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{"title"=>"Automated defect and correlation length analysis of block copolymer thin film nanopatterns", "type"=>"journal", "authors"=>[{"first_name"=>"Jeffrey N.", "last_name"=>"Murphy", "scopus_author_id"=>"26433766400"}, {"first_name"=>"Kenneth D.", "last_name"=>"Harris", "scopus_author_id"=>"55769747837"}, {"first_name"=>"Jillian M.", "last_name"=>"Buriak", "scopus_author_id"=>"7003290668"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"605996929", "doi"=>"10.1371/journal.pone.0133088", "issn"=>"19326203", "scopus"=>"2-s2.0-84941892076", "sgr"=>"84941892076"}, "id"=>"ac59e00c-734c-35e9-a937-21f1e8bd56d7", "abstract"=>"<p>Line patterns produced by lamellae- and cylinder-forming block copolymer (BCP) thin films are of widespread interest for their potential to enable nanoscale patterning over large areas. In order for such patterning methods to effectively integrate with current technologies, the resulting patterns need to have low defect densities, and be produced in a short timescale. To understand whether a given polymer or annealing method might potentially meet such challenges, it is necessary to examine the evolution of defects. Unfortunately, few tools are readily available to researchers, particularly those engaged in the synthesis and design of new polymeric systems with the potential for patterning, to measure defects in such line patterns. To this end, we present an image analysis tool, which we have developed and made available, to measure the characteristics of such patterns in an automated fashion. Additionally we apply the tool to six cylinder-forming polystyrene-<italic>block</italic>-poly(2-vinylpyridine) polymers thermally annealed to explore the relationship between the size of each polymer and measured characteristics including line period, line-width, defect density, line-edge roughness (LER), line-width roughness (LWR), and correlation length. Finally, we explore the line-edge roughness, line-width roughness, defect density, and correlation length as a function of the image area sampled to determine each in a more rigorous fashion.</p>", "link"=>"http://www.mendeley.com/research/automated-defect-correlation-length-analysis-block-copolymer-thin-film-nanopatterns", "reader_count"=>42, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>7, "Researcher"=>10, "Student > Ph. D. Student"=>14, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>7, "Researcher"=>10, "Student > Ph. D. Student"=>14, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Materials Science"=>11, "Agricultural and Biological Sciences"=>1, "Neuroscience"=>1, "Chemical Engineering"=>4, "Physics and Astronomy"=>4, "Chemistry"=>9}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Materials Science"=>{"Materials Science"=>11}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>9}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Chemical Engineering"=>{"Chemical Engineering"=>4}}, "reader_count_by_country"=>{"United States"=>1, "France"=>1}, "group_count"=>0}

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• http://doi.org/10.1039/C6CP08726D
• http://doi.org/10.1021/acs.macromol.7b00876
• http://doi.org/10.1088/0953-8984/28/40/403002
• http://doi.org/10.1021/acs.macromol.8b02460
• http://doi.org/10.1038/ncomms10518
• http://doi.org/10.1039/C6TA00161K
• http://doi.org/10.1021/acs.nanolett.8b01389
• http://doi.org/10.5937/jaes16-16888
• http://doi.org/10.1039/C7NR06069F
• http://doi.org/10.1021/acsami.7b17713
• http://doi.org/10.1021/acsmacrolett.9b00353
• http://doi.org/10.1021/acs.chemmater.6b01825
• http://doi.org/10.1021/acs.chemmater.6b03633
• http://doi.org/10.1021/acs.chemmater.6b04202
• http://doi.org/10.1021/acs.chemmater.6b02967
• http://doi.org/10.1021/acsami.7b10510
• http://doi.org/10.1039/C5SM02441B
• http://doi.org/10.1021/acsapm.8b00138
• http://doi.org/10.1039/D0TC02126A
• http://doi.org/10.1021/acsami.8b13255
• http://doi.org/10.1126/sciadv.aaw3974
• http://doi.org/10.1039/C7SM00212B
• http://doi.org/10.1021/acs.macromol.9b01703
• http://doi.org/10.1080/23746149.2018.1445558
• http://doi.org/10.1021/acsnano.0c00718
• http://doi.org/10.3390/nano10010103

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PubMed Central

• 4736107

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• {"month"=>"12", "year"=>"2018", "pdf_views"=>"14", "xml_views"=>"2", "html_views"=>"45"}
• {"month"=>"1", "year"=>"2019", "pdf_views"=>"18", "xml_views"=>"3", "html_views"=>"19"}
• {"month"=>"2", "year"=>"2019", "pdf_views"=>"32", "xml_views"=>"6", "html_views"=>"46"}
• {"month"=>"3", "year"=>"2019", "pdf_views"=>"25", "xml_views"=>"6", "html_views"=>"36"}
• {"month"=>"4", "year"=>"2019", "pdf_views"=>"43", "xml_views"=>"9", "html_views"=>"58"}
