Matches in SemOpenAlex for { <https://semopenalex.org/work/W3153697435> ?p ?o ?g. }
- W3153697435 endingPage "129872" @default.
- W3153697435 startingPage "129872" @default.
- W3153697435 abstract "Rational development of antifouling materials is of great importance for fundamental research and real-world applications. However, current experimental designs and computational modelings of antifouling materials still retain empirical flavor due to the data complexity of polymers and their associated structures/properties. In this work, we developed a data-driven, machine learning workflow, in combination with an in-house benchmark dataset of antifouling polymer brushes, to discover the potential antifouling property of existing polymer brushes using the descriptor-based artificial neural network (ANN) model and design the new antifouling polymer brushes using the group-based supporting vector regression (SVR) model. The resultant two machine learning models not only demonstrated their reliability, predictivity, and applicability, but also established the composition-structure–property relationships using both descriptors and functional groups. Finally, we synthesized different repurposed and newly designed polymer brushes, as predicted by ANN and SVR models, all of which exhibited excellent surface resistance to protein adsorption from undiluted human blood serum and plasma at optimal film thicknesses. Overall, our data-driven machine learning models can be used as an intelligent tool for determining, repurposing, and designing new superior antifouling materials beyond polymer brushes." @default.
- W3153697435 created "2021-04-26" @default.
- W3153697435 creator A5002873170 @default.
- W3153697435 creator A5015997210 @default.
- W3153697435 creator A5020042453 @default.
- W3153697435 creator A5020728100 @default.
- W3153697435 creator A5030446424 @default.
- W3153697435 creator A5059555241 @default.
- W3153697435 creator A5086846209 @default.
- W3153697435 date "2021-09-01" @default.
- W3153697435 modified "2023-10-16" @default.
- W3153697435 title "Machine Learning-Enabled Repurposing and Design of Antifouling Polymer Brushes" @default.
- W3153697435 cites W1891405699 @default.
- W3153697435 cites W1965063770 @default.
- W3153697435 cites W1986170239 @default.
- W3153697435 cites W1993659024 @default.
- W3153697435 cites W1994930875 @default.
- W3153697435 cites W1996275832 @default.
- W3153697435 cites W2007205934 @default.
- W3153697435 cites W2019366993 @default.
- W3153697435 cites W2021060402 @default.
- W3153697435 cites W2030786859 @default.
- W3153697435 cites W2032805016 @default.
- W3153697435 cites W2039609522 @default.
- W3153697435 cites W2045260522 @default.
- W3153697435 cites W2048808551 @default.
- W3153697435 cites W2059432708 @default.
- W3153697435 cites W2067295330 @default.
- W3153697435 cites W2089153310 @default.
- W3153697435 cites W2124830001 @default.
- W3153697435 cites W2136769624 @default.
- W3153697435 cites W2164579817 @default.
- W3153697435 cites W2251214836 @default.
- W3153697435 cites W2295022485 @default.
- W3153697435 cites W2312423631 @default.
- W3153697435 cites W2314569573 @default.
- W3153697435 cites W2319169138 @default.
- W3153697435 cites W2562501556 @default.
- W3153697435 cites W2770180304 @default.
- W3153697435 cites W2787169910 @default.
- W3153697435 cites W2793231882 @default.
- W3153697435 cites W2797402103 @default.
- W3153697435 cites W2801274316 @default.
- W3153697435 cites W2802368809 @default.
- W3153697435 cites W2806681928 @default.
- W3153697435 cites W2808899873 @default.
- W3153697435 cites W2888542706 @default.
- W3153697435 cites W2895812382 @default.
- W3153697435 cites W2897119344 @default.
- W3153697435 cites W2912104254 @default.
- W3153697435 cites W2913300369 @default.
- W3153697435 cites W2919338369 @default.
- W3153697435 cites W2937307539 @default.
- W3153697435 cites W2940242941 @default.
- W3153697435 cites W2945288028 @default.
- W3153697435 cites W2945892680 @default.
- W3153697435 cites W2952832141 @default.
- W3153697435 cites W2963784900 @default.
- W3153697435 cites W2968923792 @default.
- W3153697435 cites W2969922401 @default.
- W3153697435 cites W2970765526 @default.
- W3153697435 cites W2972765272 @default.
- W3153697435 cites W2975661862 @default.
- W3153697435 cites W2978186833 @default.
- W3153697435 cites W2981521947 @default.
- W3153697435 cites W2984038639 @default.
- W3153697435 cites W2991722393 @default.
- W3153697435 cites W2996982841 @default.
- W3153697435 cites W3010511960 @default.
- W3153697435 cites W3010857515 @default.
- W3153697435 cites W3012406820 @default.
- W3153697435 cites W3014806814 @default.
- W3153697435 cites W3015982918 @default.
- W3153697435 cites W3023659252 @default.
- W3153697435 cites W3032217692 @default.
- W3153697435 cites W3033220782 @default.
- W3153697435 cites W3040552910 @default.
- W3153697435 cites W3045016385 @default.
- W3153697435 cites W3072590200 @default.
- W3153697435 cites W3091706193 @default.
- W3153697435 cites W3094680454 @default.
- W3153697435 cites W3098062221 @default.
- W3153697435 cites W3098905070 @default.
- W3153697435 cites W3104182741 @default.
- W3153697435 cites W3110951414 @default.
- W3153697435 cites W3120624571 @default.
- W3153697435 cites W3130882762 @default.
- W3153697435 doi "https://doi.org/10.1016/j.cej.2021.129872" @default.
- W3153697435 hasPublicationYear "2021" @default.
- W3153697435 type Work @default.
- W3153697435 sameAs 3153697435 @default.
- W3153697435 citedByCount "15" @default.
- W3153697435 countsByYear W31536974352021 @default.
- W3153697435 countsByYear W31536974352022 @default.
- W3153697435 countsByYear W31536974352023 @default.
- W3153697435 crossrefType "journal-article" @default.
- W3153697435 hasAuthorship W3153697435A5002873170 @default.
- W3153697435 hasAuthorship W3153697435A5015997210 @default.