Search arXivSearch

arXiv · 2506.04835

Thermoplasmonics of Gold-Core Silica-Shell Colloidal Nanoparticles under Pulse Illumination

Abstract

Core-shell nanoparticles, particularly those having a gold core, have emerged as a highly promising class of materials due to their unique optical and thermal properties, which underpin a wide range of applications in photothermal therapy, imaging, and biosensing. In this study, we present a comprehensive study of the thermal dynamics of gold-core silica-shell nanoparticles immersed in water under pulse illumination. The plasmonic response of the core-shell nanoparticle is described by incorporating Mie theory with electronic temperature corrections to the refractive indices of gold, based on a Drude Lorentz formulation. The thermal response of the core-shell nanoparticles is modeled by coupling the two temperature model with molecular dynamics simulations, providing an atomistic description of nanoscale heat transfer. We investigate nanoparticles with both dense and porous silica shells (with 50% porosity) under laser pulse durations of 100 fs, 10 ps, and 1 ns, and over a range of fluences between 0.05 and 5mJ/cm2. We show that nanoparticles with a thin dense silica shell (5 nm) exhibit significantly faster water heating compared to bare gold nanoparticles. This behavior is attributed to enhanced electron-phonon coupling at the gold silica interface and to the relatively high thermal conductance between silica and water. These findings provide new insights into optimizing nanoparticle design for efficient photothermal applications and establish a robust framework for understanding energy transfer mechanisms in heterogeneous metal dielectric nanostructures.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Julien El Hajj, Gilles Ledoux, Samy Merabia. 2025-06-05. Thermoplasmonics of Gold-Core Silica-Shell Colloidal Nanoparticles under Pulse Illumination. https://arxiv.org/abs/2506.04835

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bi-Hamiltonian in Semiflexible Polymers built upon Overdamping Process

Quantifying the interaction between a system of interest and its ambient conditions, the memory effect links the states of two distinct Hamiltonians: one for the target system and one for the environment. In this paper, we propose the diffusion process derived from the Smoluchowski equation that can derive the evolution process described by the memory effect integration in a non Markovian regime. The Smoluchowski picture, within the framework of stochastic thermodynamics, justifies a diffusion process incorporated into the equations of motion, and the result of the derivation enables a coarse-grained molecular dynamics simulation with the modified equation of motion to reproduce attenuation from collisions between single walled carbon nanotubes (SWCNTs) under far from equilibrium conditions. The results of the numerical experiments on the collision confirm that heat diffusion compensates for the correlated momentum arising from the memory effect between the two Hamiltonians in both equilibrium and far from equilibrium states.

physics.comp-ph

Translation of transient acoustic fields

A method is presented for the translation of acoustic field data from a source to a target region. Field data are represented as spherical harmonic expansions on spheres surrounding the source and target regions respectively and expansions are translated using a ``point and shoot'' method using the Kirchhoff--Helmholtz integral to carry out an axial translation from one sphere to the other. The principal motivation for the method is its use in a time-domain Fast Multipole Method, and test cases reflective of this application are presented. The method converges to six digits for appropriate values of parameters and for the values of $N$ considered here computational effort scales approximately as $N^{2}$ where $N$ is the order of spherical harmonic expansion for the field data. The method is causal and thus avoids artifacts generated in methods which are not based on intrinsically causal formulations.

physics.comp-ph

Learning continuous reaction paths for transition-state prediction

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

physics.comp-ph