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MORPHOSURF: Anthony Nakhoul defended his thesis on November 29th
On The November 29, 2022
Amphitheatre J022 - Telecom Saint-Etienne Building
Anthony Nakhoul, PhD student at the Hubert Curien Lab, defended his thesis and became a Doctor!
Our one and only Anthony Nakhoul, PhD student at Laboratoire Hubert Curien, denfended his thesis at 2pm on November 29th. His work on "Surface Morphology at Nanometric Scale by Temporal and Polarization Control of Ultrashort Laser Pulses" (MORPHOSURF project) was reviewed by an international jury :
- Emmanuel STRATAKIS, Rapporteur, FORTH-IESL, Greece
- Jörn BONSE, Rapporteur, Bundesanstalt für Materialforschung, Germany
- Olivier UTEZA, Examinateur, Université Aix-Marseille, France
- Florence GARRELIE, Examinatrice, Université Jean-Monnet, France
- Florent PIGEON, Invité, Université Jean-Monnet, France
- Claire MAURICE, Co-directrice de thèse, École des Mines, France
- Jean-Philippe COLOMBIER, Directeur de thèse, Université Jean-Monnet, France
We congratulate him for his successful defense and for his hard work at the Hubert Curien lab!
We will follow his research on the HyTex project with the Georges Friedel Lab.
More info
ABSTRACT
Ultrafast-laser irradiated surface is a typical paragon of a self-organizing system, which emerge and organize complex micropatterns and even nanopatterns. A spectacular manifestation of dissipative structures consists of different types of randomly and periodically distributed nanostructures that arise from a homogeneous metal surface. The formation of nanopeaks, nanobumps, nanohumps and nanocavities patterns with 20–80 nm transverse size unit and up to 100 nm height are reported under femtosecond laser irradiation with a regulated energy dose. We shed the light on the originality of the nanopeaks, having an exceptional aspect ratio on the nanoscale. They are primarily generated on the crests grown between the convective cells formed by the very first pulses. The production of these distinct nanostructures can enable unique surface functionalizations toward the control of mechanical, biomedical, optical, or chemical surface properties on a nanometric scale. 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. Furthermore, the initial surface roughness and type of roughness is another essential feature to be controlled to switch from a regime of self-organization to another one. Moreover, we used physics-guided machine learning, an emerging field of research that combines physical knowledge and machine learning to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg partial equation, in recent research.
We will follow his research on the HyTex project with the Georges Friedel Lab.
More info
ABSTRACT
Ultrafast-laser irradiated surface is a typical paragon of a self-organizing system, which emerge and organize complex micropatterns and even nanopatterns. A spectacular manifestation of dissipative structures consists of different types of randomly and periodically distributed nanostructures that arise from a homogeneous metal surface. The formation of nanopeaks, nanobumps, nanohumps and nanocavities patterns with 20–80 nm transverse size unit and up to 100 nm height are reported under femtosecond laser irradiation with a regulated energy dose. We shed the light on the originality of the nanopeaks, having an exceptional aspect ratio on the nanoscale. They are primarily generated on the crests grown between the convective cells formed by the very first pulses. The production of these distinct nanostructures can enable unique surface functionalizations toward the control of mechanical, biomedical, optical, or chemical surface properties on a nanometric scale. 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. Furthermore, the initial surface roughness and type of roughness is another essential feature to be controlled to switch from a regime of self-organization to another one. Moreover, we used physics-guided machine learning, an emerging field of research that combines physical knowledge and machine learning to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg partial equation, in recent research.