FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3171296504> ?p ?o ?g. }

• W3171296504 endingPage "150412" @default.
• W3171296504 startingPage "150412" @default.
• W3171296504 abstract "Identification of microstructures in steel has been extensively studied to improve the understanding of corrosion behavior. However, identification by expert eyes could be subjective, and most previous works on identification are solely based on morphological features. Furthermore, it is more difficult to identify local microstructures on a small scale-length. In this study, we developed a method for differentiating local microstructures on low carbon steel based on multiple physical properties at the nanoscale combined with machine learning techniques. Machine learning techniques were applied to the atomic force microscopy images of multiple physical properties, that is, not only of morphological features but also of the surface potential and capacitance gradient. Thereafter, we analyzed the corrosion behavior according to the concentration of the NaCl solution in which the samples were immersed, on the basis of the identified local microstructures as well as obtained physical properties. This study, which is based on these multiple physical properties, potentially provides a powerful tool for identifying and visualizing features of data. It could be further extended to electrochemical systems with more complex microstructures." @default.
• W3171296504 created "2021-06-22" @default.
• W3171296504 creator A5000696411 @default.
• W3171296504 creator A5011263744 @default.
• W3171296504 creator A5016517520 @default.
• W3171296504 creator A5029603786 @default.
• W3171296504 creator A5047648026 @default.
• W3171296504 creator A5047695895 @default.
• W3171296504 creator A5072079888 @default.
• W3171296504 date "2021-10-01" @default.
• W3171296504 modified "2023-10-17" @default.
• W3171296504 title "Visualization of electrochemical behavior in carbon steel assisted by machine learning" @default.
• W3171296504 cites W1811420382 @default.
• W3171296504 cites W1965379227 @default.
• W3171296504 cites W1988217862 @default.
• W3171296504 cites W2004505284 @default.
• W3171296504 cites W2011171239 @default.
• W3171296504 cites W2015498490 @default.
• W3171296504 cites W2023831137 @default.
• W3171296504 cites W2049485244 @default.
• W3171296504 cites W2056614793 @default.
• W3171296504 cites W2057497740 @default.
• W3171296504 cites W2059409626 @default.
• W3171296504 cites W2072146810 @default.
• W3171296504 cites W2086799888 @default.
• W3171296504 cites W2089001508 @default.
• W3171296504 cites W2176975760 @default.
• W3171296504 cites W2217912240 @default.
• W3171296504 cites W2273940291 @default.
• W3171296504 cites W2312251484 @default.
• W3171296504 cites W2344469679 @default.
• W3171296504 cites W2399142781 @default.
• W3171296504 cites W2469673234 @default.
• W3171296504 cites W2579072476 @default.
• W3171296504 cites W2586155783 @default.
• W3171296504 cites W2593521092 @default.
• W3171296504 cites W2603783845 @default.
• W3171296504 cites W2762730686 @default.
• W3171296504 cites W2791247940 @default.
• W3171296504 cites W2793077529 @default.
• W3171296504 cites W2804995754 @default.
• W3171296504 cites W2806868042 @default.
• W3171296504 cites W2845865598 @default.
• W3171296504 cites W2885085843 @default.
• W3171296504 cites W2899884000 @default.
• W3171296504 cites W2910956612 @default.
• W3171296504 cites W2923400332 @default.
• W3171296504 cites W2963155579 @default.
• W3171296504 cites W3017403425 @default.
• W3171296504 cites W3094535680 @default.
• W3171296504 cites W3099859964 @default.
• W3171296504 cites W3104257088 @default.
• W3171296504 doi "https://doi.org/10.1016/j.apsusc.2021.150412" @default.
• W3171296504 hasPublicationYear "2021" @default.
• W3171296504 type Work @default.
• W3171296504 sameAs 3171296504 @default.
• W3171296504 citedByCount "9" @default.
• W3171296504 countsByYear W31712965042021 @default.
• W3171296504 countsByYear W31712965042022 @default.
• W3171296504 countsByYear W31712965042023 @default.
• W3171296504 crossrefType "journal-article" @default.
• W3171296504 hasAuthorship W3171296504A5000696411 @default.
• W3171296504 hasAuthorship W3171296504A5011263744 @default.
• W3171296504 hasAuthorship W3171296504A5016517520 @default.
• W3171296504 hasAuthorship W3171296504A5029603786 @default.
• W3171296504 hasAuthorship W3171296504A5047648026 @default.
• W3171296504 hasAuthorship W3171296504A5047695895 @default.
• W3171296504 hasAuthorship W3171296504A5072079888 @default.
• W3171296504 hasConcept C116834253 @default.
• W3171296504 hasConcept C121332964 @default.
• W3171296504 hasConcept C147789679 @default.
• W3171296504 hasConcept C154945302 @default.
• W3171296504 hasConcept C171250308 @default.
• W3171296504 hasConcept C17525397 @default.
• W3171296504 hasConcept C185592680 @default.
• W3171296504 hasConcept C186060115 @default.
• W3171296504 hasConcept C191897082 @default.
• W3171296504 hasConcept C192562407 @default.
• W3171296504 hasConcept C20625102 @default.
• W3171296504 hasConcept C2778560533 @default.
• W3171296504 hasConcept C2778755073 @default.
• W3171296504 hasConcept C36464697 @default.
• W3171296504 hasConcept C41008148 @default.
• W3171296504 hasConcept C45206210 @default.
• W3171296504 hasConcept C52859227 @default.
• W3171296504 hasConcept C59822182 @default.
• W3171296504 hasConcept C62520636 @default.
• W3171296504 hasConcept C86803240 @default.
• W3171296504 hasConcept C87976508 @default.
• W3171296504 hasConceptScore W3171296504C116834253 @default.
• W3171296504 hasConceptScore W3171296504C121332964 @default.
• W3171296504 hasConceptScore W3171296504C147789679 @default.
• W3171296504 hasConceptScore W3171296504C154945302 @default.
• W3171296504 hasConceptScore W3171296504C171250308 @default.
• W3171296504 hasConceptScore W3171296504C17525397 @default.
• W3171296504 hasConceptScore W3171296504C185592680 @default.
• W3171296504 hasConceptScore W3171296504C186060115 @default.
• W3171296504 hasConceptScore W3171296504C191897082 @default.

• next