Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309299819> ?p ?o ?g. }
- W4309299819 abstract "Abstract X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-induced parasite reaction and sample damage have driven research efforts to reduce beam dosage. Here, we report a machine-learning-based image processing method that can significantly reduce data acquisition time and X-ray dose, outperforming conventional approaches like Filtered-Back Projection, maximum-likelihood, and model-based maximum-a-posteriori probability. Applying machine learning, we achieve ultrafast nano-tomography with sub-10 second data acquisition time and sub-50 nm pixel resolution in a transmission X-ray microscope. We apply our algorithm to study dynamic morphology changes in a lithium-ion battery cathode under a heating rate of 50 o C min −1 , revealing crack self-healing during thermal annealing. The proposed method can be applied to various tomography modalities." @default.
- W4309299819 created "2022-11-25" @default.
- W4309299819 creator A5064099806 @default.
- W4309299819 creator A5067502218 @default.
- W4309299819 creator A5088524686 @default.
- W4309299819 date "2022-11-16" @default.
- W4309299819 modified "2023-10-09" @default.
- W4309299819 title "Sub-10 second fly-scan nano-tomography using machine learning" @default.
- W4309299819 cites W1480844027 @default.
- W4309299819 cites W1521266227 @default.
- W4309299819 cites W1901616594 @default.
- W4309299819 cites W1979822400 @default.
- W4309299819 cites W1990919278 @default.
- W4309299819 cites W1995277257 @default.
- W4309299819 cites W2003663624 @default.
- W4309299819 cites W2011156041 @default.
- W4309299819 cites W2025836343 @default.
- W4309299819 cites W2029744079 @default.
- W4309299819 cites W2035797964 @default.
- W4309299819 cites W2085805420 @default.
- W4309299819 cites W2093072099 @default.
- W4309299819 cites W2098239148 @default.
- W4309299819 cites W2098499024 @default.
- W4309299819 cites W2110733023 @default.
- W4309299819 cites W2113432364 @default.
- W4309299819 cites W2117962575 @default.
- W4309299819 cites W2164269215 @default.
- W4309299819 cites W2164278908 @default.
- W4309299819 cites W2165565866 @default.
- W4309299819 cites W2279977790 @default.
- W4309299819 cites W2548485319 @default.
- W4309299819 cites W2584483805 @default.
- W4309299819 cites W2588978745 @default.
- W4309299819 cites W2592141848 @default.
- W4309299819 cites W2595276417 @default.
- W4309299819 cites W2604583038 @default.
- W4309299819 cites W2772629285 @default.
- W4309299819 cites W2784174362 @default.
- W4309299819 cites W2785910705 @default.
- W4309299819 cites W2789171981 @default.
- W4309299819 cites W2793538356 @default.
- W4309299819 cites W2888067721 @default.
- W4309299819 cites W2890658905 @default.
- W4309299819 cites W2891158090 @default.
- W4309299819 cites W2899314786 @default.
- W4309299819 cites W2899995781 @default.
- W4309299819 cites W2919115771 @default.
- W4309299819 cites W2943890295 @default.
- W4309299819 cites W2946310892 @default.
- W4309299819 cites W2949333608 @default.
- W4309299819 cites W2953097421 @default.
- W4309299819 cites W2998857833 @default.
- W4309299819 cites W3004789869 @default.
- W4309299819 cites W3005671512 @default.
- W4309299819 cites W3006788756 @default.
- W4309299819 cites W3012755458 @default.
- W4309299819 cites W3035496815 @default.
- W4309299819 cites W3046910519 @default.
- W4309299819 cites W3048594975 @default.
- W4309299819 cites W3081711845 @default.
- W4309299819 cites W3125964352 @default.
- W4309299819 cites W3153699651 @default.
- W4309299819 cites W3214378865 @default.
- W4309299819 doi "https://doi.org/10.1038/s43246-022-00313-8" @default.
- W4309299819 hasPublicationYear "2022" @default.
- W4309299819 type Work @default.
- W4309299819 citedByCount "1" @default.
- W4309299819 countsByYear W43092998192023 @default.
- W4309299819 crossrefType "journal-article" @default.
- W4309299819 hasAuthorship W4309299819A5064099806 @default.
- W4309299819 hasAuthorship W4309299819A5067502218 @default.
- W4309299819 hasAuthorship W4309299819A5088524686 @default.
- W4309299819 hasBestOaLocation W43092998191 @default.
- W4309299819 hasConcept C111919701 @default.
- W4309299819 hasConcept C11413529 @default.
- W4309299819 hasConcept C120665830 @default.
- W4309299819 hasConcept C121332964 @default.
- W4309299819 hasConcept C126980161 @default.
- W4309299819 hasConcept C138268822 @default.
- W4309299819 hasConcept C141379421 @default.
- W4309299819 hasConcept C154945302 @default.
- W4309299819 hasConcept C160633673 @default.
- W4309299819 hasConcept C163716698 @default.
- W4309299819 hasConcept C163985040 @default.
- W4309299819 hasConcept C192562407 @default.
- W4309299819 hasConcept C198531522 @default.
- W4309299819 hasConcept C41008148 @default.
- W4309299819 hasConcept C97355855 @default.
- W4309299819 hasConceptScore W4309299819C111919701 @default.
- W4309299819 hasConceptScore W4309299819C11413529 @default.
- W4309299819 hasConceptScore W4309299819C120665830 @default.
- W4309299819 hasConceptScore W4309299819C121332964 @default.
- W4309299819 hasConceptScore W4309299819C126980161 @default.
- W4309299819 hasConceptScore W4309299819C138268822 @default.
- W4309299819 hasConceptScore W4309299819C141379421 @default.
- W4309299819 hasConceptScore W4309299819C154945302 @default.
- W4309299819 hasConceptScore W4309299819C160633673 @default.
- W4309299819 hasConceptScore W4309299819C163716698 @default.
- W4309299819 hasConceptScore W4309299819C163985040 @default.
- W4309299819 hasConceptScore W4309299819C192562407 @default.