ORCID Profile
0000-0002-5956-9934
Current Organisation
RISE Research Institutes of Sweden
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Royal Society of Chemistry (RSC)
Date: 2017
DOI: 10.1039/C7SM01910F
Abstract: We performed computational screening of effective diffusivity in different configurations of cubes and cuboids compared with spheres and ellipsoids. In total, more than 1500 structures are generated and screened for effective diffusivity. We studied simple cubic and face-centered cubic lattices of spheres and cubes, random configurations of cubes and spheres as a function of volume fraction and polydispersity, and finally random configurations of ellipsoids and cuboids as a function of shape. We found some interesting shape-dependent differences in behavior, elucidating the impact of shape on the targeted design of granular materials.
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
DOI: 10.1039/D2NR03554E
Abstract: The 3D sub-nanometre distribution of in idual dopant species in organic semiconductors determines the electrical properties. The distribution and cluster shape have been determined by electron tomography and shown to depend on dopant concentration.
Publisher: Wiley
Date: 20-04-2018
DOI: 10.1111/JMI.12706
Abstract: We implement a massively parallel population Monte Carlo approximate Bayesian computation (PMC-ABC) method for estimating diffusion coefficients, sizes and concentrations of diffusing nanoparticles in liquid suspension using confocal laser scanning microscopy and particle tracking. The method is based on the joint probability distribution of diffusion coefficients and the time spent by a particle inside a detection region where particles are tracked. We present freely available central processing unit (CPU) and graphics processing unit (GPU) versions of the analysis software, and we apply the method to characterize mono- and bidisperse s les of fluorescent polystyrene beads.
Publisher: Wiley
Date: 16-01-2021
DOI: 10.1111/JMI.12989
Publisher: Wiley
Date: 04-05-2021
DOI: 10.1111/JMI.13007
Abstract: Phase‐separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water‐soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB‐SEM) tomography is a well‐established technique for high‐resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB‐SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB‐SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale‐space features. Finally, we facilitate further development of FIB‐SEM segmentation methods by making the data and software used open access.
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
DOI: 10.1039/D2RA04178B
Abstract: The EC/HPC/EtOH phase diagram could be estimated from the CLSM monitoring of the cross-sectional in situ phase separation. The findings of this work provide a good understanding of the structure evolution.
Publisher: Elsevier BV
Date: 08-2016
DOI: 10.1016/J.JMR.2016.06.009
Abstract: We present the pseudo 2-D relaxation model (P2DRM), a method to estimate multidimensional probability distributions of material parameters from independent 1-D measurements. We illustrate its use on 1-D T1 and T2 relaxation measurements of saturated rock and evaluate it on both simulated and experimental T1-T2 correlation measurement data sets. Results were in excellent agreement with the actual, known 2-D distribution in the case of the simulated data set. In both the simulated and experimental case, the functional relationships between T1 and T2 were in good agreement with the T1-T2 correlation maps from the 2-D inverse Laplace transform of the full 2-D data sets. When a 1-D CPMG experiment is combined with a rapid T1 measurement, the P2DRM provides a double-shot method for obtaining a T1-T2 relationship, with significantly decreased experimental time in comparison to the full T1-T2 correlation measurement.
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
DOI: 10.1039/D2SM00113F
Abstract: Porous phase-separated EC/HPC films are used to control the drug delivery rate. To the left, ex le of structure evolution during phase separation followed in situ with CLSM. To the right, possible structures after phase separation.
Publisher: Wiley
Date: 19-08-2020
DOI: 10.1111/JMI.12950
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1SM00044F
Abstract: Porous phase-separated films made of ethylcellulose (EC) and hydroxypropylcellulose (HPC) for controlled drug release were made using spin-coating. The method optimization gave good control over the film thickness and the length scale.
Publisher: American Chemical Society (ACS)
Date: 27-11-2017
No related grants have been discovered for Magnus Röding.