Details
| Original language | English |
|---|---|
| Title of host publication | Colloidal Nanoparticles for Biomedical Applications XIX |
| Editors | Marek Osinski, Antonios G. Kanaras |
| Publisher | SPIE |
| ISBN (electronic) | 9781510669772 |
| Publication status | Published - 13 Mar 2024 |
| Event | Colloidal Nanoparticles for Biomedical Applications XIX 2024 - San Francisco, United States Duration: 27 Jan 2024 → 29 Jan 2024 |
Publication series
| Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
|---|---|
| Volume | 12859 |
| ISSN (Print) | 1605-7422 |
Abstract
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.
Keywords
- Bayesian Optimization, Core, Lanthanides, Lifetime, Monte-Carlo Simulations, Nanocrystals, Quantum Yield, Shell
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Materials Science(all)
- Biomaterials
- Medicine(all)
- Radiology Nuclear Medicine and imaging
Cite this
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- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Colloidal Nanoparticles for Biomedical Applications XIX. ed. / Marek Osinski; Antonios G. Kanaras. SPIE, 2024. 1285905 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12859).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Numerical Optimization of the Lifetime and Quantum Yield of LiYF4:Pr3+ Nanocrystals
AU - Spelthann, Simon
AU - Thiem, Jonas
AU - Melchert, Oliver
AU - Komban, Rajesh
AU - Steinke, Michael
AU - Gimmler, Christoph
AU - Demircan, Ayhan
AU - Ruehl, Axel
AU - Ristau, Detlev
N1 - Funding Information: S.S. and M.S. acknowledge the German Federal Ministry of Education and Research for funding the project EMDeN (WiVoPro, FKZ: 13N16298). Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2123 Quantum Frontiers - 390837967. O.M., A.D., and D. R. would like to thank the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for partly funding this work under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC-2122, Project ID 390833453). R. K. and C. G. would like to thank the Free and Hanseatic City of Hamburg, Germany for the financial support. The numerical results presented here were achieved by computations carried out on the LUH cluster system funded by the Leibniz Universität Hannover, the Niedersächsisches Ministerium für Wissenschaft und Kultur (MWK, Lower Saxony Ministry of Science and Culture), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation).
PY - 2024/3/13
Y1 - 2024/3/13
N2 - 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.
AB - 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.
KW - Bayesian Optimization
KW - Core
KW - Lanthanides
KW - Lifetime
KW - Monte-Carlo Simulations
KW - Nanocrystals
KW - Quantum Yield
KW - Shell
UR - http://www.scopus.com/inward/record.url?scp=85190439090&partnerID=8YFLogxK
U2 - 10.1117/12.3000339
DO - 10.1117/12.3000339
M3 - Conference contribution
AN - SCOPUS:85190439090
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Colloidal Nanoparticles for Biomedical Applications XIX
A2 - Osinski, Marek
A2 - Kanaras, Antonios G.
PB - SPIE
T2 - Colloidal Nanoparticles for Biomedical Applications XIX 2024
Y2 - 27 January 2024 through 29 January 2024
ER -