Attention : désormais les séminaires ont lieu tous les lundis à 10h45 en salle 523 du LPTMC - Tour 12-13
Mathieu Salanne (Phénix Jussieu)
Modeling supercapacitors at the molecular scale
The electric double layer is generally viewed as simply the boundary that interpolates between an electrolyte solution and a metal surface. Contrary to that view, recent studies have shown that the interface between ionic liquids and metallic electrodes can exhibit structures and fluctuations that are not simple reflections of surrounding bulk materials . The charge of the electrode is screened by the interfacial fluid and induces subtle changes in its structure, which cannot be captured by the conventional Gouy-Chapman theory.
In recent years, this topic has been more intensively addressed in order to develop more efficient supercapacitors . The latter are electrochemical devices that store the charge at the electrode/electrolyte interface through reversible ion adsorption. In order to understand the molecular mechanisms at play, we have performed molecular dynamics simulations on a variety of systems made of ionic liquids and electrodes of different geometries ranging from planar to nanoporous. A key aspect of our simulations is to use a realistic model for the electrodes, by allowing the local charges on the atoms to vary dynamically in response to the electrical potential caused by the ions and molecules in the electrolyte .
These simulations have allowed us to gain strong insight on the structure and dynamics of ionic liquids at electrified interfaces. From the comparison between graphite and nanoporous carbide-derived carbon (CDC) electrodes, we have elucidated the microscopic mechanism at the origin of the increase of the capacitance enhancement in nanoporous carbons . We have also studied the impact of the carbon texture, by comparing CDC with perforated graphene materials .
1. Fedorov, M.V., Kornyshev, A.A., Chem. Rev., 114 (2014), 2978-3036
2. Salanne, M., Rotenberg, B., Naoi, K., Kaneko, K., Taberna, P.L., Grey, C.P., Dunn, B., Simon, P., Nature Energy, 1 (2016), 16070
3. Merlet, C., Pean, C., Rotenberg, B., Madden, P.A., Simon, P., Salanne, M., J. Phys. Chem. Lett., 4 (2013), 264-268
4. Merlet, C., Rotenberg, B., Madden, P.A., Taberna, P.L., Simon, P., Gogotsi, Y., Salanne, M., Nature Materials, 11 (2012), 306-310
5. Mendez-Morales, T., Burbano, M., Haefele, M., Rotenberg, B., Salanne M., J. Chem. Phys., 148 (2018), 193812
Mark Goerbig (LPS Orsay) et Bernard Plaçais (LPA ENS)
Surface states in topological materials beyond the chiral ones: from theory to experiment
We report on the anomalous screening by Dirac states in topological HgTe/CdHgTe heterojunctions in large transverse electric fields. It is mesured in high frequency electronic compressibility experiments. Screening extends over a large chemical potentialrange of 300 meV widely exceeding the 30 meV bulk band gap of HgTe. Dirac screening breakdown is accompanied by an abrupt drop of the Dirac fermion mobility which we attribute to the existence of a series of massive interface states first introduced by Volkov and Pankratov (VP) . Field-effect compressibility is a convenient scattering spectroscopy tool to investigate VP states. Their spectrum obbeys a Landau level energy series with a pseudo magnetic field determined by the Dirac fermon velocity and electric field .
 A. Inhofer et al., Phys. Rev. B 96, 195104 (2017).
 B.A. Volkov, O.A. Pankratov, JETP Lett. 42, 178 (1985).
 S. Tchoumakov et al., Phys. Rev. B 96, 201302-R (2017).
Martin Weigt (LCQB Jussieu)
Statistical-physics inspired modeling of protein sequences: Inferring structure, function, and mutational landscapes
Over the last years, biological research has been revolutionized by experimental high-throughput techniques. Unprecedented amounts of data are accumulating, causing an urgent need to develop data-driven modeling approaches to unveil information hidden in raw data, thereby helping to increase our understanding of complex biological systems. Inference approaches based on statistical physics have played an important role across diverse systems ranging from proteins over neural networks to the collective behaviour of animal groups. To give a specific example, proteins show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino-acid sequences. Thanks to modern sequencing techniques, this amino-acid variability is easily observable, contrary to time- and labour-intensive experiments determining, e.g., the three-dimensional fold of a protein or its biological functionality. I will present recent developments around the so-called Direct-Coupling Analysis, a statistical-mechanics inspired inference approach, which links sequence variability to protein structure and function. I will show that this methodology can be used to (i) to infer contacts between residues and thus to guide 3D-structure prediction of proteins and their complexes, (ii) to infer conserved protein-protein interaction networks, and (iii) to reconstruct mutational landscapes and thus to predict the effect of mutations. Beyond a direct biological and medical interest of such findings, they provide us also insight into underlying principles connecting protein evolution, structure and function.
I will first start with a general introduction on theoretical ecology, stressing the
reasons that make connections with statistical physics interesting and timely.
I will then focus on Lotka-Volterra equations, which provide a general model to study large assemblies of strongly
interacting degrees of freedom in many different fields: biology, economy and in particular ecology.
I will present our analysis of Lotka-Volterra equations as model of ecosystems formed by a
large number of species and show the different phases that emerge. Two of them are particularly
interesting: when interactions are symmetric we find a regime characterised by an exponential
number of multiple equilibria, all poised at the edge of stability for a large number of species.
For non symmetric interactions, this phase is replaced by a chaotic one.
I will then conclude discussing relationships with experiments and general consequences of our works.
Tin Sulejmanpasic (LPT-ENS)
Fractionalization between the vacua: from QCD to quantum magnetism
Quantum Chromodynamics (QCD) -- the theory of strong nuclear forces -- has baffled the physics community and remains one of the poorly understood parts of the standard model. Its quintessential property: the confinement of quarks into protons, neutrons and mesons, while verified both experimentally and numerically, remains an elusive theoretical problem. The various cousins of QCD are however possible to understand to varying degrees and precision. In some of these theories the vacuum state is degenerate, and hence allows for domain walls -- a surface excitation which interpolates between two vacua of the theory. These domain walls have a remarkable property that quarks become liberated on them, and the domain wall excitation spectrum is very different from that of the bulk. Such QCD cousins are, unfortunately, not the physical theory, and they do not occur in nature. QCD however has another unlikely cousin: the Valence Bond Solid (VBS) state of the quantum anti-ferromagnet, where spin 1/2 excitations (or spinons) are bound into spin 1 excitations by a mechanism very similar to confinement of quarks. Perhaps surprisingly the low energy theory describing the behavior of the VBS phase is virtually identical to its QCD cousins under certain conditions. Further the VBS phase may have multiple vacua, and thus support domain walls, which in turn support liberated spinon excitations absent in the bulk. This has been verified numerically in the so-called J-Q model. These domain wall modes can in fact be seen as edge modes akin to those of the symmetry protected topological state. A multidisciplinary effort is slowly emerging to understand such phenomena, from the theoretical aspects of fundamental and condensed matter physics, to the numerical efforts in trying to understand QCD and quantum magnets.