A Review: Ion Transport of Two-Dimensional Materials in Novel Technologies from Macro to Nanoscopic Perspectives

Unsuree, Nawapong; Phanphak, Sorasak; Prajongtat, Pongthep; Bunpheng, Aritsa; Jitapunkul, Kulpavee; Kongputhon, Pornpis; Srinoi, Pannaree; Iamprasertkun, Pawin; Hirunpinyopas, Wisit

doi:10.3390/en14185819

Cited by 10 publications

(8 citation statements)

References 162 publications

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“…This is because the charge storage mechanism in graphene-based supercapacitors depends on physisorption, and increasing the SA results in a higher interfacial CAP (contributed by C H -Helmholtz CAPs shown in eq ). ,, C normalH = ε 0 ε normalr S d …”

Section: Resultsmentioning

confidence: 99%

“…Since all applied CONCs in the device are above 0.1 M, the effect of the diffuse layer (Gouy−Chapman) can be disregarded. 75 Most of the CONCs exhibit a positive SHAP value, which benefits the predicted CAP, except for 6.0 M, which shows a negative impact on the prediction. This is not related to any specific CONC but rather the type of electrolyte used.…”

Section: ■ Introductionmentioning

confidence: 99%

“…charge separation length (d), which can be estimated by the Debye length. 75,76 Therefore, the more SA there is in the graphene, the greater the CAP becomes. However, it is found that most of the data in this study exhibit SAs between 100 and 500 m 2 g −1 .…”

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: Resultsmentioning

confidence: 99%

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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“…The basal plane provides a small value of interfacial capacitance of about 4 μF cm −2 , while the edge plane shows much higher orders of magnitude up to ×1.0 10 5 μF cm −2 . This intrinsic capacitance can be used for determining the capacitance of the macro level (Unsuree et al, 2021). For example, the proposed theoretical capacitance of graphene, which is quoted to be 550 F g −1 (~21 μF cm −2 , in areal basis (Liu et al, 2010;Sawangphruk et al, 2013); however, this is not valid for most of case.…”

Section: Capacitance Of Two-dimensional Materialsmentioning

confidence: 99%

“…This is because the capacitance from the original work by Xia et al (2009) measured the capacitance using a platinum electrode on behalf of graphene, assuming the equivalent interfacial properties between graphene and platinum, ignoring the nanoscale quantum capacitance (known as space charge capacitance for higher dimension materials In fact, the pristine graphene shows far beyond those theoretical approach providing less than half of theoretical value (Wang et al, 2009). This is due to the Helmholtz, diffuse layer, quantum, as well as space charge capacitance of those two-dimensional materials, which can be varied by the operating conditions (Unsuree et al, 2021). Our group also demonstrates that the basal plane capacitance depends on the hydrated ionic size showing the basal plane capacitance of 4.7-9.4 μF cm −2 while the capacitance follows the following order Li + < Na + < K + < Rb + < Cs + (Iamprasertkun et al, 2019).…”

Section: Capacitance Of Two-dimensional Materialsmentioning

confidence: 99%

Transition of electrochemical measurement to machine learning in the perspective of two-dimensional materials

2022

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Two-dimensional materials (e.g. graphene, and transition metal dichalcogenides) have become ubiquitous in electrochemical contexts including energy storage, electrocatalyst, and ion-selective membranes. This is due to its superior electrochemical properties, specifically “capacitance”, which can be referred to the storage ions at the electrolyte/materials interfaces. Experimental work and computational chemistry were carried out in the past decade for solving and improving the understanding of two-dimensional materials; however, these techniques are relatively expensive, complex, and time-consuming. Therefore, we accentuate the future trend of two-dimensional material study with machine learning as the modest alternative. In this perspective, the intrinsic capacitance properties of the two dimension materials were described from an atomic level, explaining the heteroatom doping to a nanoscopic level, showing (basal vs edge capacitance). The studies also extended to the macroscopic level i.e., the flake size of the two-dimensional materials. We then shed more light on the applicability of machine learning coupled with the “fundamental measurement” for solving electrochemistry of two-dimensional materials. The shallow artificial neural network was demonstrated for the prediction of CV curves using the data from size-dependent graphene. In addition, the application of deep neural networks with complicated architecture has also been explored through the prediction of capacitance for heteroatom-doped graphene. This perspective provides a clear background and creates the connection between fundamental measurement and machine learning for understanding the capacitance properties of two-dimensional materials.

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Carbon-based nanomaterial intervention and efficient removal of various contaminants from effluents – A review

et al. 2023

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A Review: Ion Transport of Two-Dimensional Materials in Novel Technologies from Macro to Nanoscopic Perspectives

Cited by 10 publications

References 162 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

Transition of electrochemical measurement to machine learning in the perspective of two-dimensional materials

Carbon-based nanomaterial intervention and efficient removal of various contaminants from effluents – A review

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