Characterizing unstructured data with the nearest neighbor permutation entropy.

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data.

Non-Markovian Diffusion and Adsorption-Desorption Dynamics: Analytical and Numerical Results.

The interplay of diffusion with phenomena like stochastic adsorption-desorption, absorption, and reaction-diffusion is essential for life and manifests in diverse natural contexts. Many factors must be considered, including geometry, dimensionality, and the interplay of diffusion across bulk and surfaces. To address this complexity, we investigate the diffusion process in heterogeneous media, focusing on non-Markovian diffusion. This process is limited by a surface interaction with the bulk, described by a specific boundary condition relevant to systems such as living cells and biomaterials. The surface can adsorb and desorb particles, and the adsorbed particles may undergo lateral diffusion before returning to the bulk. Different behaviors of the system are identified through analytical and numerical approaches.

Entropy Production in a Fractal System with Diffusive Dynamics.

We study the entropy production in a fractal system composed of two subsystems, each of which is subjected to an external force. This is achieved by using the H-theorem on the nonlinear Fokker-Planck equations (NFEs) characterizing the diffusing dynamics of each subsystem. In particular, we write a general NFE in terms of Hausdorff derivatives to take into account the metric of each system. We have also investigated some solutions from the analytical and numerical point of view. We demonstrate that each subsystem affects the total entropy and how the diffusive process is anomalous when the fractal nature of the system is considered.

Results for Nonlinear Diffusion Equations with Stochastic Resetting.

In this study, we investigate a nonlinear diffusion process in which particles stochastically reset to their initial positions at a constant rate. The nonlinear diffusion process is modeled using the porous media equation and its extensions, which are nonlinear diffusion equations. We use analytical and numerical calculations to obtain and interpret the probability distribution of the position of the particles and the mean square displacement. These results are further compared and shown to agree with the results of numerical simulations. Our findings show that a system of this kind exhibits non-Gaussian distributions, transient anomalous diffusion (subdiffusion and superdiffusion), and stationary states that simultaneously depend on the nonlinearity and resetting rate.

κ-Carrageenan/poly(vinyl alcohol) functionalized films with gallic acid and stabilized with metallic ions.

Given the environmental issues caused by the extensive use of conventional petroleum-based packaging, this work proposes functional films based on commercial κ-carrageenan (κc), poly(vinyl alcohol) (PVA), and gallic acid (GA) prepared by the "casting" method. Metallic ions in the κc composition stabilized the films, supporting processability and suitable mechanical properties. However, the incorporated GA amount (6.25 and 10 wt%) in the films created from an aqueous κc solution at 3.0 % wt/v (κc3) prevented crystalline domains in the resulting materials. The κc3/GA6.25 and κc3/GA10 films had less tensile strength (8.50 ± 0.61 and 10.28 ± 0.65 MPa) and high elongation at break (2.36 ± 0.16 and 1.19 ± 0.17 %) compared to the other samples, respectively. Low κc contents (κc2.5/GA6.25 and κc2.5/GA10) promoted stiff films and less permeability to water vapor (5.36 ± 0.51 and 3.76 ± 0.02 [×10-12 g(Pa × m × s)-1], respectively. The κc/GA weight ratio also influenced the film wettability, indicating water contact angles (WCAs) between 55 and 74°. The surface wettability implies a low oil permeability and high water swelling capacity of up to 1600 %. The κc/GA also played an essential role in the film's antimicrobial action against Staphylococcus aureus and Escherichia coli. Thus, the κc3/GA10 film showed suitable physical, chemical, and biological properties, having the potential to be applied as food coatings.

Learning physical properties of liquid crystals with deep convolutional neural networks.

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy. We show that these deep neural networks identify liquid crystal phases and predict the order parameter of simulated nematic liquid crystals almost perfectly. We also show that convolutional neural networks identify the pitch length of simulated samples of cholesteric liquid crystals and the sample temperature of an experimental liquid crystal with very high precision.

Chitosan/gellan gum ratio content into blends modulates the scaffolding capacity of hydrogels on bone mesenchymal stem cells.

Here, we have demonstrated the production and characterization of hydrogel scaffolds based on chitosan/gellan gum (CS/GG) assemblies, without any covalent and metallic crosslinking agents, conventionally used to yield non-soluble polysaccharide-based materials. The polyelectrolyte complexes (physical hydrogels called as PECs) are characterized by Fourier-transform infrared spectroscopy, wide-angle X-ray scattering, and scanning electron microscopy. Hydrogels containing chitosan (CS) excesses (ranging from 60 to 80 wt%) were created. Durable polysaccharide-based scaffolds with structural homogeneity and interconnecting pore networks are developed by modulating the CS/GG weight ratio. The CS/GG hydrogel prepared at 80/20 CS/GG weight ratio (sample CS/GG80-20) is cytocompatible, supporting the attachment, growth, and spreading of bone marrow mesenchymal stem cells (BMSCs) after nine days of cell culture. The cytocompatibility is correlated to the swelling capacity of the PEC in PBS buffer (pH 7.4). By controlling the CS content, we can tune the water uptake of the material, enhancing the capacity to serve as a three-dimensional cell scaffold for BMSCs. This work presents for the first time that CS/GG hydrogels can be applied as scaffolds for tissue engineering purposes.

