Search arXivSearch

arXiv · 1803.07343

Laser Ablation of Gold into Water: near Critical Point Phenomena and Hydrodynamic Instability

Abstract

Laser ablation of gold irradiated through the transparent water is studied. We follow dynamics of gold expansion into the water along very long (up to 200 ns) time interval. This is significant because namely at these late times pressure at a contact boundary between gold (Au) and water decreases down to the saturation pressure of gold. Thus the saturation pressure begins to influence dynamics near the contact. The inertia of displaced water decelerates the contact. In the reference frame connected with the contact, the deceleration is equivalent to the free fall acceleration in a gravity field. Such conditions are favorable for the development of Rayleigh-Taylor instability (RTI) because heavy fluid (gold) is placed above the light one (water) in a gravity field. We extract the increment of RTI from 2T-HD 1D runs. Surface tension and especially viscosity significantly dump the RTI gain during deceleration. Atomistic simulation with Molecular Dynamics method combined with Monte-Carlo method (MD-MC) for large electron heat conduction in gold is performed to gain a clear insight into the underlying mechanisms. MD-MC runs show that significant amplification of surface perturbations takes place. These perturbations start just from thermal fluctuations and the noise produced by bombardment of the atmosphere by fragments of foam. The perturbations achieve amplification enough to separate the droplets from the RTI jets of gold. Thus the gold droplets fall into the water.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nail Inogamov, Vasily Zhakhovsky, Viktor Khokhlov. 2018-03-20. Laser Ablation of Gold into Water: near Critical Point Phenomena and Hydrodynamic Instability. https://doi.org/10.1063/1.5045043

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