Facile and controllable synthesis N-doping porous Graphene for high-performance Supercapacitor

He, Xiuxian; Tang, Zheng; Gao, Lianlian; Wang, Fangyuan; Zhao, Jinping; Miao, Zhichao; Wu, Xiaozhong; Zhou, Jin; Su, Yang; Zhuo, Shuping

doi:10.1016/j.jelechem.2020.114311

Cited by 19 publications

(7 citation statements)

References 49 publications

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“…The data were collected from multiple literature and compiled into a CSV file (available in Supporting Information .CSV and their source in Table S1), with references to the sources provided ,,,− ,− and our previous work. , The collected information includes parameters such as SA, DG, percentage of nitrogen dopant (% N), oxygen dopant (% O), sulfur dopant (% S), current density (CD), electrolyte concentration (CONC), and CAP. In the case of missing data (e.g., DG), the K -Nearest Neighbors imputation (KNN imputation) will be used to fill in the gaps.…”

Section: Methodsmentioning

confidence: 99%

“…Knowing the optimal doping condition is crucial to enhance the capacitive properties of graphene-based supercapacitors and can reduce experimental time, this could be done by employing data analysis and machine learning. , This approach can avoid the random synthesis procedure, which results in a randomly doped content without knowing the final CAP properties. Hence, the understanding of graphene doping can be done within a single click instead of performing a traditional synthesis method, which require almost a week. ,, By utilizing the vast experimental data available in the literature, ,,,− the optimum doping condition can be determined to avoid trial-and-error doping and achieve superior CAP. Additionally, this can lead to cost reduction and energy efficiency, as high temperatures are required for some synthesis method, e.g., chemical vapor deposition .…”

Section: Introductionmentioning

confidence: 99%

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Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

Deshsorn,

Payakkachon,

Chaisrithong

et al. 2023

J. Chem. Inf. Model.

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Graphene-based supercapacitors have emerged as a promising candidate for energy storage due to their superior capacitive properties. Heteroatom-doping is a method of improving the capacitive properties of graphene-based electrodes, but the optimal doping conditions and electrochemical properties are not yet fully understood due to the synergistic effects that occur. Many parameters, such as doping content, defects, specific surface area (SA), electrolyte, and more, could affect the capacitance (CAP). In this study, we use machine learning to solve these critical issues. We applied many models, such as Light Gradient Boost Machine, Extreme Gradient Boost, Polynomial Regression, Neural Network, Elastic Net, Lasso Regression, Ridge Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boost, AdaBoost, and Decision Tree, to find a suitable model for CAP prediction. Moreover, we enhance the prediction result by taking advantage of the top candidate model and creating a stacking concept (called “stacking models”). The SHAP value was used to identify the range of properties that affect CAP, and it was discussed in detail. Our results suggest that high-CAP graphene supercapacitors should have a large SA, with 4–5% nitrogen, 10–15% oxygen, high percentages of sulfur, a defect ratio close to 1, with acid electrolyte, and a low current density. These findings, along with the developed model and code, are expected to serve as a valuable computational tool for future electrochemical research from fundamental to applications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

Deshsorn,

Payakkachon,

Chaisrithong

et al. 2023

J. Chem. Inf. Model.

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“…[23] However, the potential window of GFSCs using PVA/H 2 SO 4 gel-electrolyte was limited to 1 V which also restricted the specific capacitance improvement of GFSCs. Thus, it is still a big challenge to improve the energy density of GFSCs only through increasing the specific surface area of the electrode material such as porous graphene [24] and hybrid graphene. [25] Wang et al prepared high-energy-density RuO 2loaded PEDOT fibers whose voltage window reaches 1.6 V while the fiber strengthen was only 123 MPa, which was extremely difficult to be weaved into 2D textile.…”

Section: Introductionmentioning

confidence: 99%

High‐Energy‐Density Graphene Hybrid Flexible Fiber Supercapacitors

Zhou

Guan

zhao

et al. 2023

Batteries & Supercaps

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Graphene fiber-based supercapacitors (GFSCs) as flexible energy-storage devices have achieved much in the wearable electronics. However, the GFSCs still suffer insufficient energy density, and of which desirable balance between mechanical and electrochemical properties has not been realized. Herein, we developed AgI and conductive polymer assisted coenhancement strategy to prepare the high-performance GFSCs with super-high energy density, favorable mechanical properties, and superior electrochemical properties. The conductive binder polymer, poly(3,4-ethylenedioxythiophene) (PEDOT) was introduced into the GO spinning solution to enhance RGO fiber strength, conductivity, and electrochemical performance by forming a strong interface with GO in the spinning solution. On the other hand, the synergistic effect of micro-and nano-scale AgI grown on the fiber surface and aqueous gel electrolyte widen the potential window of the GFSC to 1.6 V, which is much higher than that of reported GFSCs, and resulting in a significantly increased energy density. At 0.1 A cm À 3 , the volumetric capacitance and energy density of the fabricated GFSCs are as high as 166.6 F cm À 3 and 29.65 mWh cm À 3 , respectively. The high strength and flexibility of the hybrid fibers endowed the GFSCs with ~100 % capacitance retention when bent to 180°. This work opens a new way to design and fabricate high-performance GFSCs.

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“…The motivation to design such material depends on the improvement of specific capacity by porous morphology and active sites by iodine/nitrogen co‐doping. P‐type doping of iodine can form iodine negative ions on the surface of graphene through the charge transfer process 21 and nitrogen doping can improve the charge environment on the surface of carbon materials, 22 which could both increase the density of positron cloud on the surface of graphene and improve the electrochemical properties of graphene. The influence of reaction conditions on capacitive performance was discussed.…”

Section: Introductionmentioning

confidence: 99%

Fabrication of iodine/nitrogen co‐doped three‐dimensional porous reduced graphene oxide for high energy density supercapacitors

Xue

Zhang²,

Liu

et al. 2022

Intl J of Energy Research

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Summary As an electrode for supercapacitor, the gravimetric specific capacitance of two‐dimensional layered graphene is theoretically superior to general porous carbon materials. However, the relatively low porosity and volumetric energy density of two‐dimensional graphene‐based electrode materials remain challenging for its practical application. Herein, iodine/nitrogen co‐doped three‐dimensional reduced graphene oxide (PGr) synthesized through the socking‐drying process and subsequent calcination is reported. In the presence of NH4I and KI, the obtained PGr containing 2.9 wt.% of I element and 3.15 wt.% of N element possesses highly interconnected three‐dimensional hierarchically porous network among the layered structure and possesses a high specific surface area of 622.5 m2·g−1. Moreover, the synthesized PGr exhibits exceptional electrical conductivity and excellent capacitive characteristics, delivering 268 F·g−1 of high gravimetric specific capacitance in a three‐electrode system in KOH (6.0 mol·L−1). Furthermore, symmetric supercapacitor assembled from the synthesized PGr shows 9.46 Wh·kg−1 of specific energy density when the power density is 600 W·kg−1 and its initial capacitance retains 97.5% after 10 000 cycles of charging and discharging at 10 A·g−1. In addition, the device utilizing ionic liquid as electrolyte delivers 32.9 and 46.2 Wh·kg−1 of specific energy density under 1.35 kW·kg−1 at 25°C and 1.85 kW·kg−1 at −50°C, respectively.

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Facile and controllable synthesis N-doping porous Graphene for high-performance Supercapacitor

Cited by 19 publications

References 49 publications

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

High‐Energy‐Density Graphene Hybrid Flexible Fiber Supercapacitors

Fabrication of iodine/nitrogen co‐doped three‐dimensional porous reduced graphene oxide for high energy density supercapacitors

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