Notably, when compared with the powder catalyst, the catalysts supported on carbon cloth are easier to quickly recycle from the pollutant solution, thereby reducing the probability of recontamination.

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Section: Microstructure and Morphologysupporting

confidence: 59%

“…41-1445), Ag 3 PO 4 (JCPDS card No. 06-0505) and CC [48,49]. No diffraction peaks belonging to the impurity phases were detected.…”

Section: Microstructure and Morphologymentioning

confidence: 92%

Construction of Ag3PO4/SnO2 Heterojunction on Carbon Cloth with Enhanced Visible Light Photocatalytic Degradation

Liu

Wang

et al. 2020

Applied Sciences

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“…Electric vehicles (EVs) are attracting more attention around the world. Due to their high energy density, long service life, and low environmental pollution, Li-ion batteries (LIBs) have been widely applied as a power supply for EVs (Li et al, 2020c;Yan et al, 2020). EV batteries must have high reliability, which will have high economic value and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

et al. 2022

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With the development of cloud and edge computing, data-driven methods for estimating a Li-ion battery’s state of health are becoming increasingly attractive. However, existing data-driven estimation methods have problems of low accuracy and weak robustness that need to be solved. Focusing on these points, this paper proposes a parallel attention network combining multivariate time series to extract the mapping relationship between the selected health features and the state of health. First, multivariate time series are extracted, which can describe battery aging characteristics at different scales. Then, a novel parallel learning framework is designed by integrating long short-term memory cells and an attention mechanism, which can make full use of the health features and help to solve the challenging issues of estimation accuracy and robustness. Experimental results show that the proposed model is able to obtain estimation results for different batteries with a mean absolute percentage error of less than 1%. Compared with existing methods, the maximum error of the proposed model is 38% lower on average. Furthermore, even under measurement noise injections of 50 dB, a preferable estimation result (maximum error of 3.36%) can still be achieved.

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“…Power battery of the EV, being its single source of energy, is the most important part that influences the performance of the EV 4‐6 . The most significant type of battery, which may be considered as a key‐shift toward EVs, is lithium‐ion batteries (LIBs), due to their high operational voltage, specific energy, light weight, compact size, longer life cycle, and lower idle‐state discharge rate 7‐10 . Significant efforts have been made toward the development of heat‐optimization of the battery pack, new materials for electrode and various modeling‐based estimation/prediction methods to evaluate state of health (SoH) and remaining useful life (RUL) of LIBs 11‐15 .…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6] The most significant type of battery, which may be considered as a keyshift toward EVs, is lithium-ion batteries (LIBs), due to their high operational voltage, specific energy, light weight, compact size, longer life cycle, and lower idlestate discharge rate. [7][8][9][10] Significant efforts have been made toward the development of heat-optimization of the battery pack, new materials for electrode and various modeling-based estimation/prediction methods to evaluate state of health (SoH) and remaining useful life (RUL) of LIBs. [11][12][13][14][15] RUL and SoH are paramount in making efficient use of LIBs as these act as determining criteria for their reuse, disposal, and material recovery.…”

Section: Introductionmentioning

confidence: 99%

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

et al. 2020

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Summary Data‐driven modeling using measurable battery signals tends to provide robust battery capacity estimation without delving deep into electrochemical phenomenon inside the battery. Nowadays, with the advent of artificial intelligence, deep neural networks are playing crucial role in data modeling and analysis. In this article, models of three different families of network architectures such as feed‐forward neural network (FNN), convolutional neural network (CNN), and long short‐term memory neural network (LSTM) are proposed for battery capacity estimation. Measurements from a set of two rechargeable Li‐ion batteries are considered for the model performance evaluation. The battery capacity estimation by different models has been evaluated by considering the effect of certain parameters such as model complexity, sampling rate of battery measurable signals and type of battery measurable signals. With its ability to process time‐series data efficiently by memorizing long‐term dependencies, LSTM outperforms other model architectures in estimating battery capacity more accurately and flexibly with 4.69% and 19.16% decline in average test root mean square error (RMSE) as compared with FNN and CNN, respectively. Simpler architectures of LSTM and FNN are able to perform well as compared with CNN, which needs architecture with certain hidden layers to interpret the battery aging process. Moreover, investigations reveal that sparsely sampled battery signals help all the proposed models to learn the battery dynamics in a better way as compared to densely sampled battery signals which also entails for less complex model learning process. Further, among all battery measurable signals, battery temperature has relatively less weightage in estimating battery capacity.

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Construction of highly ordered ZnO microrod@SnO2 nanowire heterojunction hybrid with a test-tube brush-like structure for high performance lithium-ion batteries: experimental and theoretical study

Cited by 33 publications

References 35 publications

Construction of Ag3PO4/SnO2 Heterojunction on Carbon Cloth with Enhanced Visible Light Photocatalytic Degradation

Construction of Ag3PO4/SnO2 Heterojunction on Carbon Cloth with Enhanced Visible Light Photocatalytic Degradation

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

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