Periodic Reporting for period 3 - MUMOTT (Multi-Modal Tensor Tomography)
Reporting period: 2023-05-01 to 2024-10-31
Capture structures without looking at them directly, but rather by probing their interaction with electromagnetic waves - this is the basic principle for the new multi-modal tensor tomography developed in this research project. It enables to study the arrangement of nanostructures in macroscopic samples, six orders of magnitude larger than its building blocks. In a standard computed tomography (CT) the inside of three-dimensional objects or the human body can be routinely studied, providing a grey-scale map of electron density in each sub-volume, called voxel. Compared to this scalar value (i.e. grey scale), with tensor tomography we can extract much more information in each voxel, in particular the three-dimensional orientation of the local nanostructure. Different modes of tensor tomography are explored within this project, using visible light and X-rays. An open-source software package is being developed, with the aim of making the developed methods available to the scientific community. Over the interaction of X-rays with the sample not only orientations can be obtained, but also size and shape can be retrieved for which we work on new algorithms to extract all information contained, including the exploration of coherence within this method. With visible light the orientation of ultrastructures, i.e. structures which are smaller than what can be resolved with traditional light microscopy, can be probed with the change in polarization state. Within MUMOTT we are combining this approach with tensor tomography, in order to investigate the local structure spatially resolved in three-dimensional samples.
Apart from the methodology framework we are implementing the different modes to prove their capability in different application examples in materials and bio-science. This will serve to received significant attention in the relevant scientific communities, which will stimulate the future use of the methods being developed. As examples of material science application, we will investigate the structure of fiber composites and s reveal how the arrangement of nanoparticles in a plasmonic composite is connected to its sensing capabilities. In bio-science we are focusing on the extra-cellular matrix, in particular collagen, and how it is changing in pathological cases. Concretely we study the disruptive collagen network in liver fibrosis and changes in the extracellular matrix in connection to metastatic breast cancer.
Apart from the methodology framework we are implementing the different modes to prove their capability in different application examples in materials and bio-science. This will serve to received significant attention in the relevant scientific communities, which will stimulate the future use of the methods being developed. As examples of material science application, we will investigate the structure of fiber composites and s reveal how the arrangement of nanoparticles in a plasmonic composite is connected to its sensing capabilities. In bio-science we are focusing on the extra-cellular matrix, in particular collagen, and how it is changing in pathological cases. Concretely we study the disruptive collagen network in liver fibrosis and changes in the extracellular matrix in connection to metastatic breast cancer.
We have developed a python-based software package for the analysis of tensor tomography measurements (https://mumott.org(opens in new window)) and released V1.0. The focus of the development was to create a modular setting for tensor tomography, where different tensor field representation, i.e. models to describe the matter-structure interaction in each sub-volume of the tomography, are implemented. It offers flexibility in the projection and optimization algorithms used, which are central elements for minimizing the error between the measured data and the resulting model, from which the desired information of orientation and structural parameters can be retrieved. We have also started to include workflows and tutorials in order to make the code accessible for the scientific community and will allow its integration in data pipeline of beamlines at different synchrotrons in Europe and worldwide. A publication shows the performance of the new code for SAXS tensor tomography for simulations as well as experimental data retrieving the orientation of mineralized collagen, the nano-building blocks of bone. We there demonstrated that more complex textures can be studied, which means for example that not only a single main orientation in each sub-volume can be retrieved, but multiple orientation of crossing-bundles of collagen. As a novel modality of tensor tomography we have started to built the mathematical framework for using the polarization state of visible light to retrieve orientation and the algorithm is being built to be included within the MUMOTT software package. In plastic-plasmonic composites, plasmonic metal nanoparticles which can be used for example to detect hydrogen gas are distributed within a polymer matrix, which provides support and stability for the nanoparticles to make them applicable as sensors. The distribution of the nanoparticles within the macroscopic sensor material is thereby crucial for its performance. SAXS tensor tomography measurements have been successfully conducted to study the effect of the polymer processing, and is now used also as model system for the method development in this project. For biomedical application of the method on soft tissue, sample preparation is a very crucial step, we have performed a systematic study using different protocols and showed that standard protocols used in standard histology should be adapted in order not to compromise the signal from X-ray scattering.
We are working on the realization of tensor tomography of the polarization state of visible light, both towards an experimental setup as well as the algorithm implementation into our MUMOTT software package. This modality will boost the applicability of tensor tomography significantly compared to its use with synchrotron radiation due to easy availability and access. On the other hand, the use of X-rays offers significant advantages, apart from permeability which increases the type of samples which can be studied, from the scattering of X-rays much more information on shape and size of the nanostructure can be obtained. We are working on algorithms to extract this information from X-ray scattering tensor tomography. This will be explored first on the pastic plasmonic composite material, since the metallic nanoparticles present a well defined model system in terms of nanoparticle shape and size. We expect from this a toolbox of algorithms, each with different advantages and disadvantages, applicable as stand-alone or combined depending on the problem to be solved. For the biological example, we expect to understand which changes of the disruptive collagen network in the course of disease of liver fibrosis are connected to irreversibility which eventually result in liver cirrhosis. The same tools characterizing collagen will be used as well in studying different types of breast cancer on a mouse model, with a focus on investigating the changes in extracellular matrix for metastatic and non-metastatic tumors.