journal article·2020-12-01Open access

Femtosecond laser shockwave peening ablation in liquids for hierarchical micro/nanostructuring of brittle silicon and its biological application

Dongshi Zhang, Liang-Chun Wu, Masashi Ueki, Yoshihiro Ito, Koji Sugioka

Abstract

Abstract This paper presents a new technique, termed femtosecond laser shock peening ablation in liquids (fs-LSPAL), which can realize simultaneous crack micro/nanomanufacturing and hierarchical micro/nanolaser ablation, giving rise to the formation of diverse multiscale hierarchical structures, such as macroporous ratcheted structures and en échelon microfringes decorated with parabolic nanoripples. Through analysis of surface morphologies, many phenomena have been confirmed to take place during fs-LSPAL, including en échelon cracks, nanostriation, ripple densification, crack branching, and selective formation of high spatial frequency laser-induced periodic surface structures of 100–200 nm in period. At a high laser power of 700 mW, fs-LSPAL at scanning speeds of 0.2 mm s−1 and 1 mm s−1 enables the generation of height-fluctuated and height-homogeneous hierarchical structures, respectively. The height-fluctuated structures can be used to induce ‘colony’ aggregates of embryonic EB3 stem cells. At 200 mW, fs-LSPAL at 1 mm s−1 is capable of producing homogeneous tilt macroporous structures with cracked structures interleaved among them, which are the synergistic effects of bubble-induced light refraction/reflection ablation and cracks. As shown in this paper, the conventional laser ablation technique integrated with its self-driven unconventional cracking under extreme conditions expands the horizons of extreme manufacturing and offers more opportunities for complex surface structuring, which can potentially be used for biological applications.
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Research context

OpenAlex topics: Laser-Ablation Synthesis of Nanoparticles · Nonlinear Optical Materials Studies · Laser Material Processing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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