Influence of COVID-19 confinement on students' performance in higher education.

This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autónoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

A Model for the Characterization of the Polarizability of Thin Films Independently of the Thickness of the Film.

The dielectric properties of thin films can be modified relative to the bulk material because the interaction between film and substrate influences the mobility of the atoms or molecules in the first layers. Here we show that a strong scale effect occurs in nanometer size octadecylammine thin films. This effect is attributed to the different distribution of molecules depending on the size of the film. To accurately describe this effect, we have developed a model which is a reinterpretation of the linearized Thomas-Fermi approximation. Within this model, we have been able to characterize the polarizability of thin films independently of the thickness of the film.

Feedforward neural network methodology to characterize thin films by Electrostatic Force Microscopy.

The contribution of the present paper is in introducing a numerical method to improve the automatic characterization of thin films by increasing the effectiveness of numerical methods that take into account the macroscopic shape of the tip. To achieve this objective, we propose the combination of different feedforward neural networks architectures adapted to the specific requirements of the physical system under study. First, an Adaline architecture is redefined as a linear combination of Green functions obtained from the Laplace equation. The learning process is also redefined to accurately calculate the electrostatic charges inside the tip. We demonstrate that a complete training set for the characterization of thin films can be easily obtained by this methodology. The characterization of the sample is developed in a second stage where a multilayer perceptron is adapted to work efficiently in experimental conditions where some experimental data can be lost. We demonstrate that a very efficient strategy is to use evolutionary algorithms as training method. By the modulation of the fit function, we can improve the network performance in the characterization of thin films where some information is missing or altered by experimental noise due to the small tip-sample working distances. By doing so, we can discriminate the conductive properties of thin films from force curves that have been altered explicitly to simulate realistic experimental conditions.

Artificial intelligence in nanotechnology.

During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

Fast and non-invasive conductivity determination by the dielectric response of reduced graphene oxide: an electrostatic force microscopy study.

The high dispersion found in the literature for the conductivity of Reduced Graphene Oxide (RGO) layers makes it highly desirable to develop fast and non-invasive methods for their characterization. Here we show that Electrostatic Force Microscopy (EFM) is an in situ, fast, and contactless technique to evaluate the conductivity of chemically derived graphene layers. The dielectric response of RGO flakes is observed to depend on their conductivity in the range of 0-3 S m(-1). Interestingly, we also find that for electrostatic purposes, a graphene layer is equivalent to an extremely thin dielectric layer with an effective permittivity (ε(eff)) that depends on the conductivity of the layers and spans from 5 for the insulating layers, to 2000 for the more conductive ones. We discuss how these high values of ε(eff) are a consequence of the incomplete screening of electric fields through graphene layers.

Enhanced dielectric constant resolution of thin insulating films by electrostatic force microscopy.

Electrostatic force microscopy has been shown to be a useful tool to determine the dielectric constant of insulating films of nanometer thicknesses that play a key role in many electrical, optical and biological phenomena. Previous approaches have made use of simple analytical formulas to analyze the experimental data for thin insulating films deposited directly on a metallic substrate. Here we show that the sensitivity of the EFM signal to changes in the dielectric constant of the thin film can be enhanced by using dielectric substrates with low dielectric constants. We present detailed numerical calculations of the tip-sample electrostatic interaction in the following setup: an insulating thin film, a dielectric substrate (or spacing layer) of known low dielectric constant and a metallic electrode. The EFM sensitivity to the dielectric constant increases with the thickness of the spacing layer and saturates for thicknesses above 100-300 nm, when it is close to that of an infinite medium.

Dipolar origin of water etching of amino acid surfaces.

The etching induced by water on hydrophobic (001) surfaces of enantiomeric L-, D- and racemic DL-valine crystals has been characterized by means of atomic force microscopy (AFM) at ambient conditions. Well-defined chiral parallelepipedic shallow patterns, one bilayer deep, are observed for the enantiomeric crystals with sides (steps) oriented along low index crystallographic directions. Hence, chirality can be readily identified by visual inspection of an AFM image after etching. The formation of such regular patterns can be rationalized using basic concepts of electrical dipolar interactions. The key factor that determines the relative etching rate for each step and thus defines the shape of the etching patterns is the orientation of the molecular dipoles with respect to the step edge. The simplicity of the approach allows the prediction of the effect of water etching on other amino acid crystals as well as the effect of the interaction of water with amino acid molecules forming part of more complex structures.

Influence of the macroscopic shape of the tip on the contrast in scanning polarization force microscopy images.

We demonstrate that a quantitative analysis of the contrast obtained in electrostatic force microscopy images that probe the dielectric response of the sample (scanning polarization force microscopy (SPFM)) requires numerical simulations that take into account both the macroscopic shape of the tip and the nanoscopic tip apex. To simulate the SPFM contrast, we have used the generalized image charge method (GICM), which is able to accurately deal with distances between a few nanometers and several microns, thus involving more than three orders of magnitude. Our numerical simulations show that the macroscopic shape of the tip accounts for most of the SPFM contrast. Moreover, we find a quasi-linear relation between the working tip-sample distance and the contrast for tip radii between 50 and 200 nm. Our calculations are compared with experimental measurements of the contrast between a thermally grown silicon oxide sample and a few-layer graphene film transferred onto it.

An inverse problem solution for undetermined electrostatic force microscopy setups using neural networks.

A technique that combines a theoretical description of the electrostatic interaction and artificial neural networks (ANNs) is used to solve an inverse problem in scanning probe microscopy setups. Electrostatic interaction curves calculated by the generalized image charge method are used to train and validate the ANN in order to estimate unknown magnitudes in highly undetermined setups. To illustrate this technique, we simultaneously estimate the tip-sample distance and the dielectric constant for a system composed of a tip scanning over a metallic nanowire. In a second example, we use this method to quantitatively estimate the dielectric constant for an even more undetermined system where the tip shape (characterized by three free parameters) is not known. Finally, the proposed method is validated with experimental data.

Induced water condensation and bridge formation by electric fields in atomic force microscopy.

We present an analytical model that explains how, in humid environments, the electric field near a sharp tip enhances the formation of water menisci and bridges between the tip and a sample. The predictions of the model are compared with experimental measurements of the critical distance where the field strength causes bridge formation.

Sensing dipole fields at atomic steps with combined scanning tunneling and force microscopy.

The electric field of dipoles localized at the atomic steps of metal surfaces due to the Smoluchowski effect were measured from the electrostatic force exerted on the biased tip of a scanning tunneling microscope. By varying the tip-sample bias the contribution of the step dipole was separated from changes in the force due to van der Waals and polarization forces. Combined with electrostatic calculations, the method was used to determine the local dipole moment in steps of different heights on Au(111) and on the twofold surface of an Al-Ni-Co decagonal quasicrystal.

Initial stages of water adsorption on NaCl (100) studied by scanning polarization force microscopy.

Scanning polarization force microscopy was used to study the topography, polarizability, and contact potential of cleaved NaCl(100) as a function of the relative humidity (RH) between < 5% and 40%. In this humidity range there are reversible changes in surface potential and polarizability, while large scale modifications in step topography and irreversible ion redistribution occur above 40% RH. In dry conditions the surface contact potential was more negative near atomic steps than over flat terraces. As humidity was increased, changes were observed in the local polarizability of the steps due to ionic solvation, and the contact potential of the terraces became more negative. At 40% RH surface-potential differences between steps and terraces could no longer be detected. These results are interpreted in terms of preferential anion solvation, initially localized near steps, and later spreading over the entire surface.