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McSAS3 is a refactored software package for fitting large batches of (X-ray or Neutron) scattering data. It uses a Monte-Carlo acceptance-rejection algorithm to optimize model parameters - ideal for analysis of size-disperse scatterers.
The refactored code can exploit multiprocessing, traceably stores (multiple) results in the output file, and allows for re-histogramming of previous optimizations. Besides analysis of large batches, it can also be integrated in automated data processing pipelines.
The live demonstration will show how to use the software, what its limitations are, and what outcomes can look like for batches of results.
Simulational tools are applied to investigate the physical properties of nanoparticles.
For the description of radioactive gold nanoparticles, particles scattering simulations are performed with the Geant4 monte carlo simulation toolkit.
The temperature dependent behaviour of the magnetization dynamics of different magnetic nanoparticles are simulated with the object oriented micormagnetic framework (OOMMF).
In fields involving irradiated aqueous solutions, such as radiotherapy and nuclear waste remediation, it is often unclear whether the principal reactive species are OH° radicals or secondary (low-energy) electrons. This is mostly because both are rapidly attenuated in water. Presently a large part of the evidence for the involvement of low-energy electrons in biological radiation damage is based on 'dry' DNA samples. We demonstrate irradiation of DNA in solution by direct injection of electrons through a 40-nm thin SiO2 membrane, followed by in-situ detection of the DNA damage by a fluorescence-based method. Corresponding Monte Carlo simulations show that the spatial distribution of ionizing events in water with respect to the membrane is controlled by the electron impact energy. By immobilizing DNA to the solution side of the membrane, and because dynamics and reaction ranges of OH° radicals and low-energy electrons are dramatically different, it is possible to tune into the OH° radical or into the electron 'reaction modes' by simply changing the electron impact energy. Such experiments have the potential to provide important information on the radio-sensitivity at a level of a single biomolecule and to contribute to the development of new dosage concepts.
Partial least squares (PLS) regression-based methods have been proven to be a good alternative for quantification in X-ray fluorescence spectroscopy. These methods are fast and easy to use though giving satisfactory results under certain conditions. One of these conditions is the necessity of having a great number of spectra to build the model (training set). The choice of the constituent concentration range in the training set has a big influence on the accuracy of the model. Better accuracy is obtained if the model is built in relatively narrow regions containing (or close to) the real concentration value.
In the present work, Monte Carlo (MC) simulated spectra are used to form the training set. The advantage to use MC generated training spectra is the unlimited availability of perfect standards.
This paper aims to improve the accuracy of the method by introducing a multiple step procedure in order to build the PLS model using narrow concentration range close to (or containing) the real concentration values in the samples to be measured.
This approach consists of an initial guess of the constituents' concentrations and a preliminary PLS model before building the final model. The prediction of ten MC simulated alloy standard samples containing Ti, Mn, Fe, Co, Cu, Zn, Sr, Zr, and Mo using this method allowed to have average relative prediction errors less than 5% for elements with narrow concentration ranges.