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Senast publicerade

Temperature-dependent reaction dynamics of CO-oxidation on Pt nanoparticles
(2026) Kaiser Olsson, Anton
Key to the continued development of catalysts is bridging the gap between research and industrial operating conditions, or operando conditions. Additionally, to achieve a deeper understanding of catalytic dynamics, single-particle analysis has gained popularity as a way to mitigate the loss of information brought about by ensembleaveraging effects. In an effort to both bridge the gap between the research and application while allowing probing at the single-particle level, a technique called nanoplasmonic sensing has emerged. Nanoplasmonic sensing relies on the inherent plasmonic properties of catalytic metal nanoparticles to probe catalytic reactions. In this thesis, the temperature-dependent reaction dynamics of CO-oxidation facilitated by Pt nanoparticles on a SiO2 substrate were studied, with a focus on the dynamics of the kinetic phase transitions of Pt nanoparticles between their COpoisoned and catalytically active states. This was investigated at both the ensemble and single-particle levels under operando conditions by performing temperatureprogrammed nanoplasmonic sensing on a nanoreactor chip hosting the Pt nanoparticles and the chemical reaction. Additionally, the effects of particle size and reactant ratio on the reaction dynamics were studied. To complement the nanoplasmonic sensing measurements, a structural analysis involving hyperspectral and SEM imaging was conducted. In this thesis, it was shown that temperature-dependent reaction dynamics could be obtained using nanoplasmonic sensing by applying a newly in-house-developed autofocusing software to counteract the drift in focus caused by the thermal expansion of the nanoparticles. Additionally, the critical temperatures of the kinetic phase transitions were evaluated using a statistical method called the bootstrap approach. One of the observed transitions aligned with the expected critical temperatures. However, an additional lower-temperature transition was also observed, and further analysis is required to evaluate whether this transition represents a real physical phenomenon or a measurement/data-treatment artifact. The nanoplasmonic sensing results were supported by the structural analysis, in which irreversible structural changes were observed in the nanoparticles, alluding to insufficient annealing. When assessing the effect of particle size, differences in the observed dynamics between ensemble and single-particle analyses were found, highlighting the need for further analysis at the single-particle level.
Investigating Robustness to Variation for Increased Net Output in Production Flow
(2026) Ringeby, Hugo
In modern industrial manufacturing processes, a deep understanding of process variation and the system’s inherent robustness is crucial for maintaining high product quality and at the same time improving the process performance. This master thesis project which has been performed in collaboration with SKF Sverige AB aims to systematically analyze and identify the factors that affect robustness, in one of the company’s production flows for producing rollers. The primary objective of the project is to enhance machine utilization and solve a specific cycle time problem within the existing production line, with a clear requirement that the optimization performed should not result in sub-optimization in other parts of the system. In order to approach the problem systematically and with a data-driven approach, the study is structured around Six Sigma, and its proven DMAIC- framework (Define, Measure, Analyze, Improve, Control). In the project’s early phase, a combination of qualitative and quantitative tools were applied, including Affinity-Interrelationship Method (AIM), detailed process mapping, and Cause & Effect- Matrix. These methods were applied to systematically break down the problem, filter out irrelevant variables, and determine the current state of knowledge. Following this, the reliability of the current measuring equipment was assessed using a Measurement System Analysis (MSA) to evaluate whether the existing system was to actually detect the upcoming variation. To then isolate and analyze how key parameters such as tool wear, cycle time variations, and the choice of machine affect the process’s outcome, Design of Experiments were applied. The experimental design was then complemented by Bayesian optimization to find the optimal process parameters, in an efficient way. The results from the statistical analyses highlight the measurement equipment’s actual ability to detect real product variation. Furthermore, the study identifies which of the tested parameters have a statistically significant impact on the process outcome, and separates them from what can be defined as process noise. Based on these insights, the study serves as the foundation for improvements that will enhance the resource efficiency in the bottleneck machine and increase the net output of the production flow.
KULLAGERHUSEN
(2026) Andreasson, Samuel
TELETEATERN
(2026) Wagnsgård Djerf, Saga
Mellan Raderna
(2026) Karvo, Sabina