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.
