Advances in ionogels for proton-exchange membranes

Zhou, Yilin; Wang, Bei; Ling, Zhiwei; Liu, Qingting; Fu, Xudong; Zhang, Yanhua; Zhang, Rong; Hu, Shengfei; Zhao, Feng; Li, Xiao; Bao, Xujin; Yang, Jun

doi:10.1016/j.scitotenv.2024.171099

Cited by 35 publications

(5 citation statements)

References 111 publications

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“…Table 3 shows the functions of each component, state of the art, and future trends. -PEM with ionogels [23,76,77] Catalyst layer Site of electrochemical reactions.…”

Section: Physical Insightmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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“…Table 3 shows the functions of each component, state of the art, and future trends. -PEM with ionogels [23,76,77] Catalyst layer Site of electrochemical reactions.…”

Section: Physical Insightmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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“…In a steady state, a polarization curve can show an association between a hydrogen cell's current supply and voltage density 48 . Variables influencing this curve include cell heat, hydrogen and oxygen partial pressures, and membrane water content 49 fuel cell parameters, including temperature (T) .…”

Section: Pemfc Modellingmentioning

confidence: 99%

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Bouguerra,

Badoud,

Mekhilef

et al. 2024

Sci Rep

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This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

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“…Zinc selenide (ZnSe) is known as a good basic material for etransportation and provides greater mechanical sustenance in heterostructure. Researchers have intensively studied it for electrode material applications [20,21]. Ye et al produced successfully a NiSe/ZnSe composite material utilizing an in-situ electrodeposition method.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Nitrogen-Doped Graphene in ZnSe-TiO2 Composite Materials for Supercapacitor Electrode

Akbar,

Ali,

Mohammad

et al. 2024

Molecules

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The current study explores the prospective of a nitrogen-doped graphene (NG) incorporated into ZnSe-TiO2 composites via hydrothermal method for supercapacitor electrodes. Structural, morphological, and electronic characterizations are conducted using XRD, SEM, Raman, and UV analyses. The electrochemical study is performed and galvanostatic charge-discharge (GCD) and cyclic voltammetry (CV) are evaluated for the supercapacitor electrode material. Results demonstrate improved performance in the ZnSe-NG-TiO2 composite, indicating its potential for advanced supercapacitors with enhanced efficiency, stability, and power density. Specific capacity calculations and galvanic charge-discharge experiments confirmed the promising electrochemical activity of ZnSe-NG-TiO2, which has a specific capacity of 222 C/g. The negative link among specific capacity and current density demonstrated the composite’s potential for high energy density and high-power density electrochemical devices. Overall, the study shows that composite materials derived from multiple families can synergistically improve electrode characteristics for advanced energy storage applications.

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Advances in ionogels for proton-exchange membranes

Cited by 35 publications

References 111 publications

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Exploring the Potential of Nitrogen-Doped Graphene in ZnSe-TiO2 Composite Materials for Supercapacitor Electrode

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