The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Sriram Sundararajan - One of the best experts on this subject based on the ideXlab platform.
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correlation between evolution of surface roughness parameters and micropitting of carburized steel under boundary lubrication condition
Surface & Coatings Technology, 2018Co-Authors: Derek White, Sriram SundararajanAbstract:Abstract This paper investigates the evolution of amplitude and spatial roughness parameters during rolling contact fatigue experiments on carburized samples with around 0%, 15% and 70% retained austenite (RA) under boundary lubrication condition. The 0% RA samples failed due to early crack initiation and rapid crack propagation. The 15% and 70% RA samples showed initiation and propagation of micropitting. Four amplitude surface parameters (Ra, RRMS, Rsk and Rku) and one spatial parameter (2D auto-correlation Length) were analyzed for correlations between micropitting initiation and propagation during RCF cycles. It was observed that during run-in, a significant decrease in Ra and RRMS were observed, while the correlation Length increased and stabilized for all samples. The evolution of the 2D Autocorrelation Length correlated well with the evolution of crack propagation. It was observed that, while Ra and RRMS values follow the same trend as propagation of micropitting, skewness and kurtosis can be used to better predict initiation and propagation of micropitting on the surface. Significant propagation of micropitting was accompanied by a decreasing trend of skewness and increasing trend of kurtosis. Transverse directional correlation Length also showed good correlation with propagation of micropitting, with the correlation Length decreasing during increase of micropitting on sample surfaces.
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Method to generate surfaces with desired roughness parameters.
Langmuir, 2007Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:A surface engineering method based on the electrostatic deposition of microparticles and dry etching is described and shown to be able to independently tune both amplitude and spatial roughness parameters of the final surface. Statistical models were developed to connect process variables to the amplitude parameters (center line average and root-mean-square) and a spatial parameter (Autocorrelation Length) of the final surfaces. Process variables include particle coverage, which affects both amplitude and spatial roughness parameters, particle size, which affects only spatial parameters, and etch depth, which affects only amplitude parameters. Correlations between experimental data and model predictions are discussed.
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Generating random surfaces with desired Autocorrelation Length
Applied Physics Letters, 2006Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:A versatile surface processing method based on electrostatic deposition of particles and subsequent dry etching is shown to be able to tailor the Autocorrelation Length of a random surface by varying particle size and coverage. An explicit relation between final Autocorrelation Length, surface coverage of the particles, particle size, and etch depth is built. The Autocorrelation Length of the final surface closely follows a power law decay with particle coverage, the most significant processing parameter. Experimental results on silicon substrates agree reasonably well with model predictions.
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The effect of Autocorrelation Length on the real area of contact and friction behavior of rough surfaces
Journal of Applied Physics, 2005Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:Autocorrelation Length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as root-mean-square roughness. This paper presents a relationship between ACL and the friction behavior of a rough surface. The influence of ACL on the peak distribution of a profile is studied based on Whitehouse and Archard’s classical analysis [Whitehouse and ArchardProc. R. Soc. London, Ser. A 316, 97 (1970)] and their results are extended to compare profiles from different surfaces. The probability density function of peaks and the mean peak height of a profile are given as functions of its ACL. These results are used to estimate the number of contact points when a rough surface comes into contact with a flat surface, and it is shown that the larger the ACL of the rough surface, the less the number of contact points. Based on Hertzian contact mechanics, it is shown that the real area of contact increases with increasing of number of co...
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A Relationship Between Autocorrelation Length and Adhesive Friction Behavior of Rough Surfaces
World Tribology Congress III Volume 1, 2005Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:Autocorrelation Length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as Root Mean Square roughness. This paper presents a statistical relation between ACL and the real area of contact, which is used to study the adhesive friction behavior of a rough surface. The influence of ACL on profile peak distribution is studied based on Whitehouse and Archard’s classical analysis, and their results are extended to compare profiles from different surfaces. With the knowledge of peak distribution, the real area of contact of a rough surface with a flat surface can be calculated using Hertzian contact mechanics. Numerical calculation shows that real area of contact increases with decreasing of ACL under the same normal load. Since adhesive friction force is proportional to real area of contact, this suggests that the adhesive friction behavior of a surface will be inversely proportional to its ACL. Results from microscale friction experiments on polished and etched silicon surfaces are presented to verify the analysis.Copyright © 2005 by ASME
Gabriel Popescu - One of the best experts on this subject based on the ideXlab platform.
