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In an attempt to further characterise the near-Earth asteroid (NEA) population we present 38 new light-curves acquired between September 2020 and November 2023 for NEAs (7335) 1989 JA, (7822) 1991 CS, (154244) 2002 KL6 and (159402) 1999 AP10, obtained from observations taken at the Teide Observatory (Tenerife, Spain). With these new observations along with archival data, we computed their first shape models and spin solutions by applying the light curve inversion method. The obtained rotation periods are in good agreement with those reported in previous works, with improved uncertainties. Additionally, besides the constant period models for (7335) 1989 JA, (7822) 1991 CS and (159402) 1999 AP10, our results for (154244) 2002 KL6 suggest that it could be affected by a Yarkovsky-O'Keefe-Radzievskii-Paddack acceleration with a value of $\upsilon \simeq -7 \times 10^{-9}$ rad d$^{-2}$. This would be one of the first detections of this effect slowing down an asteroid.

The transit method allows the detection and characterization of planetary systems by analyzing stellar light curves. Convolutional neural networks appear to offer a viable solution for automating these analyses. In this research, two 1D convolutional neural network models, which work with simulated light curves in which transit-like signals were injected, are presented. One model operates on complete light curves and estimates the orbital period, and the other one operates on phase-folded light curves and estimates the semimajor axis of the orbit and the square of the planet-to-star radius ratio. Both models were tested on real data from TESS light curves with confirmed planets to ensure that they are able to work with real data. The results obtained show that 1D CNNs are able to characterize transiting exoplanets from their host star's detrended light curve and, furthermore, reducing both the required time and computational costs compared with the current detection and characterization algorithms.

To simulate a 200 nm photoexcitation in cyclobutanone to the n-3s Rydberg state, classical trajectories were excited from a Wigner distribution to the singlet state manifold based on excitation energies and oscillator strenghts. Twelve singlet and twelve triplet states are treated using TD-B3LYP-D3/6-31+G$^{**}$ for the electronic structure and the nuclei are propagated with the Tully Surface Hopping method. Using TD-DFT, we are able to predict the bond cleavage that takes place on the S$_1$ surface as well as the ultrafast deactivation from the Rydberg n-3s state to the n$\pi^*$. After showing that triplet states and higher-lying singlet states do not play any crucial role during the early dynamics (i.e., the first 300 fs), the SA(6)-CASSCF(8,11)/aug-cc-pvDZ method is used as an electronic structure and the outcome of the non-adiabatic dynamic simulations is recomputed. Gas-phase ultrafast electron diffraction (GUED) spectra are computed for both electronic structure methods, showing significantly different results.

We present a surface analog to a dripping faucet, where a viscous liquid slides down an immiscible meniscus. Periodic pinch-off of the dripping filament is observed, generating a succession of monodisperse floating lenses. We show that this interfacial dripping faucet can be described analogously to its single-phase counterpart, replacing surface tension by the spreading coefficient, and even undergoes a transition to a jetting regime. This liquid/liquid/gas system opens perspectives for the study of the dynamics of emulsions at interfaces., Comment: 9 pages, 8 figures, 5 supplemental movies

We present 24 new dense lightcurves of the near-Earth asteroids (3103) Eger, (161989) Cacus, (2100) Ra-Shalom and (12711) Tukmit, obtained with the Instituto Astrof\'isico Canarias 80 and Telescopio Abierto Remoto 2 telescopes at the Teide Observatory (Tenerife, Spain) during 2021 and 2022, in the framework of projects visible NEAs observations survey and NEO Rapid Observation, Characterization and Key Simulations. The shape models and rotation state parameters ($P$, $\lambda$, $\beta$) were computed by applying the lightcurve inversion method to the new data altogether with the archival data. For (3013) Eger and (161989) Cacus, our shape models and rotation state parameters agree with previous works, though they have smaller uncertainties. For (2100) Ra-Shalom, our results also agree with previous studies. Still, we find that a Yarkovsky - O'Keefe - Radzievskii - Paddack acceleration of $\upsilon = (0.223\pm0.237)\times10^{-8}$ rad d$^{-2}$ slightly improves the fit of the lightcurves, suggesting that (2100) Ra-Shalom could be affected by this acceleration. We also present for the first time a shape model for (12711) Tukmit, along with its rotation state parameters ($P=3.484900 \pm 0.000031$ hr, $\lambda = 27^{\circ}\pm 8^{\circ}$, $\beta = 9^{\circ} \pm 15^{\circ}$).

