| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Liebi, Marianne
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Unveiling breast cancer metastasis through an advanced X-ray imaging approachcitations
- 2024Phase-separated polymer blends for controlled drug delivery by tuning morphologycitations
- 2024Iron-carbohydrate complexes treating iron anaemia: Understanding the nano-structure and interactions with proteins through orthogonal characterisationcitations
- 2023SAXS imaging reveals optimized osseointegration properties of bioengineered oriented 3D-PLGA/aCaP scaffolds in a critical size bone defect model.citations
- 2023Small-angle scattering tensor tomography algorithm for robust reconstruction of complex texturescitations
- 2022Photoresponsive movement in 3D printed cellulose nanocompositescitations
- 2022Amphiphilic polymer co-network: a versatile matrix for tailoring the photonic energy transfer in wearable energy harvesting devicescitations
- 2020Validation study of small-angle X-ray scattering tensor tomographycitations
- 2019High-speed tensor tomography: iterative reconstruction tensor tomography (IRTT) algorithmcitations
- 2018Small-angle X-ray scattering tensor tomography : Model of the three-dimensional reciprocal-space map, reconstruction algorithm and angular sampling requirementscitations
- 2018Bioinspired Structural Hierarchy within Macroscopic Volumes of Synthetic Compositescitations
- 2015Six-dimensional real and reciprocal space small-angle X-ray scattering tomographycitations
- 2015Nanostructure surveys of macroscopic specimens by small-angle scattering tensor tomographycitations
Places of action
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article
Nanostructure surveys of macroscopic specimens by small-angle scattering tensor tomography
Abstract
The mechanical properties of many materials are based on the macroscopic arrangement and orientation of their nanostructure. This nanostructure can be ordered over a range of length scales. In biology, the principle of hierarchical ordering is often used to maximize functionality, such as strength and robustness of the material, while minimizing weight and energy cost. Methods for nanoscale imaging provide direct visual access to the ultrastructure (nanoscale structure that is too small to be imaged using light microscopy), but the field of view is limited and does not easily allow a full correlative study of changes in the ultrastructure over a macroscopic sample. Other methods of probing ultrastructure ordering, such as small-angle scattering of X-rays or neutrons, can be applied to macroscopic samples; however, these scattering methods remain constrained to two-dimensional specimens or to isotropically oriented ultrastructures. These constraints limit the use of these methods for studying nanostructures with more complex orientation patterns, which are abundant in nature and materials science. Here, we introduce an imaging method that combines small-angle scattering with tensor tomography to probe nanoscale structures in three-dimensional macroscopic samples in a non-destructive way. We demonstrate the method by measuring the main orientation and the degree of orientation of nanoscale mineralized collagen fibrils in a human trabecula bone sample with a spatial resolution of 25 micrometres. Symmetries within the sample, such as the cylindrical symmetry commonly observed for mineralized collagen fibrils in bone, allow for tractable sampling requirements and numerical efficiency. Small-angle scattering tensor tomography is applicable to both biological and materials science specimens, and may be useful for understanding and characterizing smart or bio-inspired materials. Moreover, because the method is non-destructive, it is appropriate for in situ measurements and allows, for example, the role of ultrastructure in the mechanical response of a biological tissue or manufactured material to be studied.