The combustion of metal fuels as energy carriers in a closed-cycle carbon-free process is a promising approach for reducing CO₂ emissions in the energy sector. For a possible large-scale implementation, the influence of process conditions on particle properties and vice versa has to be well understood. In this study, the influence of different fuel–air equivalence ratios on particle morphology, size and degree of oxidation in an iron–air model burner is investigated by means of small- and wide-angle X-ray scattering, laser diffraction analysis and electron microscopy. The results show a decrease in median particle size and an increase in the degree of oxidation for leaner combustion conditions. The difference of 1.94 μm in median particle size between lean and rich conditions is twentyfold greater than the expected amount and can be connected to an increased intensity of microexplosions and nanoparticle formation for oxygen-rich atmospheres. Furthermore, the influence of the process conditions on the fuel usage efficiency is investigated, yielding efficiencies of up to 0.93. Furthermore, by choosing a suitable particle size range of 1 to 10 μm, the amount of residual iron content can be minimized. The results emphasize that particle size plays a key role in optimizing this process for the future.

• Magnetic seeded filtration can efficiently separate microplastics from cellulose. • Enzymatic and oxidative treatments only marginally affect separation efficiencies. • Hydrophobic interactions govern the hetero-agglomeration process. • The agglomeration behavior of PVC differs from that of other microplastics.

Screw design in the extrusion process has an important effect on the distribution of material through the extruder, resulting in partially filled sections in the processing zone. Accordingly, the local accumulation of material in the extruder leads to variations in material strain conditions and also influences the local residence time of the material in a given screw section. This work evaluates particle dispersion in anode slurry considering three different screw arrangements. The particle size distribution is considered as a quality parameter representing the microstructure of the battery slurry components and their distribution. Numerical simulation of the material flow behavior through a laboratory extruder was performed to investigate the filling ratios and resulting shear rates for different screw designs and process conditions. The importance of process parameters and a suitable screw configuration to achieve specific particle sizes in battery slurry is discussed.

The current state of separation technology often neglects the multidimensional nature of real particle systems, which are distributed not only in terms of size, but also in terms of other properties, such as surface charge. Therefore, the aim of this study is to experimentally investigate the applicability of magnetic seeded filtration as a multidimensional separation process. Magnetic seed particles are added to a multisubstance suspension, and a selective heteroagglomeration with the nonmagnetic target particles is induced, allowing for an easy subsequent magnetic separation. The results show that high separation efficiencies can be achieved and that the parameters pH and ionic strength govern the agglomeration process. Selective separation based on surface charge was observed, but undesirable heteroagglomeration processes between the target particles lead to a loss of selectivity. Particle size was clearly identified as a second relevant separation feature, and its partially opposite influence on collision frequency and collision efficiency was discussed. Finally, experimental data of multidimensional separation are presented, in which a size-distributed two-substance suspension is separated into defined size and material fractions in a single process step. This study highlights the need for multidimensional evaluation in general and the potential of magnetic seeded filtration as a promising separation technique.

Multiple linear regression models were trained to predict the degree of substitution (DS) of cellulose acetate based on raw infrared (IR) spectroscopic data. A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from ¹H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.069 in DS on test data, demonstrating higher accuracy compared to the manual evaluation based on peak integration. Limiting the model to physically relevant areas unexpectedly showed the $${\hbox {C}{-}\hbox {H}}$$ peak to be the strongest predictor of DS. By applying a n-best feature selection algorithm based on the F-statistic of the Pearson correlation coefficient, several relevant areas were identified and the optimized model achieved an improved MAE of 0.052. Predicting the DS of other cellulose acetate data sets yielded similar accuracy, demonstrating that the developed models are robust and suitable for efficient and accurate routine evaluations. The model solely trained on cellulose acetate was further able to predict the DS of other cellulose esters with an accuracy of $$\approx 0.1-0.2$$ in DS and model architectures for a more general analysis of cellulose esters were proposed.