Self-Organization Regimes Induced by Ultrafast Laser on Surfaces in the Tens of Nanometer Scales

Molecular Dynamics Modeling of Pulsed Laser Fragmentation of Solid and Porous Si Nanoparticles in Liquid Media

The production of non-toxic and homogeneous colloidal solutions of nanoparticles (NPs) for biomedical applications is of extreme importance nowadays. Among the various methods for generation of NPs, pulsed laser ablation in liquids (PLAL) has proven itself as a powerful and efficient tool in biomedical fields, allowing chemically pure silicon nanoparticles to be obtained. For example, laser-synthesized silicon nanoparticles (Si NPs) are widely used as contrast agents for bio visualization, as effective sensitizers of radiofrequency hyperthermia for cancer theranostics, in photodynamic therapy, as carriers of therapeutic radionuclides in nuclear nanomedicine, etc. Due to a number of complex and interrelated processes involved in the laser ablation phenomenon, however, the final characteristics of the resulting particles are difficult to control, and the obtained colloidal solutions frequently have broad and multimodal size distribution. Therefore, the subsequent fragmentation of the obtained NPs in the colloidal solutions due to pulsed laser irradiation can be utilized. The resulting NPs' characteristics, however, depend on the parameters of laser irradiation as well as on the irradiated material and surrounding media properties. Thus, reliable knowledge of the mechanism of NP fragmentation is necessary for generation of a colloidal solution with NPs of predesigned properties. To investigate the mechanism of a laser-assisted NP fragmentation process, in this work, we perform a large-scale molecular dynamics (MD) modeling of FS laser interaction with colloidal solution of Si NPs. The obtained NPs are then characterized by their shape and morphological properties. The corresponding conclusion about the relative input of the properties of different laser-induced processes and materials to the mechanism of NP generation is drawn.

Effect of heterogeneous environmental conditions on labyrinthine vegetation patterns

Self-organization is a ubiquitous phenomenon in Nature due to the permanent balance between injection and dissipation of energy. The wavelength selection process is the main issue of pattern formation. Stripe, hexagon, square, and labyrinthine patterns are observed in homogeneous conditions. In systems with heterogeneous conditions, a single wavelength is not the rule. Large-scale self-organization of vegetation in arid environments can be affected by heterogeneities, such as interannual precipitation fluctuations, fire occurrences, topographic variations, grazing, soil depth distribution, and soil-moisture islands. Here, we investigate theoretically the emergence and persistence of vegetation labyrinthine patterns in ecosystems under deterministic heterogeneous conditions. Based on a simple local vegetation model with a space-varying parameter, we show evidence of perfect and imperfect labyrinthine patterns, as well as disordered vegetation self-organization. The intensity level and the correlation of the heterogeneities control the regularity of the labyrinthine self-organization. The phase diagram and the transitions of the labyrinthine morphologies are described with the aid of their global spatial features. We also investigate the local spatial structure of labyrinths. Our theoretical findings qualitatively agree with satellite images data of arid ecosystems that show labyrinthinelike textures without a single wavelength.

Learning PDE to Model Self-Organization of Matter

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.

Highly Regular Hexagonally-Arranged Nanostructures on Ni-W Alloy Tapes upon Irradiation with Ultrashort UV Laser Pulses

Nickel tungsten alloy tapes (Ni—5 at% W, 10 mm wide, 80 µm thick, biaxially textured) used in second-generation high temperature superconductor (2G-HTS) technology were laser-processed in air with ultraviolet ps-laser pulses (355 nm wavelength, 300 ps pulse duration, 250–800 kHz pulse repetition frequency). By employing optimized surface scan-processing strategies, various laser-generated periodic surface structures were generated on the tapes. Particularly, distinct surface microstructures and nanostructures were formed. These included sub-wavelength-sized highly-regular hexagonally-arranged nano-protrusions, wavelength-sized line-grating-like laser-induced periodic surface structures (LIPSS, ripples), and larger irregular pyramidal microstructures. The induced surface morphology was characterized in depth by electron-based techniques, including scanning electron microscopy (SEM), electron back scatter diffraction (EBSD), cross-sectional transmission electron microscopy (STEM/TEM) and energy dispersive X-ray spectrometry (EDS). The in-depth EBSD crystallographic analyses indicated a significant impact of the material initial grain orientation on the type of surface nanostructure and microstructure formed upon laser irradiation. Special emphasis was laid on high-resolution material analysis of the hexagonally-arranged nano-protrusions. Their formation mechanism is discussed on the basis of the interplay between electromagnetic scattering effects followed by hydrodynamic matter re-organization after the laser exposure. The temperature stability of the hexagonally-arranged nano-protrusion was explored in post-irradiation thermal annealing experiments, in order to qualify their suitability in 2G-HTS fabrication technology with initial steps deposition temperatures in the range of 773–873 K.

Boosted Spontaneous Formation of High-Aspect Ratio Nanopeaks on Ultrafast Laser-Irradiated Ni Surface

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.

High-Density Nanowells Formation in Ultrafast Laser-Irradiated Thin Film Metallic Glass

We present an effective approach for fabricating nanowell arrays in a one-step laser process with promising applications for the storage and detection of chemical or biological elements. Biocompatible thin films of metallic glasses are manufactured with a selected composition of Zr₆₅Cu₃₅, known to exhibit remarkable mechanical properties and glass forming ability. Dense nanowell arrays spontaneously form in the ultrafast laser irradiation spot with dimensions down to 20 nm. The flared shape observed by transmission electron microscopy is ideal to ensure chemical or biological material immobilization into the nanowells. This also indicates that the localization of the cavitation-induced nanopores can be tuned by the density and size of the initial nanometric interstice from the columnar structure of films deposited by magnetron sputtering. In addition to the topographic functionalization, the laser-irradiated amorphous material exhibits structural changes analyzed by spectroscopic techniques at the nanoscale such as energy-dispersive X-ray spectroscopy and electron energy loss spectroscopy. Results reveal structural changes consisting of nanocrystals of monoclinic zirconia that grow within the amorphous matrix. The mechanism is driven by local oxidation process catalyzed by extreme temperature and pressure conditions estimated by an atomistic simulation of the laser-induced nanowell formation.

Ten Open Questions about Laser-Induced Periodic Surface Structures

Laser-induced periodic surface structures (LIPSS) are a simple and robust route for the nanostructuring of solids that can create various surface functionalities featuring applications in optics, medicine, tribology, energy technologies, etc. While the current laser technologies already allow surface processing rates at the level of m2/min, industrial applications of LIPSS are sometimes hampered by the complex interplay between the nanoscale surface topography and the specific surface chemistry, as well as by limitations in controlling the processing of LIPSS and in the long-term stability of the created surface functions. This Perspective article aims to identify some open questions about LIPSS, discusses the pending technological limitations, and sketches the current state of theoretical modelling. Hereby, we intend to stimulate further research and developments in the field of LIPSS for overcoming these limitations and for supporting the transfer of the LIPSS technology into industry.