arXiv subjects

Carlos Sánchez-Sánchez

Publications and source records attributed to Carlos Sánchez-Sánchez.

5 recordsLinked to original sources

arXiv2026

Emergent Quantum Geometric Phases in Holey Graphene

In graphene and other two-dimensional materials, periodic modulations of the electron density can significantly alter the energy spectrum and transport properties. Here, we report magnetotransport measurements in encapsulated monolayer graphene with ultra-high-quality patterned periodic antidot lattices that preserve the intrinsic electronic properties of the material. This lithographically defined platform enables controlled access to commensurability and superlattice phenomena at length scales otherwise difficult to achieve. By systematically tuning the lattice dimensions, we reveal a hierarchy of classical commensurability features arising from cyclotron orbits with comparable radii that follow multiple classical trajectories, resulting in broadened resistance peaks beyond the conventional single-orbit picture. Superimposed on these features, we observe pronounced Brown-Zak oscillations arising from the quantum commensurability between the magnetic flux quantum and the unit cell of the engineered Bravais lattices. We demonstrate that the intrinsic geometric phase of our system is directly measurable and show a precise matching of the magnetic field periodicity to the lithographic periodic patterning, where moiré-like electronic spectra can be geometrically generated in single-layer graphene without the need for twist, lattice mismatch, or multilayer stacking. Our results establish nanopatterned graphene as a clean, tunable, and scalable platform for realizing and exploring moiré physics through on-demand real-space design

cond-mat.mes-hall↗

arXiv2025

Superballistic conduction in hydrodynamic antidot graphene superlattices

Viscous electron flow exhibits exotic signatures such as superballistic conduction. In order to observe hydrodynamics effects, a 2D device where the current flow is as inhomogeneous as possible is desirable. To this end, we build three antidot graphene superlattices with different hole diameters. We measure their electrical properties at various temperatures and under the effect of a perpendicular magnetic field. We find an enhanced superballistic effect, suggesting the effectiveness of the geometry at bending the electron flow. In addition, superballistic conduction, which is related to a transition from a non-collective to a collective regime of transport, behaves non-monotonically with the magnetic field. We also analyze the device resistance as a function of the size of the antidot superlattice to find characteristic scaling laws describing the different transport regimes. We prove that the antidot superlattice is a convenient geometry for realizing hydrodynamic flow and provide valuable explanations for the technologically relevant effects of superballistic conduction and scaling laws.

cond-mat.mes-hall↗

arXiv2019

On-Surface Hydrogen-Induced Covalent Coupling of Polycyclic Aromatic Hydrocarbons via a Superhydrogenated Intermediate

The activation and subsequent covalent coupling of polycyclic aromatic hydrocarbons (PAHs) are of great interest in fields like chemistry, energy, biology, or health, among others. However, this is not a trivial process. So far, it is based on the use of catalysts that drive and increase the efficiency of the reaction. Here, we report on an unprecedented method in which the dehydrogenation and covalent coupling is thermally activated in the presence of atomic hydrogen and a surface. This mechanism, which requires of the superhydrogenation of the PAHs, has been characterized by high-resolution scanning tunnelling microscopy (STM) and rationalized by density functional theory (DFT) calculations. This work opens a door toward the formation of covalent, PAH-based, macromolecular nanostructures on low-reactive surfaces, thus facilitating its applicability.

cond-mat.mtrl-sci↗

arXiv2016

Real-time optimal control via Deep Neural Networks: study on landing problems

Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the on-board decision making system. In this paper this claim is investigated in more detail training deep artificial neural networks to represent the optimal control action during a pinpoint landing, assuming perfect state information. It is found to be possible to train deep networks for this purpose and that the resulting landings, driven by the trained networks, are close to simulated optimal ones. These results allow for the design of an on-board real time optimal control system able to cope with large sets of possible initial states while still producing an optimal response.

eess.SY↗

arXiv2016

Optimal Orderings of k-subsets for Star Identification

Finding the optimal ordering of k-subsets with respect to an objective function is known to be an extremely challenging problem. In this paper we introduce a new objective for this task, rooted in the problem of star identification on spacecrafts: subsets of detected spikes are to be generated in an ordering that minimizes time to detection of a valid star constellation. We carry out an extensive analysis of the combinatorial optimization problem, and propose multiple algorithmic solutions, offering different quality-complexity trade-offs. Three main approaches are investigated: exhaustive search (branch and prune), goal-driven (greedy scene elimination, minimally intersecting subsets), and stateless algorithms which implicitly seek to satisfy the problem's goals (pattern shifting, base unrank). In practical terms, these last algorithms are found to provide satisfactory approximations to the ideal performance levels, at small computational costs.

math.OC↗