• {"month"=>"5", "year"=>"2019", "pdf_views"=>"30", "xml_views"=>"8", "html_views"=>"47"}
• {"month"=>"6", "year"=>"2019", "pdf_views"=>"27", "xml_views"=>"9", "html_views"=>"44"}
• {"month"=>"7", "year"=>"2019", "pdf_views"=>"24", "xml_views"=>"4", "html_views"=>"29"}
• {"month"=>"8", "year"=>"2019", "pdf_views"=>"42", "xml_views"=>"7", "html_views"=>"33"}
• {"month"=>"9", "year"=>"2019", "pdf_views"=>"46", "xml_views"=>"3", "html_views"=>"34"}
• {"month"=>"10", "year"=>"2019", "pdf_views"=>"36", "xml_views"=>"3", "html_views"=>"36"}
• {"month"=>"11", "year"=>"2019", "pdf_views"=>"72", "xml_views"=>"7", "html_views"=>"53"}
• {"month"=>"12", "year"=>"2019", "pdf_views"=>"22", "xml_views"=>"2", "html_views"=>"51"}
• {"month"=>"1", "year"=>"2020", "pdf_views"=>"39", "xml_views"=>"1", "html_views"=>"41"}
• {"month"=>"2", "year"=>"2020", "pdf_views"=>"45", "xml_views"=>"4", "html_views"=>"30"}
• {"month"=>"3", "year"=>"2020", "pdf_views"=>"55", "xml_views"=>"1", "html_views"=>"34"}
• {"month"=>"4", "year"=>"2020", "pdf_views"=>"94", "xml_views"=>"0", "html_views"=>"52"}
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• {"month"=>"7", "year"=>"2020", "pdf_views"=>"38", "xml_views"=>"0", "html_views"=>"36"}
• {"month"=>"8", "year"=>"2020", "pdf_views"=>"2", "xml_views"=>"0", "html_views"=>"5"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/2189661"], "description"=>"<p>Data from all resolutions shown. Data from individual images are shown with dark markers. A. Boxplot of the standard deviation for line-width (LWR, 1σ) divided by the line-width for various resolutions, grouped by polymer. B. Boxplot of the standard deviation for line edge position (LER, 1σ) divided by the line-width for various resolutions, grouped by polymer. C. Boxplot of measured line-widths for polymer groups by polymer. D. Same data, plotted as a function of BCP period (nm), all resolutions included; the error bars are standard deviations for the line-widths from measuring the lines separately.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494146, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g016", "stats"=>{"downloads"=>5, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplots_of_calculated_BCP_pattern_metrics_for_SEM_images_with_various_resolutions_for_5_cylinder_forming_PS_b_P2VP_block_copolymers_each_identically_treated_/1494146", "title"=>"Boxplots of calculated BCP pattern metrics for SEM images with various resolutions for 5 cylinder-forming PS-b-P2VP block copolymers, each identically treated.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189663"], "description"=>"<p>Units are in nm, except for defect density, which is given as defect pairs per μm². Line-edge roughness (LER) is given as three times the standard deviation (3σ) in the edge position, relative to the center of the line; line-width roughness (LWR) is three times the standard deviation (3σ) in the width of the line.</p><p>Data for each of the four panels in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.g001\" target=\"_blank\">Fig 1</a>, including period, LER, LWR, correlation length, and defect density.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494148, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.t001", "stats"=>{"downloads"=>5, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_for_each_of_the_four_panels_in_Fig_1_including_period_LER_LWR_correlation_length_and_defect_density_/1494148", "title"=>"Data for each of the four panels in Fig 1, including period, LER, LWR, correlation length, and defect density.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189681", "https://ndownloader.figshare.com/files/2189682", "https://ndownloader.figshare.com/files/2189683", "https://ndownloader.figshare.com/files/2189684", "https://ndownloader.figshare.com/files/2189685", "https://ndownloader.figshare.com/files/2189686", "https://ndownloader.figshare.com/files/2189687", "https://ndownloader.figshare.com/files/2189688", "https://ndownloader.figshare.com/files/2189689", "https://ndownloader.figshare.com/files/2189690", "https://ndownloader.figshare.com/files/2189691"], "description"=>"<div><p>Line patterns produced by lamellae- and cylinder-forming block copolymer (BCP) thin films are of widespread interest for their potential to enable nanoscale patterning over large areas. In order for such patterning methods to effectively integrate with current technologies, the resulting patterns need to have low defect densities, and be produced in a short timescale. To understand whether a given polymer or annealing method might potentially meet such challenges, it is necessary to examine the evolution of defects. Unfortunately, few tools are readily available to researchers, particularly those engaged in the synthesis and design of new polymeric systems with the