The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial neural network model that is able to detect these transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model, which was later tested, obtaining an accuracy of 99.02 % and an estimated error (loss function) of 0.03. These results, among others, helped to confirm that the 1D CNN is a good choice for working with non-phased-folded Mandel and Agol light curves with transits. It also reduces the number of light curves that have to be visually inspected to decide if they present transit-like signals and decreases the time needed for analyzing each (with respect to traditional analysis).

In splat painting, a collection of liquid droplets is projected onto the substrate by imposing a controlled acceleration to a paint-loaded brush. To unravel the physical phenomena at play in this artistic technique, we perform a series of experiments where the amount of expelled liquid and the resulting patterns on the substrate are systematically characterized as a function of the liquid viscosity and brush acceleration. Experimental trends and orders of magnitude are rationalized by simple physical models, revealing the existence of an inertia-dominated flow in the anisotropic, porous tip of the brush. We argue that splat painting artists intuitively tune their parameters to work in this regime, which may also play a role in other pulsed flows, like violent expiratory events or sudden geophysical processes., Comment: 15 pages, 10 figures

A significant step forward in Lithium-ion batteries (LIBs) developments can only be achieved by proposing mold-breaking research based on selecting the best materials for the cell components, optimizing cell manufacture, anticipating the degradation mechanisms of the LIBs, and consolidating the regeneration processes of damaged batteries. LIBs with longer recycling life, better safety, and the ability to be reused will establish sustainable state-of-the-art batteries with maximum energy efficiency, low costs, and minimal CO2 emissions within a circular economy, promoting sustainability in areas as relevant as electromobility and portable electronics. Recently, there has been increasing interest in applying Artificial Intelligence (AI) techniques and their subclasses to better predict novel materials with designed properties. This collection of methods has already obtained considerable success, having been used to predict numerous physical properties of materials. However, compared to other fields, the materials data are typically much smaller and sometimes more diverse, which undoubtedly affects the construction and effectiveness of AI models. At present, several Data Augmentation (DA) methods have been proposed in materials science based on flipping, rotating, and distorting the unit cells of materials, which have been demonstrated to be very efficient in increasing the size and quality of data. Here we present an even more effective new method of Data Augmentation based on the lattice scaling of crystal structures. In the lattice scaling DA method, the unit cell is perturbed, undergoing an increase and decrease of the unit cell volume in an isotropic or anisotropic way. This transformation is particularly pertinent for battery components since volume changes of up to 5% have been reported for the insertion-based LIBs during cycling.

Understand and predict the dynamics of dispersed micro-objects in microfluidics is crucial in numerous natural, industrial and technological situations. In this paper, we experimentally characterized the equilibrium velocity $V$ and lateral position $\varepsilon$ of various dispersed micro-objects such as beads, bubbles and drops, in a cylindrical microchannel over an unprecedent wide range of parameters. By systematically varying the dimensionless object size ($d \in [0.1; 1]$), the viscosity ratio ($\lambda \in [10^{-2}; \infty[$), the density ratio ($\varphi \in [10^{-3}; 2]$), the Reynolds number ($\Re \in [10^{-2}; 10^2]$), and the capillary number ($\text{Ca} \in [10^{-3}; 0.3]$), we offer a general study exploring various dynamics from the nonderformable viscous regime to the deformable visco-inertio-capillary regime, thus enabling to highlight the sole and combined roles of inertia and capillary effects on lateral migration. The experiments are compared and well-agree with a steady 3D Navier-Stokes model for incompressible two-phase fluids including both the effects of inertia and possible interfacial deformations. This model enables to rationalize the experiments and to provide an exhaustive parametric analysis on the influence of the main parameters of the problem, mainly on two aspects: the stability of the centered position and the velocity of the dispersed object. Interestingly, we propose a useful correlation for the object velocity $V$ as functions of the $d$, $\varepsilon$ and $\lambda$, obtained in the $\text{Re}=\text{Ca}=0$ limit, but actually valid for a larger range of values of $\text{Re}$ and $\text{Ca}$ in the linear regimes., Comment: 22 pages, 11 figures, submitted to Journal of Fluid Mechanics

