Optimizing bio-imaging with computationally designed polymer nanoparticles

Conjugated polymer nanoparticles (CPNs), especially poly(<i>p</i>-phenylene ethynylene) 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 using 30 PPE dimer chains. Combining molecular dynamics (MD) simulations and time-dependent density functional theory (TD-DFT) calculations, we examined the structural and optical properties of PPE-NPs in water. The MD simulations showed that PPE-NPs remain stable <i>via</i> hydrophobic interactions, with octyloxy side chains shielding 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 <i>vs.</i> 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. Highest occupied molecular orbitals (HOMO)-lowest unoccupied molecular orbitals (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, supported by a reliable computational approach for designing future CPNs. By comparing computational predictions with experimental data, this study contributes to the development of customized nanomaterials for biomedical applications.