potential for patterning, to measure defects in such line patterns. To this end, we present an image analysis tool, which we have developed and made available, to measure the characteristics of such patterns in an automated fashion. Additionally we apply the tool to six cylinder-forming polystyrene-<i>block</i>-poly(2-vinylpyridine) polymers thermally annealed to explore the relationship between the size of each polymer and measured characteristics including line period, line-width, defect density, line-edge roughness (LER), line-width roughness (LWR), and correlation length. Finally, we explore the line-edge roughness, line-width roughness, defect density, and correlation length as a function of the image area sampled to determine each in a more rigorous fashion.</p></div>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494166, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0133088.s001", "https://dx.doi.org/10.1371/journal.pone.0133088.s002", "https://dx.doi.org/10.1371/journal.pone.0133088.s003", "https://dx.doi.org/10.1371/journal.pone.0133088.s004", "https://dx.doi.org/10.1371/journal.pone.0133088.s005", "https://dx.doi.org/10.1371/journal.pone.0133088.s006", "https://dx.doi.org/10.1371/journal.pone.0133088.s007", "https://dx.doi.org/10.1371/journal.pone.0133088.s008", "https://dx.doi.org/10.1371/journal.pone.0133088.s009", "https://dx.doi.org/10.1371/journal.pone.0133088.s010", "https://dx.doi.org/10.1371/journal.pone.0133088.s011"], "stats"=>{"downloads"=>27, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_Defect_and_Correlation_Length_Analysis_of_Block_Copolymer_Thin_Film_Nanopatterns_/1494166", "title"=>"Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189640"], "description"=>"<p>The following molecular weights correspond to the polystyrene-<i>block</i>-poly(2-vinylpyridine) block copolymers used: (A) 44k-<i>b</i>-18.5k, (B) 32.5k-<i>b</i>-12k, (C & D) 50k<i>-b</i>-16.5k. Units are in kg/mol, hence 44k is 44 kg/mol. The first three images are taken at 50,000x magnification; the fourth at 25,000x. The orange scale bars all represent 200 nm.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494128, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g001", "stats"=>{"downloads"=>4, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_SEM_images_for_Pt_line_patterns_derived_from_3_different_PS_b_P2VP_polymers_/1494128", "title"=>"Sample SEM images for Pt line patterns derived from 3 different PS-<i>b</i>-P2VP polymers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189641"], "description"=>"<p>(1) The original SEM image; (2) Smoothing of the image to reduce noise; (3) Thresholding the image to produce a binary image suitable for particle analysis; (4) Analysis of period and line-widths in order to set parameters in subsequent analyses; (5) Particle analysis of the binary image to find lines and dots; (6) Skeletonization of the lines; (7) Grooming and analysis of the skeletons; and (8) Compiling visual and other data files for output.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494129, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_brief_visual_outline_of_the_analysis_undertaken_by_the_ADAblock_application_broken_into_8_major_stages_in_sequential_order_/1494129", "title"=>"A brief visual outline of the analysis undertaken by the ADAblock application, broken into 8 major stages in sequential order.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189642"], "description"=>"<p>(A) Unmodified binary image of platinized PS(50k)-<i>b</i>-P2VP(16.5k) and (B) simplified binary image; (C) fit of particle area as a function of perimeter for the unmodified image and (D) fit for the simplified image. (E) Demonstration that a fit of 18 nm for line-width is reasonable for the filtered greyscale image, (F) the thresholded binary image, and (G) a profile of the filtered image. (H) Line diagram showing the relationship between particle area, perimeter, and length.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494130, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Process_for_determining_line_width_and_period_directly_from_binary_patterns_/1494130", "title"=>"Process for determining line width and period directly from binary patterns.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189643"], "description"=>"<p>(A) Original image. (B) Overlay of binary and skeletonised images showing retained connectivity. (C) Binary image. (D) Inverted binary image. (E) and (F) Processed images (C) and (D), with dots in respective phases removed. (G) and (H) Skeleton images derived from (E) and (F). Images are all 735 nm × 735 nm.