FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384393258> ?p ?o ?g. }

• W4384393258 endingPage "113366" @default.
• W4384393258 startingPage "113366" @default.
• W4384393258 abstract "Cu-based materials are the most commonly used electrocatalysts for CO2 reduction to ethylene. The selectivity of copper-based catalysts is affected by many complicated and coupled factors, such as composition, additive and morphology. Therefore, developing highly selective copper-based catalysts for ethylene production is still a significant challenge. This study constructs a CO2 reduction catalysis database using published experimental data. Machine learning (ML) models are developed to study the importance of various factors on the CO2 reduction activity of Cu-based materials. The ML model predicts that the needle-like structured Cu2O (110) composited with copper hydroxide, N-doped carbon black would benefit the Faradaic efficiency of ethylene production in KOH electrolyte. This data-guided ML framework provides a facile alternative method for the quick screening of active Cu-based catalysts towards CO2 reduction to ethylene." @default.
• W4384393258 created "2023-07-15" @default.
• W4384393258 creator A5004503548 @default.
• W4384393258 creator A5046850864 @default.
• W4384393258 creator A5051237478 @default.
• W4384393258 creator A5070169953 @default.
• W4384393258 creator A5076890873 @default.
• W4384393258 creator A5082752199 @default.
• W4384393258 date "2023-08-01" @default.
• W4384393258 modified "2023-10-17" @default.
• W4384393258 title "Machine-learning-guided prediction of Cu-based electrocatalysts towards ethylene production in CO2 reduction" @default.
• W4384393258 cites W1999186005 @default.
• W4384393258 cites W2020786104 @default.
• W4384393258 cites W2046236942 @default.
• W4384393258 cites W2082498048 @default.
• W4384393258 cites W2097968133 @default.
• W4384393258 cites W2126396070 @default.
• W4384393258 cites W2330888951 @default.
• W4384393258 cites W2340417484 @default.
• W4384393258 cites W2586675271 @default.
• W4384393258 cites W2783223040 @default.
• W4384393258 cites W2889313523 @default.
• W4384393258 cites W2900375438 @default.
• W4384393258 cites W2912281710 @default.
• W4384393258 cites W2913439825 @default.
• W4384393258 cites W2951783257 @default.
• W4384393258 cites W2964386462 @default.
• W4384393258 cites W2988615383 @default.
• W4384393258 cites W2996755838 @default.
• W4384393258 cites W2999315053 @default.
• W4384393258 cites W3023748241 @default.
• W4384393258 cites W3033694447 @default.
• W4384393258 cites W3035139483 @default.
• W4384393258 cites W3045783934 @default.
• W4384393258 cites W3049658640 @default.
• W4384393258 cites W3112816787 @default.
• W4384393258 cites W3124552913 @default.
• W4384393258 cites W3126772863 @default.
• W4384393258 cites W3159860044 @default.
• W4384393258 cites W3160363205 @default.
• W4384393258 cites W3170629733 @default.
• W4384393258 cites W3171151288 @default.
• W4384393258 cites W3174164587 @default.
• W4384393258 cites W3180783143 @default.
• W4384393258 cites W3184790901 @default.
• W4384393258 cites W3190325024 @default.
• W4384393258 cites W3208932648 @default.
• W4384393258 cites W3209858185 @default.
• W4384393258 cites W3212360274 @default.
• W4384393258 cites W4200080230 @default.
• W4384393258 cites W4200383003 @default.
• W4384393258 cites W4210538176 @default.
• W4384393258 cites W4220823505 @default.
• W4384393258 cites W4293112657 @default.
• W4384393258 cites W4293470067 @default.
• W4384393258 cites W4317951748 @default.
• W4384393258 cites W4318426300 @default.
• W4384393258 cites W4322624416 @default.
• W4384393258 doi "https://doi.org/10.1016/j.mcat.2023.113366" @default.
• W4384393258 hasPublicationYear "2023" @default.
• W4384393258 type Work @default.
• W4384393258 citedByCount "0" @default.
• W4384393258 crossrefType "journal-article" @default.
• W4384393258 hasAuthorship W4384393258A5004503548 @default.
• W4384393258 hasAuthorship W4384393258A5046850864 @default.
• W4384393258 hasAuthorship W4384393258A5051237478 @default.
• W4384393258 hasAuthorship W4384393258A5070169953 @default.
• W4384393258 hasAuthorship W4384393258A5076890873 @default.
• W4384393258 hasAuthorship W4384393258A5082752199 @default.
• W4384393258 hasConcept C111335779 @default.
• W4384393258 hasConcept C118792377 @default.
• W4384393258 hasConcept C127413603 @default.
• W4384393258 hasConcept C147789679 @default.
• W4384393258 hasConcept C159985019 @default.
• W4384393258 hasConcept C161790260 @default.
• W4384393258 hasConcept C17525397 @default.
• W4384393258 hasConcept C176933379 @default.
• W4384393258 hasConcept C178790620 @default.
• W4384393258 hasConcept C179104552 @default.
• W4384393258 hasConcept C185592680 @default.
• W4384393258 hasConcept C191897082 @default.
• W4384393258 hasConcept C192562407 @default.
• W4384393258 hasConcept C2524010 @default.
• W4384393258 hasConcept C2778597550 @default.
• W4384393258 hasConcept C33923547 @default.
• W4384393258 hasConcept C42360764 @default.
• W4384393258 hasConcept C44038986 @default.
• W4384393258 hasConcept C544778455 @default.
• W4384393258 hasConcept C68801617 @default.
• W4384393258 hasConceptScore W4384393258C111335779 @default.
• W4384393258 hasConceptScore W4384393258C118792377 @default.
• W4384393258 hasConceptScore W4384393258C127413603 @default.
• W4384393258 hasConceptScore W4384393258C147789679 @default.
• W4384393258 hasConceptScore W4384393258C159985019 @default.
• W4384393258 hasConceptScore W4384393258C161790260 @default.
• W4384393258 hasConceptScore W4384393258C17525397 @default.
• W4384393258 hasConceptScore W4384393258C176933379 @default.
• W4384393258 hasConceptScore W4384393258C178790620 @default.

• next