Evaporation of multi-component liquid mixtures in confined geometries, such as capillaries, is crucial in applications such as microfluidics, two-phase cooling devices, and inkjet printing. Predicting the behaviour of such systems becomes challenging because evaporation triggers complex spatio-temporal changes in the composition of the mixture. These changes in composition, in turn, affect evaporation. In the present work, we study the evaporation of aqueous glycerol solutions contained as a liquid column in a capillary tube. Experiments and direct numerical simulations show three evaporation regimes characterised by different temporal evolutions of the normalised mass transfer rate (or Sherwood number, $Sh$), namely $Sh (\tilde{t}) = 1$, $Sh \sim 1/\sqrt{\tilde{t}}$, and $Sh \sim \exp\left(-\tilde{t}\right)$. Here $\tilde{t}$ is a normalised time. We present a simplistic analytical model which shows that the evaporation dynamics can be expressed by the classical relation $Sh = \exp \left( \tilde{t} \right) \mathrm{erfc} \left( \sqrt{\tilde{t}}\right)$. For small and medium $\tilde{t}$, this expression results in the first and second of the three observed scaling regimes, respectively. This analytical model is formulated in the limit of pure diffusion and when the penetration depth $\delta(t)$ of the diffusion front is much smaller than the length $L(t)$ of the liquid column. When $\delta \approx L$, finite length effects lead to $Sh \sim \exp\left(-\tilde{t}\right)$, i.e. the third regime. Finally, we extend our analytical model to incorporate the effect of advection and determine the conditions under which this effect is important. Our results provide fundamental insight into the physics of selective evaporation from a multi-component liquid column.

In the present work, we developed a sustainable and effective antimicrobial surface film based on Micro-Fibrillated Cellulose. The resulting porous cellulose thin film is barely noticeable to human eyes due to its sub-micron thickness, of which the coverage, porosity and microstructure can be modulated by the formulations developed. Using goniometers and a quartz crystal microbalance (QCM), we observed a threefold reduction in water contact angles and accelerated (more than 50% faster) water evaporation kinetics on the cellulose film. The thin film exhibits not only a rapid inactivation effect against SARS-CoV-2 in 5 minutes, following deposition of the virus loaded droplets, but also an exceptional ability to reduce contact transfer of liquid, e.g. respiratory droplets, onto surfaces such as artificial skin by more than 90%. It also exhibits excellent antimicrobial performance in inhibiting the growth of both gram-negative and gram-positive bacteria (E.coli and S.epidermidis) due to the excellent porosity and hydrophilicity. Additionally, the cellulose film shows nearly 100% resistance to skin scraping in dry condition thanks to its strong attachment to the substrate, whilst good removability once wetted, suggesting its practical suitability for daily use. Importantly, the coating can be formed on solid substrates readily by spraying and requires solely a simple formulation of a plant-based cellulose material with no additives, rendering it a scalable, affordable and green solution for antimicrobial surfaces. Implementing such cellulose films could thus play a significant role in controlling future pan- and epidemics, in particularly during the first phase when appropriate medication needs to be developed.

Pathogens contained in airborne respiratory droplets have been seen to remain infectious for periods of time that depend on the ambient temperature and humidity. In particular, regarding the humidity, the empirically least favorable conditions for the survival of viral pathogens are found at intermediate humidities. However, the precise physico-chemical mechanisms that generate such least-favorable conditions are not understood yet. In this work, we analyze the evaporation dynamics of respiratory-like droplets in air, semi-levitating them on superhydrophobic substrates with minimal solid-liquid contact area. Our results reveal that, compared to pure water droplets, the salt dissolved in the droplets can significantly change the evaporation behaviour, especially for high humidities close to and above the deliquesence limit. Due to the hygroscopic properties of salt, water evaporation is inhibited once the salt concentration reaches a critical value that depends on the relative humidity. The salt concentration in a stable droplet reaches its maximum at around 75% relative humidity, generating conditions that might shorten the time in which pathogens remain infectious., Comment: 11 pages, 5 figures. Added missing figure 3 compared to previous version. Also slightly edited the bibliography style