A boundary element framework for fractional dual-phase-lag modeling of nanoparticle-assisted photothermal heat transfer in skin tissue

• A fractional dual-phase-lag (FDPL) bioheat model is developed to capture finite-speed and memory-dependent thermal transport in biological tissue. • An efficient FDPL–BEM framework is proposed, transforming the transient fractional problem into a sequence of modified Helmholtz equations without volumetric meshing. • Nanoparticle-assisted photothermal heating is shown to exhibit strong non-Fourier behavior with wave-like thermal fronts and pronounced surface confinement. • Fractional order, nanoparticle concentration, laser power, and perfusion rate are identified as key regulators of thermal localization and safety margins. • Compared with Fourier models, the FDPL formulation predicts markedly reduced deep-tissue heating and more realistic photothermal treatment outcomes. Accurate prediction of heat transfer in biological tissue subjected to rapid and localized laser irradiation is essential for the engineering design of safe and effective photothermal therapies. Classical Fourier-based bioheat formulations become insufficient in such regimes, as they fail to account for finite-speed heat propagation and the inherent thermal memory arising from the heterogeneous and multiscale nature of living tissue. This study develops a non-Fourier computational framework for nanoparticle-assisted photothermal heating in layered skin based on a fractional dual-phase-lag (FDPL) bioheat model. The formulation incorporates temporal memory, distinct phase lags for heat flux and temperature gradient, temperature-dependent blood perfusion, and volumetric heat generation induced by plasmonic nanoparticles under Gaussian laser irradiation. An operator-splitting strategy transforms the transient FDPL system into a sequence of modified Helmholtz problems, which are solved efficiently using a boundary element method augmented by dual reciprocity, eliminating volumetric meshing while preserving high spatial resolution. The proposed framework enables accurate prediction of spatiotemporal temperature fields at significantly reduced computational cost. Numerical results reveal pronounced surface thermal confinement and finite-speed thermal wave behavior, in sharp contrast to classical diffusion predictions. Parametric studies demonstrate strong sensitivity of peak temperature and penetration depth to the fractional order, nanoparticle concentration, laser power, and perfusion rate. Compared with Fourier-based models, the FDPL approach predicts markedly reduced deep-tissue heating and more realistic transient responses. The proposed FDPL–BEM framework constitutes a physically consistent and computationally efficient platform for non-Fourier bioheat modeling, enabling the systematic analysis and optimization of minimally invasive nanoparticle-assisted photothermal therapeutic strategies.