Evaluation of the fractal dimension of sol-gel deposited oxide films by means of the power spectral density

Пономарева, А. А.; Мошников, В. А.; Suchaneck, G.

doi:10.1134/s1087659614020163

Cited by 16 publications

(5 citation statements)

References 11 publications

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“…In our previous studies on the surface morphology of thin films [40], [41], a new algorithm named as roughness scaling extraction (RSE) was proposed to evaluate FD based on a single morphological image. It was found that RSE algorithm was much more accurate than the traditional algorithms including BC algorithm [42], power spectral density (PSD) algorithm [43], autocorrelation function (ACF) algorithm [44] and structure function (SF) algorithm [45]. And FD results obtained by RSE were also consistent with actual features in the surface morphologies of alumina thin films.…”

Section: Introductionmentioning

confidence: 53%

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

et al. 2019

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Electroencephalography (EEG) was widely investigated in brain status detection and disease diagnosis, in which the fractal analysis played an important role. In this paper, the roughness scaling extraction (RSE) algorithm proposed in our previous study on surface morphologies was applied to calculate the fractal dimensions (FDs) of artificial profiles and EEG signals. Fractal profiles with ideal FDs ranging from 1.01 to 1.99 were generated through the Weierstrass-Mandelbrot function. The RSE algorithm and the traditional algorithms, including the Higuchi algorithm, the Katz algorithm, and the box counting algorithm, were compared by analyzing the artificial profiles. Based on the mean relative errors and mean square errors, it was found that the RSE algorithm was more accurate than the traditional algorithms. To investigate the influence of noise on FD calculation, noise with different levels was added to the fractal profiles. The RSE and Higuchi algorithms were found reliable at signal-to-noise ratios of 50 and 40 dB, while the accuracy of RSE was also superior to that of the Higuchi. The RSE, Higuchi, and Katz algorithms were utilized to analyze the EEG signals of epilepsy events. The significant FD increasing, which corresponded to the seizure onset, could be detected, and the overlapping between the seizure and non-seizure statuses was small by using the RSE algorithm, indicating its feasibility for the EEG fractal analysis.

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Section: Introductionmentioning

confidence: 53%

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

et al. 2019

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“…For 2D PSD analysis, the AFM data used have scan dimensions of 2.5 x 2.5 μm, 1 x 1 μm, and 250 x 250 nm to cover different surface features with different resolutions and in different but overlapped frequency domains (Figure ). From these plots, we can determine the consistent, dominant surface features from spikes in the spectral response, isotropic behavior of the surface and roughness exponent from the slope, surface roughness from the area under the PSD function, and fractal dimensions at difference length scales . These characteristics collectively describe the surface topography, spatial distribution, spatial length scale, and size of dominating features that contribute to surface roughness .…”

Section: Results and Discussionmentioning

confidence: 99%

“…From these plots, we can determine the consistent, dominant surface features from spikes in the spectral response, isotropic behavior of the surface and roughness exponent from the slope, surface roughness from the area under the PSD function, and fractal dimensions at difference length scales. 35 These characteristics collectively describe the surface topography, spatial distribution, spatial length scale, and size of dominating features that contribute to surface roughness. 32 The calculated PSD plots are shown in Figure 5 at three different length scales: microscale (2 μm to 50 nm), sub-microscale (1 μm to 10 nm), and nanoscale (200−1 nm).…”

Section: Resultsmentioning

confidence: 99%

Carbon Fiber Surface Functional Landscapes: Nanoscale Topography and Property Distribution

Adstedt

Stojcevski

Newman

et al. 2022

ACS Appl. Mater. Interfaces

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The ultimate properties of carbon fibers and their composites are largely dictated by the surface topography of the fibers and the interface characteristics, which are primarily influenced by the surface distribution of chemical functionalities and their interactions with the matrix resin. Nevertheless, nanoscale insights on the carbon fiber surface in relationship with its chemical modification are still rarely addressed. Here, we demonstrate a critical insight on the nanoscale surface topography characterization of modified novel carbon fibers using high-resolution atomic force microscopy at multiple length scales. We compare the nanoscale surface characteristics relevant to their role in controlling interfacial interactions for carbon fibers manufactured at two different tensions and two distinct chemically functionalized coatings. We used surface dimple (also known as nanopores) profiling, microroughness analysis, power spectral density analysis, and adhesion and electrostatic potential mapping to reveal the fine details of surface characteristics at different length scales. This analysis demonstrates that the carbon fibers processed at lower tension possess a higher fractal dimension with a more corrugated surface and higher surface roughness, which leads to increased surface adhesion and energy dissipation across nano- and microscales. Furthermore, electrochemical surface modification with amine- and fluoro-functional groups significantly masks the microroughness inherent to these fibers. This results in increased fractal dimension and decreased energy dissipation and adhesion due to the high chemical reactivity in the areas of asperities and surface defects combined with a significant increase in the surface potential, as revealed by Kelvin probe mapping. These local surface properties of carbon fibers are crucial for designing next-generation fiber composites with predictable interfacial strength and the overall mechanical performance by considering the fiber surface topography for proper control of interphase formation.