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Tissue spatial correlation as cancer marker.
Journal of biomedical optics, 2019Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial Autocorrelation Length obtained from quantitative phase images. The spatial Autocorrelation Length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation Length maps are calculated by computing two-dimensional Fourier transforms over image subregions-requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.
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Tissue spatial correlation as cancer marker
2018Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:We propose a new intrinsic cancer marker in fixed tissue biopsy slides, which is based on the localspatial Autocorrelation Length obtained from quantitative phase images. The spatial Autocorrelation Length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation Length maps are calculated by computing 2D Fourier transforms over image sub-regions - requiring long computational times. In this paper, we propose a more time efficient method of computing the correlation map and demonstrate its value for screening of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy (SLIM).
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High throughput calculation of local spatial Autocorrelation Length for label-free diagnosis of tissue biopsy
Quantitative Phase Imaging IV, 2018Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) can access quantitative information on thickness and/or refractive index changes of weakly absorbing and scattering objects, which normally require staining prior to observation. The quantitative phase image itself yields significant information for a medical diagnosis, particularly in cancer biopsies. Previously, several parameters such as a local standard deviation of refractive index have been utilized as a marker of diseases. We focus on the local spatial Autocorrelation Length, which is calculated at each point in the field of view. The local spatial Autocorrelation Length is defined as the standard deviation of the local spatial Autocorrelation function and reveals the local and directional disorder information of tissues. However, generally, a direct calculation of the local spatial Autocorrelation Length take an immense amount of time. In this paper, we propose a high-throughput calculation procedure of a local spatial Autocorrelation Length, by exploiting frequency-domain calculations. After deriving a simple equation to calculate the local spatial Autocorrelation Length map in a short time, we perform label-free screening of benign and malignant breast tissue biopsies using this parameter as a marker.
Yilei Zhang - One of the best experts on this subject based on the ideXlab platform.
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Method to generate surfaces with desired roughness parameters.
Langmuir, 2007Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:A surface engineering method based on the electrostatic deposition of microparticles and dry etching is described and shown to be able to independently tune both amplitude and spatial roughness parameters of the final surface. Statistical models were developed to connect process variables to the amplitude parameters (center line average and root-mean-square) and a spatial parameter (Autocorrelation Length) of the final surfaces. Process variables include particle coverage, which affects both amplitude and spatial roughness parameters, particle size, which affects only spatial parameters, and etch depth, which affects only amplitude parameters. Correlations between experimental data and model predictions are discussed.
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Generating random surfaces with desired Autocorrelation Length
Applied Physics Letters, 2006Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:A versatile surface processing method based on electrostatic deposition of particles and subsequent dry etching is shown to be able to tailor the Autocorrelation Length of a random surface by varying particle size and coverage. An explicit relation between final Autocorrelation Length, surface coverage of the particles, particle size, and etch depth is built. The Autocorrelation Length of the final surface closely follows a power law decay with particle coverage, the most significant processing parameter. Experimental results on silicon substrates agree reasonably well with model predictions.
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The effect of Autocorrelation Length on the real area of contact and friction behavior of rough surfaces
Journal of Applied Physics, 2005Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:Autocorrelation Length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as root-mean-square roughness. This paper presents a relationship between ACL and the friction behavior of a rough surface. The influence of ACL on the peak distribution of a profile is studied based on Whitehouse and Archard’s classical analysis [Whitehouse and ArchardProc. R. Soc. London, Ser. A 316, 97 (1970)] and their results are extended to compare profiles from different surfaces. The probability density function of peaks and the mean peak height of a profile are given as functions of its ACL. These results are used to estimate the number of contact points when a rough surface comes into contact with a flat surface, and it is shown that the larger the ACL of the rough surface, the less the number of contact points. Based on Hertzian contact mechanics, it is shown that the real area of contact increases with increasing of number of co...