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494131, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g004", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Process_for_the_skeletonization_of_both_positive_and_negative_phases_of_a_binary_image_/1494131", "title"=>"Process for the skeletonization of both positive and negative phases of a binary image.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189644"], "description"=>"<p>Analysis of two images are shown in parallel with corresponding images in two columns. (A) The original images. (B) Defects in the positive phase marked. (C) Defects in the negative phase marked. (D) All defects. Legend at the bottom shows colours and shapes used for each feature: Bright phase: red lines, teal dots, yellow circles at terminal points, and 3- and 4-connected junctions. Dark phase: navy blue lines, magenta dots, aqua terminal points, and 3- and 4-connected junctions represented by shapes with an equal number of branches.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494132, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g005", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_How_the_defects_depending_on_phase_bright_8220_positive_8221_dark_8220_negative_8221_tend_to_be_of_different_types_/1494132", "title"=>"How the defects, depending on phase (bright = “positive”; dark = “negative”), tend to be of different types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189645"], "description"=>"<p>Shown are each major type of component defect, as exists in either the positive (e.g. P2VP) phase or the negative (e.g. PS) phase. For each, 3-branch junctions, terminal points, and dots, examples are given with defects highlighted by a magenta dot. This analysis is done relative to an ideal striped pattern without any interrupting features, save for the edge of the image.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494133, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g006", "stats"=>{"downloads"=>3, "page_views"=>156, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Table_of_topological_defect_components_typically_found_in_BCP_thin_film_nanopatterns_/1494133", "title"=>"Table of topological defect components typically found in BCP thin film nanopatterns.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189646"], "description"=>"<p>Highlighted are pixels representing (i) terminal points at the end of the line or branch, each adjacent to only 1 pixel, (ii) contiguous points along the line, each with 2 neighbour pixels, and (iii) junction points where three or more branches meet, having 3 or more neighbour pixels. Similar to minesweeper games, the number of adjacent pixels determines the value of each skeleton pixel.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494134, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pixels_of_a_typical_junction_and_three_associated_terminal_points_showing_the_counting_of_adjacent_skeleton_pixels_/1494134", "title"=>"Pixels of a typical junction and three associated terminal points showing the counting of adjacent skeleton pixels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189647"], "description"=>"<p>(A) Image of metallized PS(50k)-<i>b</i>-P2VP(16.5k) nanowire. (B) Image of skeletonized image, with positive lines in red and skeleton in white, and negative lines in black and skeleton in blue. (C) Detail of region identified by green box in (B), showing a branch, yellow, trimmed from the skeleton. (D) Schematic showing radius-based trimming of branches: (1) a branch that exceeds the radius does not undergo trimming and (2) a branch that terminates within the radius is trimmed.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494135, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g008", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Grooming_the_skeleton_to_remove_junctions_formed_as_an_artifact_from_variations_in_line_width_or_from_edge_effects_/1494135", "title"=>"Grooming the skeleton to remove junctions formed as an artifact from variations in line width or from edge effects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189648"], "description"=>"<p>(A) Sketch to conceptually demonstrate line edge roughness, where the variation in edge position of the line (shown in rose with black edge) varies with respect to the ideal (shown overlaid in blue) or, in this case, the average edge position. Each individual displacement is measured with respect to the average, and the LER calculated as 3 times the standard deviation. (B) Sketch of line-width roughness, which is the variation in line-width. The sketch is adapted from the bulges and pinches shown in the SEM image below. (C) SEM image of block copolymer templated Pt nanowires on a Si wafer, using PS(44k)-<i>b</i>-P2VP(18.5k), annealed at 200°C for 20 minutes.