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

High-Transmission Biomimetics Structural Surfaces Produced via Ultrafast Laser Manufacturing

Inspired by periodically aligned micro/nanostructures on biological surfaces, researchers have been fabricating biomimetic structures with superior performance. As a promising and versatile tool, an ultrafast laser combined with other forms of processing technology has been utilized to manufacture functional structures, e.g., the biomimetic subwavelength structures to restrain the surface Fresnel reflectance. In this review paper, we interpret the biomimetic mechanism of antireflective subwavelength structures (ARSSs) for high-transmission windows. Recent advances in the fabrication of ARSSs with an ultrafast laser are summarized and introduced. The limitations and challenges of laser processing technology are discussed, and the future prospects for advancement are outlined, too.

Increasing the Stability of Isolated and Dense High-Aspect-Ratio Nanopillars Fabricated Using UV-Nanoimprint Lithography

Structural anti-reflective coating and bactericidal surfaces, as well as many other effects, rely on high-aspect-ratio (HAR) micro- and nanostructures, and thus, are of great interest for a wide range of applications. To date, there is no widespread fabrication of dense or isolated HAR nanopillars based on UV nanoimprint lithography (UV-NIL). In addition, little research on fabricating isolated HAR nanopillars via UV-NIL exists. In this work, we investigated the mastering and replication of HAR nanopillars with the smallest possible diameters for dense and isolated arrangements. For this purpose, a UV-based nanoimprint lithography process was developed. Stability investigations with capillary forces were performed and compared with simulations. Finally, strategies were developed in order to increase the stability of imprinted nanopillars or to convert them into nanoelectrodes. We present UV-NIL replication of pillars with aspect ratios reaching up to 15 with tip diameters down to 35 nm for the first time. We show that the stability could be increased by a factor of 58 when coating them with a 20 nm gold layer and by a factor of 164 when adding an additional 20 nm thick layer of SiN. The coating of the imprints significantly improved the stability of the nanopillars, thus making them interesting for a wide range of applications.

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.