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It has been claimed that graphite hosts superconductivity at room temperature, although all efforts to isolate it have been vain. Here we report a separation method that uses magnetic field gradients to sort the superconducting from normal grains out of industrial graphite powders. We have obtained a concentrate of above room temperature superconducting particles. Electrical resistance measurements on agglomerates of sorted grains of three types of graphite show transition temperatures up to T$_{c{_{onset}}} \sim$ 700K with zero resistance up to $\sim$ 500K. Magnetization measurements confirm these values through jumps at \textit{T$_c$} in the zero field cooled curves, and by the occurrence diamagnetic hysteretic cycles shrinking with temperature. Our results open the door towards the study of above room temperature superconducting ill-stacked graphite phases., Comment: 21 pages, 9 figures

A standard method to reduce the linewidth of semiconductor lasers involves the use of external optical feedback (EOF). However, feedback powers less than 1 % usually trigger coherence collapse (CC), leading to chaotic laser dynamics and linewidth broadening. This paper explores a method to mitigate CC through precise tuning of the feedback polarization depending on the feedback power. We report a semiconductor laser with a sub-kHz linewidth, achieved via EOF. The laser features a U-shaped cavity with two sampled grating distributed Bragg reflectors (SG-DBRs), enabling broad tunability across a 42 nm wavelength range (1513-1555 nm). By injecting optical feedback into both sides of the laser cavity via an external fiber-based cavity, we reduce the linewidth by more than three orders of magnitude, from MHz to sub-kHz across the laser's tuning range. Our approach achieves significant linewidth reduction while maintaining coherence at high feedback levels, marking an improvement over prior studies where CC limited performance. These results pave the way for ultra-narrow linewidth diode lasers with wide tunability, which would benefit fields like coherent optical communications and spectroscopy.

The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a challenge. In this context, Riemannian geometry offers promising solutions by identifying robot motions with geodesics on the so-called configuration space manifold. While this manifold naturally considers the intrinsic robot dynamics, constraints such as joint limits, self-collisions, and collisions with the environment remain overlooked. In this paper, we propose a modification of the Riemannian metric of the configuration space manifold allowing for the generation of robot motions as geodesics that efficiently avoid given regions. We introduce a class of Riemannian metrics based on barrier functions that guarantee strict region avoidance by systematically generating accelerations away from no-go regions in joint and task space. We evaluate the proposed Riemannian metric to generate energy-efficient, dynamic-aware, and collision-free motions of a humanoid robot as geodesics and sequences thereof., Comment: Accepted for publication in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) 2023. 8 pages, 7 figures, accompanying video at https://youtu.be/qT43XgYOlU0

Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that human motions are planned as sequences of geodesic synergies, and thus correspond to coordinated joint movements achieved with piecewise minimum energy. The underlying computational model is built on Riemannian geometry to account for the inertial characteristics of the body. Through the analysis of various human arm motions, we find that our model segments motions into geodesic synergies, and successfully predicts observed arm postures, hand trajectories, as well as their respective velocity profiles. Moreover, we show that our analysis can further be exploited to transfer arm motions to robots by reproducing individual human synergies as geodesic paths in the robot configuration space., Comment: Accepted for publication in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022

The interest in finding experimental realization of Quantum Liquid Spin phases leads us to elaborate polycrystalline samples of the 6H-B phase of Ba3NiSb2O9 . This structure shows Ni triangular planes and the first magnetic characterizations confirm the absence of any magnetic order as expected for QLS. The samples were synthesized by treatment under HP-HT of the 6H-A phase of Ba3NiSb2O9. The optimized synthesis conditions were 2.5 GPa and 620{\deg}C. By combining several technics as XRD, NPD and TEM we show that even the obtained compound is in fact a new form of Ba3NiSb2O9, that corresponds to a new stacking along c. This preserves the presence of Ni planes but results in a loss of the hexagonal symmetry. The TEM is helpful to conclude to a trigonal symmetry at a scale of 10nm., Comment: 5 figures submitted to Journal of Solid State Chemistry on 9/12/2015_ revised version on 30/01/2016

The dynamics of quasicrystal growth remains an unsolved problem in condensed matter. By means of synchrotron live imaging, facetted growth proceeding by the tangential motion of ledges at the solid-melt interface is clearly evidenced all along the solidification of icosahedral AlPdMn quasicrystals. The effect of interface kinetics is significant so that nucleation and free growth of new facetted grains occur in the melt when the solidification rate is increased. The evolution of these grains is explained in details, which reveals the crucial role of aluminum rejection, both in the poisoning of grain growth and driving fluid flow.