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M. Espinosa

Publications and source records attributed to M. Espinosa.

5 recordsLinked to original sources
arXiv2026

Quantum oscillations and linear magnetoresistance in ultraclean CaVO$_3$ thin films

Advances in epitaxy of transition metal oxides with perovskite structure allow novel insights into transport mechanisms of strongly correlated electron systems, which are of interest for future transparent electronics. In this study, we investigate magnetotransport properties of thin epitaxial CaVO$_3$ films, grown coherently strained on LaAlO$_3$, and demonstrate for the ultraclean limit quantum oscillations. Fermi liquid behavior is detected in the temperature-dependent resistivity $\rho(T) \sim T^2$ up to 20 K, with effective mean free paths exceeding up to 20 times the film thickness (38 nm). Shubnikov-de Haas oscillations and a non-linear Hall resistance reveal two electron-like (1 and 2) and one hole-like (h) carriers, reflecting the three-fold Fermi surface of orthorhombic CaVO$_3$, with effective charge carrier densities and mobilities at 4.2 K of: (1) $n_1 \approx 9.3 \cdot 10^{21}{}cm^{-3}$ with low mobility $\mu_1 \approx 926{}cm^{2}V^{-1}s^{-1}$, (2) $n_2 \approx 7.2 \cdot 10^{19}{}cm^{-3}$ with high mobility $\mu_2 \approx 6600 {}cm^{2}V^{-1}s^{-1}$, and (h) $n_h \approx 2.2 \cdot 10^{18}{}cm^{-3}$ with $\mu_h \approx 1500{}cm^{2}V^{-1}s^{-1}$. A non-saturating linear magnetoresistance dominates at low temperatures, exceeding the value for single crystals by 30$\%$. Our findings on epitaxial films demonstrate the delicate interplay of multiple carriers with correlations stemming from a non-spherical nested Fermi surface of a perovskite structure with orthorhombic distortion.

cond-mat.str-el↗
arXiv2021

Experiments in the penetration of cuboid intruders near walls into granular matter

When an object penetrates into granular matter near a boundary, it experiences a horizontal repulsion due to the intruder-grain-wall interaction. Here we show experimentally that a square cuboid intruder, released from rest with no initial velocity, neara vertical wall to its left, goes through three distinct kinds of motion: it first tilts clockwise, then 'slides' away from the wall,and finally tilts counterclockwise. This dynamic highly favors both repulsion and penetration of the cuboid intruder as compared to that observed from its release farther away from the wall

cond-mat.soft↗
arXiv2021

Intruders cooperatively interact with a wall into granular matter

When a cylindrical object penetrates granular matter near a vertical boundary, it experiences two effects: its center of mass moves horizontally away from the wall, and it rotates around its symmetry axis. Here we show experimentally that, if two identical intruders instead of one are released side-by-side near the wall, both effects are also detected. However, unexpected phenomena appear due to a cooperative dynamics between the intruders. The net horizontal distance traveled by the common center of mass of the twin intruders is much larger than that traveled by one intruder released at the same initial distance from the wall, and the rotation is also larger. The experimental results are well described by the Discrete Element Method, which reveals a further unexpected phenomenon: when four intruders are released as a column near a wall, they penetrate like a chain that "bends away" from the wall so its lower end is very strongly repelled away from it.

cond-mat.soft↗
arXiv2018

Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning

Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a Support Vector Machine, an Artificial Neural Networks, a K-nearest neighbors and a Decision Tree Regressor. Considerations are made on parameter tuning and the validation and training approach. The results are compared to linear models used in previous works with similar data sets for generating temporal predictive models. These new tools perform better than linear approaches, in particular Nearest Neighbor Regression (KNNR) performs the best. These results provide better alternatives to be implemented operatively on the Argentine geospatial Risk system that is running since 2012.

stat.ML↗