A laser-irradiated surface is the paradigm of a self-organizing system, as coherent, aligned, chaotic, and complex patterns emerge at the microscale and even the nanoscale. A spectacular manifestation of dissipative structures consists of different types of randomly and periodically distributed nanostructures that arise from a homogeneous metal surface. The noninstantaneous response of the material reorganizes local surface topography down to tens of nanometers scale modifying long-range surface morphology on the impact scale. Under ultrafast laser irradiation with a regulated energy dose, the formation of nanopeaks, nanobumps, nanohumps and nanocavities patterns with 20–80 nm transverse size unit and up to 100 nm height are reported. We show that the use of crossed-polarized double laser pulse adds an extra dimension to the nanostructuring process as laser energy dose and multi-pulse feedback tune the energy gradient distribution, crossing critical values for surface self-organization regimes. The tiny dimensions of complex patterns are defined by the competition between the evolution of transient liquid structures generated in a cavitation process and the rapid resolidification of the surface region. Strongly influencing the light coupling, we reveal that initial surface roughness and type of roughness both play a crucial role in controlling the transient emergence of nanostructures during laser irradiation.

The capacity to synthesize and design highly intricated nanoscale objects of different sizes, surfaces, and shapes dramatically conditions the development of multifunctional nanomaterials. Ultrafast laser technology holds great promise as a contactless process able to rationally and rapidly manufacture complex nanostructures bringing innovative surface functions. The most critical challenge in controlling the growth of laser‐induced structures below the light diffraction limit is the absence of external order associated to the inherent local interaction due to the self‐organizing nature of the phenomenon. Here high aspect‐ratio nanopatterns driven by near‐field surface coupling and architectured by timely‐controlled polarization pulse shaping are reported. Electromagnetic coupled with hydrodynamic simulations reveal why this unique optical manipulation allows peaks generation by inhomogeneous local absorption sustained by nanoscale convection. The obtained high aspect‐ratio surface nanotopography is expected to prevent bacterial proliferation, and have great potential for catalysis, vacuum to deep UV photonics and sensing.

A self-organization hydrodynamic process has recently been proposed to partially explain the formation of femtosecond laser-induced nanopatterns on Nickel, which have important applications in optics, microbiology, medicine, etc. Exploring laser pattern space is difficult, however, which simultaneously (i) motivates using machine learning (ML) to search for novel patterns and (ii) hinders it, because of the few data available from costly and time-consuming experiments. In this paper, we use ML to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg (SH) partial differential equation (PDE). To do so, we propose a framework to learn with few data, in the absence of initial conditions, by benefiting from background knowledge in the form of a PDE solver. We show that in the case of a self-organization process, a feature mapping exists in which initial conditions can safely be ignored and patterns can be described in terms of PDE parameters alone, which drastically simplifies the problem. In order to apply this framework, we develop a second-order pseudospectral solver of the SH equation which offers a good compromise between accuracy and speed. Our method allows us to predict new nanopatterns in good agreement with experimental data. Moreover, we show that pattern features are related, which imposes constraints on novel pattern design, and suggest an efficient procedure of acquiring experimental data iteratively to improve the generalization of the learned model. It also allows us to identify the limitations of the SH equation as a partial model and suggests an improvement to the physical model itself.

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Ultrafast laser was recently used to modify the surface integrity and peen the surface region of aluminum based alloy 2024-T351 without a sacrificial layer prior to the process. We show that controllable laser parameters such as fluence and pulse duration have a significant influence on peening qualities, such as the compressive residual stress, hardness, and surface roughness of peened parts. The residual stress profile was analyzed by x-ray diffraction. By controlling the laser fluence and pulse duration, it was possible to obtain 200 MPa of compressive residual stresses close to the surface and 100 MPa of compressive residual stresses at 50 μm depth. Moreover, micro-hardness was increased from 2.1 to 2.5 GPa in the near-surface region. In addition, the dislocation densities were evaluated from high-resolution x-ray diffraction peaks. The increase of the dislocation density indicates that plastic deformation occurred, which generated compressive residual stresses and hardness enhancement. Plastic deformation is considered to be created by an ultrafast laser-induced pressure wave. The correlation between laser parameters and modified surface properties is interpreted by the complex interplay between laser excitation, material relaxation, and pressure waves. A pulse duration in the picosecond range and a relatively low fluence is possibly the optimal condition for a best peening quality with small surface roughness, which could potentially be used to reduce surface cracking and associated failures of additively manufactured parts.

Driven by local field enhancements enhancing feedback, novel structuring features demonstrate the potential of ultrafast laser for the fabrication of self-organized patterns with a periodic topography well below the diffraction limit. We report the achievement of laser-induced nanocavities that results from the control of a Marangoni convection instability at the nanoscale.