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494136, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g009", "stats"=>{"downloads"=>5, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diagrams_depicting_measurement_of_line_edge_roughness_and_line_width_roughness_/1494136", "title"=>"Diagrams depicting measurement of line-edge roughness and line-width roughness.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189649"], "description"=>"<p>(A) Outline of a line, showing edge points (black dots) and skeleton points (black diamonds) on the centre line. One edge point (x_edge,y_edge) is selected and distances to nearest skeleton points are checked. (B) Interpolation to nearest orthogonal point from the edge point to a point on the skeleton segment. (C) Extension of edge-to-skeleton vector to intersection with transverse edge segment. (D) Expanded, with parameterization as scalable, intersecting vectors.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494137, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g010", "stats"=>{"downloads"=>2, "page_views"=>160, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diagram_showing_relationship_between_line_edge_points_skeleton_points_and_the_vectors_used_to_determine_edge_positions_and_line_widths_for_LER_and_LWR_/1494137", "title"=>"Diagram showing relationship between line edge points, skeleton points, and the vectors used to determine edge positions and line-widths for LER and LWR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189650"], "description"=>"<p>The labels 1, 2, 3, and 4 mark the line subject to each of the four stages of smoothing described. All images with the cyan-to-red colour scheme show the relative width of the opposite side of the line, from the skeleton centre, to the edge; if a side is wider in proportion it is shown in red; narrower is shown in cyan. A colour scale is given provided. (A) The top left shows the edge-to-edge width, following both sides of the edge of the line (C1), hence it is roughly symmetric; (B) the edge-to-skeleton widths are plotted similarly, but with roughly half of the displacement. (C) Next, the lines are shown replotted in a straightened fashion. Note that the lengths have been scaled to be equal, as smoothing of the skeleton shortens the length measured along the skeleton, as expected, due to smaller point-to-point displacements. In the above 3 cases, the more smoothed lines show smaller variations in colour. (D) The histograms represent the edge to skeleton widths relative to the half widths for each point. € Last, the original skeleton (0), along with the 4 stages of smoothing (1,2,3,4) are shown for a re-drawn line, along with a the distribution of edge widths <i>via</i> colouration (5).</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494138, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g011", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_smoothing_process_used_to_partially_eliminate_roughness_resulting_from_pixelation_of_the_lines_/1494138", "title"=>"The smoothing process used to partially eliminate roughness resulting from pixelation of the lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189651"], "description"=>"<p>SEMs are shown in false colour to display the angle of each wire as used in the calculation of the correlation functions, shown right. The raw correlation data is shown in red, the smoothed data is blue, and the calculated correlation length (κ) is marked with a green line and noted on each plot. Beside each image is a blue circle whose radius is equal to the correlation length, as the correlation length is often given as a measure of average grain size. Each image is shown cropped here to ~2 μm wide. The scale bar is 1 μm. (See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.s006\" target=\"_blank\">S5 Fig</a> for full images). The labels (A-F) correspond to the same labelled images in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.g014\" target=\"_blank\">Fig 14</a>.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494139, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g012", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_lengths_and_orientation_maps_for_six_SEM_images_of_metallized_PS_b_P2VP_50k_b_16_5k_44k_b_18_5k_and_32_5k_b_12k_patterns_with_different_degrees_of_thermal_annealing_/1494139", "title"=>"Correlation lengths and orientation maps for six SEM images of metallized PS-<i>b</i>-P2VP (50k-<i>b</i>-16.5k, 44k-<i>b</i>-18.5k, and 32.5k-<i>b</i>-12k) patterns with different degrees of thermal annealing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189652"], "description"=>"<p>SEM images on left, and confirmation images with defects identified shown right. These images are spectacular only upon a close-up. Each image is shown cropped here to ~2 μm wide. The scale bar is 1 μm. (See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.s006\" target=\"_blank\">S5 Fig</a> for full images). The labels (A-F) show correspondence to the same processed images in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.g012\" target=\"_blank\">Fig 12</a>.