Investigation of the energy transfer network in upconverting nanoparticles

Upconverting nanoparticles (UCNPs) have emerged as promising luminescent materials for a wide range of applications, including bioimaging, drug delivery, and photovoltaics. The intricate network of energy transfer processes within UCNPs enables their unique ability to convert low-energy infrared (IR) radiation into higher-energy visible light through photon upconversion, presenting significant challenges for accurate modeling. Despite their broad applications, theoretical models of UCNPs remain incomplete, and current models fail to accurately reproduce all experimental results. This thesis presents a comprehensive comparison of prevalent modeling approaches with the aim of developing improved models that more faithfully reproduce experimental observations. Using the Judd-Ofelt theory, we calculated essential transition rate parameters, including electric dipole (ED), magnetic dipole (MD), multiphonon relaxation (MPR), and energy transfer (ET), using constants sourced from the literature. We implemented both Monte Carlo models and Ordinary Differential Equation (ODE) models. Using the calculated rate parameters, we simulate the energy transfer pathways in Yb³⁺-Er³⁺ and Yb³⁺-Tm³⁺ UCNPs. Simulation results from all models were compared with experimental data to evaluate their effectiveness in capturing key luminescent properties such as population evolution, lifetime, saturation curves, and spectral purity.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Zheng, Yuxuan

Advisor dc:contributor.advisor

• Peng, Chunte Sam

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/159106

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/159106