Lanthanide nanoparticles offer potential in nanoscale photonics due to their high lifetime and quantum yield. However, surface quenching degrades these properties, requiring time-consuming experimental optimization. Here, we present a versatile Monte-Carlo approach that accurately predicts the lifetimes and quantum yields of lanthanide nanoparticles. Based on a Bayesian optimization algorithm, we optimize the geometry and doping concentration of nanocrystals resulting in simulated quantum yields of >60% and lifetimes of >30μs. This approach saves 95% time compared to experimental methods and holds promise for applications such as nanoparticle lasers or quantum memories.
Water electrolysis in Proton Exchange Membrane Water Electrolysis (PEMWE) cells is important for sustainable energy conversion. The efficiency and longevity of these cells depend on operating conditions such as the temperature of the membrane. We employ a fiber-based sensor using lanthanide-doped nanoparticles as nanothermometers to measure the temperature at the cell’s membrane for different operational conditions. In the future, this sensor can be used to optimize the cell’s operational parameters and is also applicable in strong electromagnetic fields, for example in battery technology or magnetic resonance tomography.
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