Functionality of an oxygen Ca3Co4O9+δ electrode for reversible solid oxide electrochemical cells based on proton-conducting electrolytes

Pikalova, E. Yu.; Kolchugin, A.А.; Королева, М. С.; Vdovin, G. K.; Фарленков, А. С.; Medvedev, Dmitry

doi:10.1016/j.jpowsour.2019.226996

Cited by 51 publications

(24 citation statements)

References 72 publications

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“…Explicitly, if we define re ( , ) = Re( ( , )) = Therefore, if we can estimate (log ) from either re ( ) or im ( ), then we will be able to obtain im ( ) or re ( ), respectively, thanks to (8). We must stress that, in the context of this article, we will not aim to assign any physical meaning to the (latent) function (log ).…”

Section: +mentioning

confidence: 99%

“…Once the | exp is estimated, we can use (8) to compute H ( ⋆ ), either − im ( ⋆ ) or re ( ⋆ ), at a new angular frequency ⋆ as…”

Section: Bayesian Hilbert Transformmentioning

confidence: 99%

“…Electrochemical impedance spectroscopy (EIS) is one of the most important and versatile techniques of electrochemistry. [1] EIS has been used widely in the fields of energy storage, [2,3] solid-state ionics, [4,5] fuel cells, [6,7] electrolyzers, [8] solar cells, [9,10] porous media, [11] sensors, [12] biology, [13] virological diagnostics, [14] and medicine. [15,16] The EIS technique is particularly appreciated because it can be carried out for frequencies spanning several orders of magnitude, typically from 1 mHz to 10 MHz.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores

Liu

Wan

Ciucci

2020

Electrochimica Acta

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Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers-Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.

show abstract

Section: +mentioning

confidence: 99%

“…Once the | exp is estimated, we can use (8) to compute H ( ⋆ ), either − im ( ⋆ ) or re ( ⋆ ), at a new angular frequency ⋆ as…”

Section: Bayesian Hilbert Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores

Liu

Wan

Ciucci

2020

Electrochimica Acta

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show abstract

“…Therefore, if we can estimate (log ) from re ( ) or im ( ), then we will be able to obtain im ( ) or re ( ), respectively, thanks to (8). We must stress that, in the context of this article, we will not aim to assign any physical meaning to the (latent) function (log ).…”

Section: +mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

Liu

Wan²,

Ciucci³

2020

Preprint

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<p>Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.<br></p>

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Roadmap for Sustainable Mixed Ionic‐Electronic Conducting Membranes

Chen

Feldhoff

Weidenkaff

et al. 2021

Adv Funct Materials

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Mixed ionic-electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H 2 and O 2 production, CO 2 reduction, O 2 and H 2 separation, CO 2 separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar-driven evaporation and energy-saving regeneration as well as electrolyzer cells for powerto-X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state-of-the-art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry-oriented research toward commercialization of MIEC membranes for different applications.

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Functionality of an oxygen Ca3Co4O9+δ electrode for reversible solid oxide electrochemical cells based on proton-conducting electrolytes

Cited by 51 publications

References 72 publications

A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores

A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

Roadmap for Sustainable Mixed Ionic‐Electronic Conducting Membranes

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