Upconversion nanoparticles (UCNPs)
are some of the most promising
nanomaterials for bioanalytical and biomedical applications. One important
challenge to be still solved is how UCNPs can be optimally implemented
into Förster resonance energy transfer (FRET) biosensing and
bioimaging for highly sensitive, wash-free, multiplexed, accurate,
and precise quantitative analysis of biomolecules and biomolecular
interactions. The many possible UCNP architectures composed of a core
and multiple shells doped with different lanthanoid ions at different
ratios, the interaction with FRET acceptors at different possible
distances and orientations via biomolecular interaction, and the many
and long-lasting energy transfer pathways from the initial UCNP excitation
to the final FRET process and acceptor emission make the experimental
determination of the ideal UCNP-FRET configuration for optimal analytical
performance a real challenge. To overcome this issue, we have developed
a fully analytical model that requires only a few experimental configurations
to determine the ideal UCNP-FRET system within a few minutes. We verified
our model via experiments using nine different Nd-, Yb-, and Er-doped
core–shell–shell UCNP architectures within a prototypical
DNA hybridization assay using Cy3.5 as an acceptor dye. Using the
selected experimental input, the model determined the optimal UCNP
out of all theoretically possible combinatorial configurations. An
extreme economy of time, effort, and material was accompanied by a
significant sensitivity increase, which demonstrated the powerful
feat of combining a few selected experiments with sophisticated but
rapid modeling to accomplish an ideal FRET biosensor.