Impedance Spectroscopy and Neurocomputing Approaches to Investigate the Enhanced Electrical Blocking Properties of CH3(CH2)n−1SH Thin Monolayers Electrodeposited on a Gold Electrode

Hadjimi, Safia; Belayadi, Adel; Zabat, Mokhtar; Mougari, Ahmed; Khemici, M. W.

doi:10.1007/s11664-019-07151-4

Cited by 6 publications

(2 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of our recent works show the quantification of gold electrodes modified by different types of thiols. [19][20][21] Changes at the double layer of the electrode surface are detectable at low frequencies, [18,22] this requires large stabilization time of the harmonic cycles; hence the choice of the frequencies defining the experimental low frequencies range is crucial. The spontanious adsorption of self-assembled monolayer's (SAM's) of alkanethiolates on gold electrode has been the subject of numerous reports.…”

Section: Introductionmentioning

confidence: 99%

An Estimate of a Frequency Characterizing the Electrochemical Stability of a Gold Electrode Modified by MHDA Thiol in Different Ways

et al. 2021

Self Cite

View full text Add to dashboard Cite

A theoretical investigation aimed at estimating a characteristic frequency in the medium-low frequency domain in which the impedance response of a given interface measured by electrochemical impedance spectroscopy (EIS) is almost constant, constitutes the basic idea of this work. A theoretical model was subsequently applied to the data resulting from EIS measurements performed on gold electrodes modified by various ways of 16-mercaptohexadecanoic acid (MHDA) thiol functionalization. Analysis of these data revealed a direct relationship between the way the substrate was modified and this characteristic frequency.

show abstract

Section: Introductionmentioning

confidence: 99%

An Estimate of a Frequency Characterizing the Electrochemical Stability of a Gold Electrode Modified by MHDA Thiol in Different Ways

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…ML is used both to accelerate atomistic simulations with near DFT accuracy and to avoid atomistic simulations altogether by making predictions based on information extracted from either computational or experimental databases. ML models may also aid in interpreting interface characterization measurements, for example, by modeling the electrochemical response in impedance spectroscopy [25,26].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the modeling of interfaces in energy storage and conversion materials

Artrith

2019

J. Phys. Energy

View full text Add to dashboard Cite

The properties and atomic-scale dynamics of interfaces play an important role for the performance of energy storage and conversion devices such as batteries and fuel cells. In this topical review, we consider recent progress in machine-learning (ML) approaches for the computational modeling of materials interfaces. ML models are computationally much more efficient than first principles methods and thus allow to model larger systems and extended timescales, a necessary prerequisites for the accurate description of many interface properties. Here we review the recent major developments of ML-based interatomic potentials for atomistic modeling and ML approaches for the direct prediction of materials properties. This is followed by a discussion of ML applications to solid-gas, solid-liquid, and solid-solid interfaces as well as to nanostructured and amorphous phases that commonly form in interface regions. We then highlight how ML has been used to obtain important insights into the structure and stability of interfaces, interfacial reactions, and mass transport at interfaces. Finally, we offer a perspective on the current state of ML potential development and identify future directions and opportunities for this exciting research field.

show abstract

First principle calculation of mechanical stability, opto-electronic and thermo-electric properties of TaIrGeSn (0 ≤ x ≤ 1) Half-Heusler alloy

Hussain

Kashif

Belabbas

et al. 2020

Computational Condensed Matter

View full text Add to dashboard Cite

Impedance Spectroscopy and Neurocomputing Approaches to Investigate the Enhanced Electrical Blocking Properties of CH3(CH2)n−1SH Thin Monolayers Electrodeposited on a Gold Electrode

Cited by 6 publications

References 59 publications

An Estimate of a Frequency Characterizing the Electrochemical Stability of a Gold Electrode Modified by MHDA Thiol in Different Ways

An Estimate of a Frequency Characterizing the Electrochemical Stability of a Gold Electrode Modified by MHDA Thiol in Different Ways

Machine learning for the modeling of interfaces in energy storage and conversion materials

First principle calculation of mechanical stability, opto-electronic and thermo-electric properties of TaIrGeSn (0 ≤ x ≤ 1) Half-Heusler alloy

Contact Info

Product

Resources

About