Pseudocapacitive deionization with polypyrrole grafted CMC carbon aerogel electrodes

Miao, Luwei; Wang, Zhen; Peng, Jie; Deng, Wenyang; Chen, Wenqing; Dai, Qizhou; Ueyama, Tetsuro

doi:10.1016/j.seppur.2022.121441

Cited by 33 publications

(1 citation statement)

References 68 publications

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“…So far, carbon materials, featuring a range of porous structures, have found application as CDI electrode materials, becoming the main source of electrode materials in CDI system [49][50][51]. Examples include activated carbon (AC) [52], carbon aerogel (CA) [53], activated carbon fiber (ACF) [54], carbon nanotubes (CNT) and graphene [55,56]. Despite the drawbacks of carbon materials, such as limited specific surface area and reduced stability, composite materials that combine the advantages of different materials have emerged as a research hotspot in CDI electrodes.…”

Section: Grey Relational Analysis In Filtering Characteristic Variablesmentioning

confidence: 99%

Heavy metal removal performance of capacitive deionization technology studied by machine learning

Dian,

Hao,

Zhang

et al. 2024

Eng. Res. Express

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Capacitive deionization (CDI) technology is utilized for efficient treatment of industrial wastewater, characterized by low energy consumption and environmental protection. In order to comprehend the correlation between key experimental parameters and the electrosorption capacity (EC) of heavy metals in CDI technology, this paper employs a genetic algorithm (GA) to optimize a backpropagation artificial neural network (BPANN) for predicting the EC of CDI technology for heavy metal ions, with the characteristics of electrode materials converted into numerical characteristics for further analysis. Compared to the BPANN, the optimized GABPANN model demonstrates superior predictive accuracy. It achieves automatic adjustment of the hidden layer structure, neuron count, and transfer functions. Furthermore, the grey relational analysis indicates that the electrode material and the initial pH value of the solution are pivotal in determining the EC of heavy metal ions. This underscores the efficacy of machine learning (ML) algorithms in forecasting the nonlinear dynamics of CDI systems and elucidates the influence of individual parameters on the efficacy of heavy metal removal.

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Section: Grey Relational Analysis In Filtering Characteristic Variablesmentioning

confidence: 99%

Heavy metal removal performance of capacitive deionization technology studied by machine learning

Dian,

Hao,

Zhang

et al. 2024

Eng. Res. Express

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Titanium Carbon Oxide Flakes with Tunable Interlayer Spacing for Efficient Capacitive Deionization

Zhang,

Yi,

et al. 2024

Adv Funct Materials

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The environmental toxicity caused by heavy metal ions is always taken seriously. Capacitive deionization (CDI), as an emerging and effective treatment method, is widely studied. Among them, the rational design and development of efficient electrodes based on high theoretical capacitance, high conductivity, and high stability have great prospects for CDI applications. In this study, a series of 2D titanium carbide oxide flakes (2D TCOs) are synthesized using a cation‐giving modulation strategy, and the interlayer spacing of materials is successfully controlled by confining cations (tetraalkylammonium hydroxides, TAAH) with different radii between layers. Interestingly, these TCOs exhibit widely varying electrochemical properties due to their different interlayer spacings. Among them, titanium carbide oxide flakes‐tetrapropylammonium hydroxide (TCOs‐TPAH) with the maximum interlayer spacing exhibits the best performance in sodium ion embedding and de‐embedding, with a capacity of 53.11 mg g−1. In addition, the capacitive removal efficiency of heavy metal ions (Fe3+, Cr3+, Cu2+, Cd2+, Co2+, Ni2+, and Pb2+) can reach over 97% within 2 h. This work presents a simple and creative strategy for the bottom‐up synthesis of oversized 2D nanomaterials, thus supporting the further development of high‐performance 2D materials in the fields of environmental and energy applications.

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Utilization of peanut shells for the fabrication of high-performance activated carbon electrodes in capacitive deionization

Yan,

Deng,

et al. 2023

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Pseudocapacitive deionization with polypyrrole grafted CMC carbon aerogel electrodes

Cited by 33 publications

References 68 publications

Heavy metal removal performance of capacitive deionization technology studied by machine learning

Heavy metal removal performance of capacitive deionization technology studied by machine learning

Titanium Carbon Oxide Flakes with Tunable Interlayer Spacing for Efficient Capacitive Deionization

Utilization of peanut shells for the fabrication of high-performance activated carbon electrodes in capacitive deionization

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