High rate of copper electrodeposition from the hexafluorosilicate bath

Dudin, Petr V.; Reva, O. V.; Vorobyova, T.N.

doi:10.1016/j.surfcoat.2010.02.062

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…X-ray diffraction (XRD) is applied to analyze the grain size and deposition reversibility during cycles (Figure 1e,f). [15] According to Scheele's formula, the half-peak width is proportional to the grain size. It is noted that at high current densities (>1 mA cm -2 ), Li metal tends to form coarse-crystal in 2m FM, while deposited as fine grains in 2m FM+2% NC.…”

Section: Morphology and Structure Transition At Varying Deposition Ratesmentioning

confidence: 99%

Morphology Selection Kinetics of Li Sphere via Interface Regulation at High Current Density for Pragmatic Li Metal Batteries

Luo

Yang

et al. 2021

Advanced Energy Materials

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Uncontrollable lithium dendrite growth and severe Li/electrolyte side reactions under high operating current densities seriously hinder the development of high‐performance Li metal batteries (LMBs). To address the aforementioned critical issues, spherical Li nuclei are designed via an “all‐in‐one” nitrocellulose (NC)/LiFSI electrolyte to achieve high‐energy/power‐density and long‐cycle LMBs. First, the synergistic effect of LiFSI induced LiF‐rich interface and the nitro group in the NC scaffold promote uniform Li nucleation, resulting in spherical nuclei morphology instead of dendritic even under high current densities. Moreover, NC exhibits strong adsorption energy on the electrode surface, which facilitates the formation of an organic protection layer to suppress side reactions, which enables highly reversible Li cycling, even in a lean‐electrolyte environment. With the assistance of the unique interphase, the Li|Li symmetric cells using NC/LiFSI electrolyte can stably run at a high current density of 10 mA cm‐2. Moreover, the assembled Li|LiFePO4 pouch cell achieves excellent cycling stability of 210 cycles with 100% capacity retention. This finding provides a new strategy relying on electrolyte engineering to achieve high‐energy/power‐density and long‐cycling‐life LMBs.

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Section: Morphology and Structure Transition At Varying Deposition Ratesmentioning

confidence: 99%

Morphology Selection Kinetics of Li Sphere via Interface Regulation at High Current Density for Pragmatic Li Metal Batteries

Luo

Yang

et al. 2021

Advanced Energy Materials

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“…Достоинствами медных покрытий являются эффективное сцепление практически со всеми металлами, высокие удельная электропроводность и теплопроводность. Медь часто используется для нанесения промежуточного подслоя под покрытия из никеля, хрома и т. д. Для осаждения меди обычно применяются кислые электролиты [1][2][3][4]. Однако непосредственное осаждение меди из кислых растворов на такие активные металлы, как цинк, практически невозможно, поскольку он имеет отрицательный электродный потенциал.…”

Section: Introductionunclassified

Electrochemical deposition of copper on zinc and its alloys

Kuznetsov¹

2022

Journal of the Belarusian State University. Chemistry

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A new electrolyte composition and deposition conditions of quality (light, densely packed, homogeneous) copper coatings on zinc and its alloys were found. It was established that the electrochemical deposition of copper is possible from an alkaline solution containing propylene glycol. It was shown that the adding of hydroxyethylidene diphosphonic acid in the solution stabilises the electrolyte during operation and storage. The addition of morpholine provided deposition of quality copper coatings on zinc and its alloys in a wide range of current densities.

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“…The electrodeposited thin film depends on various parameters like temperature, pH, voltage, current density, and concentration of electrolyte has many numerous effects on the brightness, hardness, porosity and grain size of the deposited thin film. Dudin et al [11] illustrated that the grain size of nickel coatings decreased from 70 nm to 40 nm by increasing the current density from 1 to 30 mA dm −2 . Garcia et al showed that the changing chemical composition of the coatings was changed the grain sizes of the Cu-Zn alloys [12].…”

Section: Introductionmentioning

confidence: 99%

A Comparison for Grain Size Calculation of Cu-Zn Alloys with Genetic Programming and Neural Networks

Karahan

Özdemir

2015

Acta Phys. Pol. A

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Neural Networks (NN) and Genetic Programming (GP) were used as a new method for formulation of grain size of electrodeposited Cu1−xZnx alloys as a function of Zinc and Copper content both electrolyte and the alloy films produced by electrodeposition technique. To predict grain size a great number of different expression models genetic programming and neural network were conducted. Each model differs from the other with their linking function, number of genes, head size, and chromosomes. To generate databases for the new grain size formulations, testing and training sets in total of 134 samples were selected at different Zn and Cu ratios of components. 6 different input parameters were selected and the output parameter was grain size of the electrodeposited Cu-Zn alloys. The testing and training sets consisted of randomly selected 106 and 28 for the proposed models. All results in the models indicated an applicable performance for predicting grain size of the alloys and found reliable. The predicted model showed that all of the input parameters effected on the resulting grain size. The NN and GEP based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2 = 0.995 for GEP and 0.999 for NN).

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High rate of copper electrodeposition from the hexafluorosilicate bath

Cited by 8 publications

References 34 publications

Morphology Selection Kinetics of Li Sphere via Interface Regulation at High Current Density for Pragmatic Li Metal Batteries

Morphology Selection Kinetics of Li Sphere via Interface Regulation at High Current Density for Pragmatic Li Metal Batteries

Electrochemical deposition of copper on zinc and its alloys

A Comparison for Grain Size Calculation of Cu-Zn Alloys with Genetic Programming and Neural Networks

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