Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Trond R. Henninen"'
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract The understanding of crystal growth mechanisms has broadened substantially. One significant advancement is based in the conception that the interaction between particles plays an important role in the growth of nanomaterials. This is in cont
Externí odkaz:
https://doaj.org/article/86f0015a905e4eb2b9cd4129b93a3d87
Publikováno v:
Applied Microscopy, Vol 50, Iss 1, Pp 1-9 (2020)
Abstract We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain S $\mathcal {S}$ to a target domain C $\mathcal {C}$ , where S $\mathc
Externí odkaz:
https://doaj.org/article/4cdd9b5bd32249f797bacb5580bf2191
Publikováno v:
Nanoscale
Elementary atomic mechanisms underlying nanoparticle growth in liquids are largely unexplored and mostly a subject of conjectures based on theory and indirect experimental insights. Direct, experimental observation of such processes at an atomic leve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26f85d9b07ecab980c78498e33022a31
https://doi.org/10.1039/d0nr06541b
https://doi.org/10.1039/d0nr06541b
Publikováno v:
Nano Letters
The formation of nanocrystals is at the heart of various scientific disciplines, but the atomic mechanisms underlying the early stages of crystallization from supersaturated solutions are still rather unclear. Here, we used in situ liquid-phase scann
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1acc5ab0b947dafa5524c1825c2b3924
Publikováno v:
ChemNanoMat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd29458733f77c4aff76f14d33135b76
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Inverse problems in image processing, phase imaging, and computer vision often share the same structure of mapping input image(s) to output image(s) but are usually solved by different application-specific algorithms. Deep convolutional neural networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc275b5ffa138a8594d7407755630bf8
Publikováno v:
Angewandte Chemie International Edition
Little is known about metallic clusters consisting merely of a dozen of atoms or even less, despite of their importance in catalysis and crystal nucleation. Scanning transmission electron microscopy (STEM) provides direct atomic structure information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39d8a50022c3c1852751fb8e836f8d4b
https://pubmed.ncbi.nlm.nih.gov/31682061
https://pubmed.ncbi.nlm.nih.gov/31682061
Publikováno v:
Micron
Ionic liquids (ILs) feature negligibly low vapor pressures and can thus be freely introduced into the high vacuum of a transmission electron microscope. With this extraordinary property, the ILs offer a powerful tool for in situ transmission electron
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce93bc60ae62faf1e96607b9c25b1aa0
Publikováno v:
ChemNanoMat. 7:100-100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c64b41b88d848cdf50094a79babca6ef
https://doi.org/10.1002/cnma.202100015
https://doi.org/10.1002/cnma.202100015
Publikováno v:
Applied Microscopy
Applied Microscopy, Vol 50, Iss 1, Pp 1-9 (2020)
Applied Microscopy, Vol 50, Iss 1, Pp 1-9 (2020)
We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain $\mathcal {S}$ S to a target domain $\mathcal {C}$ C , where $\mathcal {S}$ S i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f92828c22ae09c3f4491a8eb8a19dc1f