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“…Appendix A.1 Herrasti P. et al [120] 1992 TR 2.5 ± 0.1 (Au) 100 nm 10 nm 2.7 ± 0.1 (Vapour) Krim J. et al [14] 1993 TR 2.47-2.98 10 µm 10 nm Ba L. et al [121] 1995 BC 1.67-1.83 --Strizhak P. E. [122] 1995 other 1.49-2 10 mm 0.02 mm Ba L. et al [123] 1996 BC 1.67-1.78 --Chen Z. W. et al [124] 2001 BC 1.52-1.75 --Sun X. et al [125] 2002 BC Gold, polycrystalline copper sputter deposition STM Herrasti P. et al [120] 1992 Au electrochemically STM Krim J. et al [14] 1993 iron ion-beam erosion STM Ba L. et al [121] 1995 Ge-22% Au deposited & annealed TEM Strizhak P. E. [122] 1995 CuS chemical synthesis OM Ba L. et al [123] 1996 Ge-5% Au deposited & annealed TEM Chen Z. W. et al [124] 2001 Au/Ge evaporation& annealed TEM Sun X. et al [125] 2002 ZnO reactive sputtering AFM Fang T. H. et al [75] 2003 ZnO magnetron sputtering AFM Wang Y. et al [37] 2004 Cu-W magnetron sputtering AFM Catalan G. et al [126] 2008 multiferroic BiFeO 3 PLD PFM Raoufi D. [127] 2010 ITO EBE AFM Raoufi D. [88] 2010 SiO 2 -SiO 2 polymeric sol-gel AFM Chen Z. W. et al [128] 2010 SiO 2 PLD SEM Miyata S. et al [129] 2011 MgO Ion beam assisted deposition AFM Gao H. J. et al [130] 2011 C60-polymer ionized-cluster-beam TEM Chen Z. W. et al [61] 2011 Pd/Ge evaporation & annealing TEM Feng F. et al [5] 2012 alumina/Hastelloy C276 Ion beam assisted deposition AFM Ponomareva A. A. et al [131] 2014 Oxide sol-gel deposited AFM Hou L. et al [132] 2014 Pd/Ge thermal evaporation TEM Haniam P. et al [79] 2014 cobalt oxides laser CVD SEM Kong Y. L. et al [133] 2014 TsNiPc spin-coating & annealed AFM Park K. et al [134] 2014 ferroelectric copolymer spin-coating PFM Arman A. et al [135] 2015 copper magnetron sputtering AFM Yadav R. P. et al [20] 2015 BaF 2 EBE AFM Yadav R. P. et al…”

Section: Abbreviations Appendix Amentioning

confidence: 99%

Fractal Analysis on Surface Topography of Thin Films: A Review

et al. 2022

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The topographies of various surfaces have been studied in many fields due to the significant influence that surfaces have on the practical performance of a given sample. A comprehensive evaluation requires the assistance of fractal analysis, which is of significant importance for modern science and technology. Due to the deep insights of fractal theory, fractal analysis on surface topographies has been widely applied and recommended. In this paper, the remarkable uprising in recent decades of fractal analysis on the surfaces of thin films, an essential domain of surface engineering, is reviewed. By summarizing the methods used to calculate fractal dimension and the deposition techniques of thin films, the results and trends of fractal analysis are associated with the microstructure, deposition parameters, etc. and this contributes profoundly to exploring the mechanism of film growth under different conditions. Choosing appropriate methods of surface characterization and calculation methods to study diverse surfaces is the main challenge of current research on thin film surface topography by using fractal theory. Prospective developing trends are proposed based on the data extraction and statistics of the published literature in this field.

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Evaluation of the fractal dimension of sol-gel deposited oxide films by means of the power spectral density

Cited by 16 publications

References 11 publications

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

Carbon Fiber Surface Functional Landscapes: Nanoscale Topography and Property Distribution

Fractal Analysis on Surface Topography of Thin Films: A Review

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