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A Relationship Between Autocorrelation Length and Adhesive Friction Behavior of Rough Surfaces
World Tribology Congress III Volume 1, 2005Co-Authors: Yilei Zhang, Sriram SundararajanAbstract:Autocorrelation Length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as Root Mean Square roughness. This paper presents a statistical relation between ACL and the real area of contact, which is used to study the adhesive friction behavior of a rough surface. The influence of ACL on profile peak distribution is studied based on Whitehouse and Archard’s classical analysis, and their results are extended to compare profiles from different surfaces. With the knowledge of peak distribution, the real area of contact of a rough surface with a flat surface can be calculated using Hertzian contact mechanics. Numerical calculation shows that real area of contact increases with decreasing of ACL under the same normal load. Since adhesive friction force is proportional to real area of contact, this suggests that the adhesive friction behavior of a surface will be inversely proportional to its ACL. Results from microscale friction experiments on polished and etched silicon surfaces are presented to verify the analysis.Copyright © 2005 by ASME
Masanori Takabayashi - One of the best experts on this subject based on the ideXlab platform.
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Tissue spatial correlation as cancer marker.
Journal of biomedical optics, 2019Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial Autocorrelation Length obtained from quantitative phase images. The spatial Autocorrelation Length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation Length maps are calculated by computing two-dimensional Fourier transforms over image subregions-requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.
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Tissue spatial correlation as cancer marker
2018Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:We propose a new intrinsic cancer marker in fixed tissue biopsy slides, which is based on the localspatial Autocorrelation Length obtained from quantitative phase images. The spatial Autocorrelation Length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation Length maps are calculated by computing 2D Fourier transforms over image sub-regions - requiring long computational times. In this paper, we propose a more time efficient method of computing the correlation map and demonstrate its value for screening of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy (SLIM).
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High throughput calculation of local spatial Autocorrelation Length for label-free diagnosis of tissue biopsy
Quantitative Phase Imaging IV, 2018Co-Authors: Masanori Takabayashi, Hassaan Majeed, Andre Kajdacsy-balla, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) can access quantitative information on thickness and/or refractive index changes of weakly absorbing and scattering objects, which normally require staining prior to observation. The quantitative phase image itself yields significant information for a medical diagnosis, particularly in cancer biopsies. Previously, several parameters such as a local standard deviation of refractive index have been utilized as a marker of diseases. We focus on the local spatial Autocorrelation Length, which is calculated at each point in the field of view. The local spatial Autocorrelation Length is defined as the standard deviation of the local spatial Autocorrelation function and reveals the local and directional disorder information of tissues. However, generally, a direct calculation of the local spatial Autocorrelation Length take an immense amount of time. In this paper, we propose a high-throughput calculation procedure of a local spatial Autocorrelation Length, by exploiting frequency-domain calculations. After deriving a simple equation to calculate the local spatial Autocorrelation Length map in a short time, we perform label-free screening of benign and malignant breast tissue biopsies using this parameter as a marker.
J. Herrero - One of the best experts on this subject based on the ideXlab platform.
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Improving conductivity and texture in ZnO:Al sputtered thin films by sequential chemical and thermal treatments
Applied Surface Science, 2013Co-Authors: C. Guillén, J. HerreroAbstract:Abstract Smooth, transparent and conductive ZnO:Al (AZO) thin films have been deposited by sputtering at room temperature on glass substrates. The conductivity and texture of the as-grown layers have been enhanced by the application of thermal heating at 350 °C and chemical etching in HCl or HF solutions. The structure, morphology, optical and electrical properties of the layers have been analysed before and after the successive thermal and chemical treatments, which were performed in different sequences. The surface texture has been characterized optically by the haze parameter for transmittance, which has been related to the root mean square roughness and the Autocorrelation Length coefficients for each sample. The highest optical scattering has been achieved with the HF etching and subsequent heating sequence, which gave also the highest root mean square roughness. In this way, an average haze of 36%, together with total visible transmittance about 80% and sheet resistance of 8 Ω/sq have been obtained. Otherwise, the application of heating before HCl or HF etching resulted in AZO films with average haze of 11%, visible transmittance about 85% and sheet resistance near 6 Ω/sq.
