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| 005 | 20240610120631.0 | ||
| 024 | 7 | _ | |a 10.1038/s42256-022-00521-4 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-03229 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Gompper, Gerhard |0 P:(DE-Juel1)130665 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Delivering microcargo with artificial microtubules |
| 260 | _ | _ | |a [London] |c 2022 |b Springer Nature Publishing |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
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| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a Kein Post-print vorhanden |
| 520 | _ | _ | |a The controlled transport of microcargos poses major challenges, such as how to beat the low efficiency of diffusive transport, how to employ active processes effectively, how to overcome the limitations due to thermal noise, how to switch transport on and off as needed, how to control the transport direction, and how to cope with complex environments and confined spaces. Nature has evolutionarily developed a large variety of intelligent solutions to move cargo around at the level of biological cells. On the subcellular level, this is mostly achieved by motor proteins, which pull microcargo unidirectionally along the cellular highways that are established by microtubules, which are long and stiff polar filaments within the cell. On the cellular level, propulsion and transport is mostly achieved by the active motion of flagella and cilia, or by active body deformation. Here, eukaryotic flagella and cilia generate propulsion by a snake-like travelling bending wave, while prokaryotic flagella are helical and generate propulsion by a rotational motion that is induced by a rotary motor in the cell wall. Inspired by cytoskeletal motors that carry vesicles along microtubule highways in biological cells, Hongri Gu et al.1 have developed an artificial microtubule (AMT), a structured microfibre with embedded micromagnets that serve as stepping stones to guide particles rapidly through flow networks. |
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| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 773 | _ | _ | |a 10.1038/s42256-022-00521-4 |g Vol. 4, no. 8, p. 663 - 664 |0 PERI:(DE-600)2933875-X |n 8 |p 663 - 664 |t Nature machine intelligence |v 4 |y 2022 |x 2522-5839 |
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