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494140, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g013", "stats"=>{"downloads"=>3, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Original_and_defect_analysis_images_for_six_SEM_images_of_metallized_PS_b_P2VP_50k_b_16_5k_44k_b_18_5k_and_32_5k_b_12k_patterns_with_different_degrees_of_thermal_annealing_/1494140", "title"=>"Original and defect analysis images for six SEM images of metallized PS-<i>b</i>-P2VP (50k-<i>b</i>-16.5k, 44k-<i>b</i>-18.5k, and 32.5k-<i>b</i>-12k) patterns with different degrees of thermal annealing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189653"], "description"=>"<p>Based on data for a variety of annealed, neat and blended, cylinder-forming, PS-<i>b</i>-P2VP polymer thin films of a variety of molecular weights including blends, using images of the metallized P2VP domains on Si substrates. This enables direct comparison between different polymers, which result in patterns with different periodicities. Defects initially show a dramatic decrease, as structures move away from dot arrays, for which the normalized, defect metric would be ~1. Inset displays four representative images. Respectively, their periods are 32, 43, 36, & 37 nm; their correlation lengths are 19, 43, 65, & 121 nm; their defect densities are 744, 195, 134, & 42 defect pairs·μm^-2.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494141, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g014", "stats"=>{"downloads"=>5, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_correlation_lengths_plotted_here_as_persistence_length_L_o_versus_the_defect_density_normalized_per_unit_period_squared_/1494141", "title"=>"Relationship between correlation lengths, plotted here as persistence length (κ /L_o) versus the defect density, normalized per unit period squared.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}
• {"files"=>["https://ndownloader.figshare.com/files/2189658"], "description"=>"<p>All images had areas of 1280 x 896 pixels, taken with different magnification factors. Five cylinder-forming PS-b-P2VP block copolymers, each identically treated, were imaged: PS(23.6k)-b-P2VP(10.4k) [blue circles], PS(32.5k)-b-P2VP(12k) [green triangles], PS(34k)-b-P2VP(18k) [yellow squares], PS(44k)-b-P2VP(18.5k) [orange pentagons], and PS(50k)-b-P2VP(16.5k) [red diamonds]. Average values are indicated by dark markers and standard deviation error bars; data from individual images are shown with light markers. A. Standard deviation for line-width (LWR, 1σ) divided by the line-width for various resolutions and plotted as a function of real image area, μm². B. Standard deviation for line edge position (LER, 1σ) divided by the line-width for various resolutions and plotted as a function of real image area, μm². C. Defect pair density as a function of real image area, μm². D. Correlation length measured as a function of real image area, μm²; see also <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133088#pone.0133088.s010\" target=\"_blank\">S9 Fig</a>, which plots the correlation length as a function of the number of grains measured.</p>", "links"=>[], "tags"=>["polymer", "fashion", "correlation length", "bcp", "Film Nanopatterns Line patterns", "roughness", "patterning", "defect density", "method", "Correlation Length Analysis", "LWR", "image analysis tool", "characteristic", "ler"], "article_id"=>1494143, "categories"=>["Biological Sciences"], "users"=>["Jeffrey N. Murphy", "Kenneth D. Harris", "Jillian M. Buriak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133088.g015", "stats"=>{"downloads"=>2, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_showing_effect_of_sampling_area_and_resolution_for_BCP_pattern_metrics_LWR_LER_defect_density_and_correlation_length_/1494143", "title"=>"Data showing effect of sampling area and resolution for BCP pattern metrics: LWR, LER, defect density, and correlation length.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-24 04:10:18"}

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• {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
• {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
• {"unique-ip"=>"10", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"21", "supp-data"=>"33", "cited-by"=>"1", "year"=>"2020", "month"=>"3"}
• {"unique-ip"=>"4", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
• {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"11", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
• {"unique-ip"=>"6", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime