Optimizing Bio-Imaging with Computationally Designed Polymer Nanoparticles

Conjugated polymer nanoparticles (CPNs), especially poly(p-phenylene ethyny- lene) nanoparticles (PPE-NPs), are promising candidates for bio-imaging due to their high photostability, adjustable optical characteristics, and biocompatibility. Despite their potential, the fluorescence mechanisms of these nanoparticles are not yet fully understood. In this work, we modeled a spherical PPE-NP in a water environment us- ing 30 PPE dimer chains. Combining molecular dynamics (MD) simulations and time- dependent density functional theory (TD-DFT) calculations, we examined the struc- tural and optical properties of PPE-NPs in water. The MD simulations showed that PPE-NPs remain stable via hydrophobic interactions, with octyloxy side chains shield- ing the core from water. After evaluating six hybrid functionals, we found that the M05 functional provided the most accurate prediction of absorption wavelengths (450.94 nm vs. the experimental value of 450.00 nm). TD-DFT analysis of selected PPE dimer chains revealed strong fluorescence, characterized by high oscillator strengths (2.689– 4.004) and large Stokes shifts (134.51–156.31 nm), which minimize spectral overlap and improve imaging resolution. HOMO–LUMO orbital analysis confirmed that π →π∗ transitions dominate (> 90%), indicating efficient electronic behavior. These results reinforce the potential of PPE-NPs as effective fluorescent probes for bio-imaging, sup- ported by a reliable computational approach for designing future CPNs. By comparing computational predictions with experimental data, this study contributes to the devel- opment of customized nanomaterials for biomedical applications.