On the efficient simulation of electrical circuits with constant phase elements: The Warburg element as a test case

Athanasiou, Vasileios; Konkoli, Zoran

doi:10.1002/cta.2474

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…where Q 0 is the magnitude of the CPE (or its 'pseudo-capacitance'), n is the phase of the CPE, and Γ(α) is the gamma function [41]. For cyclic voltammetry, where it is the voltage that is controlled and the current that is measured, solving this involves expressing the electrical network as a differential equation.…”

Section: Methodsmentioning

confidence: 99%

Study of Activity and Super-Capacitance Exhibited by Bifunctional Raney 2.0 Catalyst for Alkaline Water-Splitting Electrolysis

Gannon

Dunnill

2020

Hydrogen

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Low-cost, high-performance coatings for hydrogen production via electrolytic water-splitting are of great importance for de-carbonising energy. In this study the Raney2.0 coating was analysed using various electrochemical techniques to assess its absolute performance, and it was confirmed to have an extremely low overpotential for hydrogen evolution of just 28 mV at 10 mA/cm2. It was also confirmed to be an acceptable catalyst for oxygen evolution, making it the highest performing simple bifunctional electrocatalyst known. The coating exhibits an extremely high capacitance of up to 1.7 F/cm2, as well as being able to store 0.61 J/cm2 in the form of temporary hydride deposits. A new technique is presented that performs a best-fit of a transient simulation of an equivalent circuit containing a constant phase element to cyclic voltammetry measurements. From this the roughness factor of the coating was calculated to be approximately 40,000, which is the highest figure ever reported for this type of material. The coating is therefore an extremely useful improved bifunctional coating for the continued roll-out of alkaline electrolysis for large-scale renewable energy capture via hydrogen production.

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Section: Methodsmentioning

confidence: 99%

Study of Activity and Super-Capacitance Exhibited by Bifunctional Raney 2.0 Catalyst for Alkaline Water-Splitting Electrolysis

Gannon

Dunnill

2020

Hydrogen

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“…Therefore, reservoirs are considered as more efficient information processing devices than neutral networks, their training and operation is, however, more challenging. 124,126,127) The dynamics of the internal reservoir state can be described with simple mathematical formulations. 123) In simplified terms, the reservoir evolves and is updated in discrete time steps t. It depends on two factors-its initial condition x t−1 and input signal u t , based on which current reservoir state x t can be obtained, giving (11):…”

Section: Memristive Reservoirsmentioning

confidence: 99%

Towards synthetic neural networks: can artificial electrochemical neurons be coupled with artificial memristive synapses?

Wlaźlak

Przyczyna

Gutiérrez

et al. 2020

Jpn. J. Appl. Phys.

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The enormous amount of data generated nowadays worldwide is increasingly triggering the search for unconventional and more efficient ways of processing and classifying information, eventually able to transcend the conventional von-Neumann-Turing computational central dogma. It is, therefore, greatly appealing to draw inspiration from less conventional but computationally more powerful systems such as the neural architecture of the human brain.This neuromorphic route has the potential to become one of the most influential and long-lasting paradigms in the field of unconventional computing. The material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the above mentioned von-Neumann-Turing prescription; we do know, however, that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons. This requires challenging the intrinsic definition of the single units and the architecture of computing machines. Memristive and the recently proposed memfractive systems have been shown to display basic features of neural systems such as synaptic-like plasticity and memory features, so that they may offer a diverse playground to implement synaptic connections. Their combination with reservoir computing approaches can further increase their versatility since reservoir networks do not require extra optimization of internal connections. In this review, we address various material-based strategies of implementing unconventional computing hardware: (i) electrochemical oscillators based on liquid metals and (ii) mem-devices exploiting Schottky barrier modulation in polycrystalline and disordered structures made of oxide or perovskite-type semiconductors. Both items (i) and (ii) build the two pillars of neuromimetic computing devices, which we will denote as synthetic neural networks. We complement the more experimental aspects of the review with an overview of few atomistic and phenomenological modelling approaches of memdevices as well as of reservoir computing networks. We expect that the current review will be of great interest for scientists aiming at bridging unconventional computing strategies with specific materials-based platforms.

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“…where Γ(α) is the gamma function [41]. It can thus be seen for an ideal capacitor (where α = 1) that (t−u) α−1 = Γ(α) = 1, and the equation simplifies to:…”

Section: S6 Constant Phase Element: Transient Simulationmentioning

confidence: 99%

Apparent disagreement between cyclic voltammetry and electrochemical impedance spectroscopy explained by time-domain simulation of constant phase elements

Gannon

Dunnill

2020

International Journal of Hydrogen Energy

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A selection of electrodes was analysed using cyclic-voltammetry (CV) and electrochemical impedance spectroscopy (EIS), and a large apparent resistance was observed with CV that was absent with EIS. The explanation for this resistance anomaly was traced to the constant phase element (CPE) behaviour which is exhibited by the electrode double-layer capacitance. Computer simulations of the transient-response of an RQ network (where Q represents a CPE) to a voltage ramp revealed bi-exponential behaviour, with two separate time-constants. One is equal to the product of R and Q, but the other is fixed at about 0.3 seconds. This finding is supported by observation, by mathematical derivation, and by a novel mixed-domain five-component equivalent circuit model. In addition, example code is provided as a basis for transient simulations of constant phase elements with arbitrary voltage waveforms. This explanation assists in the correct interpretation of potentially misleading cyclic voltammetry results.

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On the efficient simulation of electrical circuits with constant phase elements: The Warburg element as a test case

Cited by 5 publications

References 14 publications

Study of Activity and Super-Capacitance Exhibited by Bifunctional Raney 2.0 Catalyst for Alkaline Water-Splitting Electrolysis

Study of Activity and Super-Capacitance Exhibited by Bifunctional Raney 2.0 Catalyst for Alkaline Water-Splitting Electrolysis

Towards synthetic neural networks: can artificial electrochemical neurons be coupled with artificial memristive synapses?

Apparent disagreement between cyclic voltammetry and electrochemical impedance spectroscopy explained by time-domain simulation of constant phase elements

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