HAL Portal IOGS (nstitut d'Optique Graduate School)
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Insight on oxidation process and wettability of femtosecond laser ablated Ti surfaces
No full textfemtosecond laser surface treatment offers unparalleled advantages in terms of precision, minimal thermal damage, versatility, reduced post-processing requirements, rapid processing speed, and environmental friendliness. These advantages make femtosecond lasers an attractive choice for surface engineering applications across various industries, driving innovation and enabling the development of advanced functional materials with tailored properties. The application of MD simulations offers a comprehensive understanding of the complex phenomena underlying the wettability modulation of femtosecond laser-treated Ti surfaces. By bridging the gap between theory and experiment, this study contributes to advancing our knowledge of surface engineering techniques and paves the way for the rational design of functional materials with tailored surface properties
Tomography of a Spatially Resolved Single-Atom Detector in the Presence of Shot-to-Shot Number Fluctuations
No full textInternational audienceThe tomography of single-particle-resolved detectors is of primary importance for characterizing particle correlations, with applications in quantum metrology, quantum simulation, and quantum computing. However, it is a nontrivial task in practice due to the uncertainties on the statistics of the state impinging on the detector and to the unavoidable presence of noise that affects the measurement but does not originate from the detector. In this work, we address this problem for a three-dimensional single-atom-resolved detector where shot-to-shot atom-number fluctuations are a central issue in performing a quantum detector tomography. We overcome this difficulty by exploiting the parallel measurement of the counting statistics in subvolumes of the detector, from which we evaluate the effect of shot-to-shot fluctuations and perform a local tomography of the detector. In addition, we illustrate the validity of our method from applying it to Gaussian quantum states with different number statistics. Finally, we show that the response of microchannel plate detectors is well described by using a binomial distribution with the detection efficiency as a single parameter. Published by the American Physical Society 202
Multiple scattering theory in one dimensional space and time dependent disorder: Average field
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Fast analysis of multiple exposure speckle data to provide relative blood flow maps using Convolutional Neural Networks
No full textInternational audienceLaser Speckle Contrast Imaging is a well-established technique able to produce relative blood flow maps contactless and without using dyes. It relies on the statistical analysis of dynamic speckle images, observed when a coherent light is used to illuminate a medium that contains moving scatterers. The local speckle contrast is related to the movements of the scatterers. Multiple exposure speckle imaging (MESI) is a variant of the technique that takes advantage of multiple exposure data to retrieve more quantitative flow maps by accounting for the unwanted and superimposed contribution of static scatterers. Yet, in MESI, a model is adjusted pixelwise to the experimental data requiring long computation times and an a priori guess on the flow regimes. These issues hindered so far, the translation of MESI to clinical applications though some studies have already demonstrated its potential. Here we propose an alternative method based on Convolutional Neural Networks to analyze MESI data. The proposed CNN architecture has been trained and validated using experimental data acquired on calibrated microfluidics flow phantoms. Then, the trained network was applied to analyze MESI data acquired in vivo in mice brain. In addition to be model-bias-free, we have found that the CNN approach infers flow maps much faster than the classical pixelwise regression approach. This new approach is promising for the clinical translation of MESI
Metallic glasses for biological applications and opportunities opened by laser surface texturing: A review
No full textInternational audienceImplants and surgical tools are commonly used in the medical field. However, issues including poor osseointegration, rejection, or bacterial contamination may still occasionally occur, causing serious complications susceptible to lead even to the patient death. It is therefore necessary to move toward new alternative advanced surfaces, that may possess both antibacterial properties and improved biocompatibility compared with existing solutions. Metallic glasses may constitute this kind of promising materials, gathering a high physico-chemical resistance combined with outstanding mechanical properties. The first part of this review explores the interest of metallic glasses for biomedical applications, and focuses on their biological properties. Metallic glasses are considered under their two forms: bulk, as well as thin films. The behaviour of these metallic glasses towards micro-organisms (bacteria, cells in particular) is then described. Besides, surface texturing by pulsed laser represents a further degree of freedom to deeply functionalize the metallic glasses’ surface. The induced modifications may not only concern the morphology of the surface, but also its chemistry at a small scale. In this sense, the review demonstrates the importance of such a surface modification on the biological properties, and on the dynamic of cells on these advanced surfaces in particular. Finally, the last part is dedicated to the latest developments of ultrashort laser irradiation of metallic glasses. It is shown how these nano-engineered surfaces can influence the biological behaviour of metallic glasses. Explanations rely on the patterning design on the one hand, on chemistry of the irradiated material on the other hand
Near-Field Heat Transfer Close to the Contact and in Many-Body Systems
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Control of the local photonic density of states above magneto-optical metamaterials
No full textInternational audienceThe local density of states (LDOS) of electromagnetic field drives many basic processes associated with light-matter interaction such as the thermal emission of objects, the spontaneous emission of quantum systems, or the fluctuation-induced electromagnetic forces on molecules. Here, we study the LDOS in the close vicinity of magneto-optical metamaterials under the influence of an external magnetic field and demonstrate that it can be efficiently altered in a narrow or a broad spectral range simply by changing the spatial orientation or the magnitude of this field. This result paves the way for an active control of the photonic density of states at deep subwavelength scale
Challenges in focal plane and telescope calibration for high-precision space astrometry
No full textInternational audienceWith sub-microarcsecond angular accuracy, the Theia telescope will be capable of revealing the architectures of nearby exoplanetary systems down to the mass of Earth. This research addresses the challenges inherent in space astrometry missions, focusing on focal plane calibration and telescope optical distortion. We propose to assess the future feasibility of large-format detectors (50 to 200 megapixels) in a controlled laboratory environment. The aim is to improve the architecture of the focal plane while ensuring that specifications are met. The use of field stars as metrological sources for calibrating the optical distortion of the field may help to constrain telescope stability. The paper concludes with an attempt to confirm in the laboratory the performance predicted by simulations. We will also address the possibility of using such techniques with a dedicated instrument for the Habitable World Observatory
SELF-SUPERVISED LEARNING OF MULTI-MODAL COOPERATION FOR SAR DESPECKLING
No full textInternational audienceSynthetic aperture radar (SAR) is a widely used modality for Earth observation, as they provide weather-independent imaging capabilities. However, interpretation of SAR images is difficult due to the speckle phenomenon: fluctuations appear in the image, which are stronger in areas with high radar reflectivity. As a result, many speckle reduction methods have been developed, with deep learning approaches standing out as particularly effective. Our article presents here a deep learning approach with two novel features: the use of an optical image to improve the restoration of a SAR image, while using a self-supervised neural network trainin