Poly(vinyl alcohol)/cationic tannin blend films with antioxidant and antimicrobial activities.

This study reports the synthesis, characterization and biological properties of films based on poly(vinyl alcohol) (PVA) and a cationic tannin polymer derivative (TN). Films are obtained from polymeric blends by tuning the PVA/TN weight ratios. The materials are characterized through infrared spectroscopy, X-ray photoelectron spectroscopy, contact angle measurements, mechanical analyses, and scanning electron microscopy. More hydrophilic surfaces are created by modulating the PVA and TN concentrations in the blends. Disintegration tests showed that the films present durability in phosphate buffer (pH 7.4) and low stability in simulated gastric fluid (pH 1.2). The film created at 90/10 PVA/TN weight ratio and crosslinked at 109 PVA/glutaraldehyde molar ratio (sample PVA10/TN10) supports the attachment and proliferation of bone marrow mesenchymal stem cells after 7 days of culture. The scaffolding capacity of the PVA10/TN10 surface is compared with titanium, one of the most important biomedical materials used in bone replacements. Also, the PVA/TN films exhibited cytocompatibility, antioxidant and antimicrobial activity against Staphylococcus aureus and Pseudomonas aeruginosa. These properties make PVA/TN films are candidates for biomedical applications in the tissue engineering field.

Dynamic Control of Light Direction Enabled by Stimuli-Responsive Liquid Crystal Gratings.

The ability to control light direction with tailored precision via facile means is long-desired in science and industry. With the advances in optics, a periodic structure called diffraction grating gains prominence and renders a more flexible control over light propagation when compared to prisms. Today, diffraction gratings are common components in wavelength division multiplexing devices, monochromators, lasers, spectrometers, media storage, beam steering, and many other applications. Next-generation optical devices, however, demand nonmechanical, full and remote control, besides generating higher than 1D diffraction patterns with as few optical elements as possible. Liquid crystals (LCs) are great candidates for light control since they can form various patterns under different stimuli, including periodic structures capable of behaving as diffraction gratings. The characteristics of such gratings depend on several physical properties of the LCs such as film thickness, periodicity, and molecular orientation, all resulting from the internal constraints of the sample, and all of these are easily controllable. In this review, the authors summarize the research and development on stimuli-controllable diffraction gratings and beam steering using LCs as the active optical materials. Dynamic gratings fabricated by applying external field forces or surface treatments and made of chiral and nonchiral LCs with and without polymer networks are described. LC gratings capable of switching under external stimuli such as light, electric and magnetic fields, heat, and chemical composition are discussed. The focus is on the materials, designs, applications, and future prospects of diffraction gratings using LC materials as active layers.

Controllable Dynamic Zigzag Pattern Formation in a Soft Helical Superstructure.

Zigzag pattern formation is a common and important phenomenon in nature serving a multitude of purposes. For example, the zigzag-shaped edge of green leaves boosts the transportation and absorption of nutrients. However, the elucidation of this complicated shape formation is challenging in fluid mechanics and soft condensed matter systems. Herein, a dynamically reconfigurable zigzag pattern deformation of a soft helical superstructure is demonstrated in a photoresponsive self-organized cholesteric liquid crystal superstructure under the simultaneous influence of an applied electric field and light irradiation. The zigzag-shaped pattern can not only be generated and terminated repeatedly on demand, but can also be easily manipulated by alternating irradiation of ultraviolet and visible light while under the influence of a sustained electric field. This unique behavior results from a delicate balance among the variable experimental parameters. The evolution of the zigzag-shaped pattern is successfully modeled by numerical simulations and has been monitored through diffraction of a probe laser. Interestingly, this fascinating zigzag-shaped pattern yields crescent-shaped diffraction pattern. The reversibly controllable dynamic zigzag pattern could enable the fabrication of novel photonic devices and architectures, besides greatly advancing the fundamental understanding of temporal behavior of ordered soft materials under combined stimuli.

Optically Reconfigurable Chiral Microspheres of Self-Organized Helical Superstructures with Handedness Inversion.

Optically reconfigurable monodisperse chiral microspheres of self-organized helical superstructures with dynamic chirality were fabricated via a capillary-based microfluidic technique. Light-driven handedness-invertible transformations between different configurations of microspheres were vividly observed and optically tunable RGB photonic cross-communications among the microspheres were demonstrated.

NIR light-directing self-organized 3D photonic superstructures loaded with anisotropic plasmonic hybrid nanorods.

Self-organized 3D photonic superstructures loaded with plasmonic hybrid nanorods were found to undergo structural transformation from body-centered cubic to simple cubic upon NIR-light irradiation resulting from the "photothermal effect" of gold nanorods. Furthermore, dynamic NIR light-directed red, green and blue reflections of the nanocomposites were demonstrated.

Mechanism of electrically induced photonic band gap broadening in polymer stabilized cholesteric liquid crystals with negative dielectric anisotropies.

We experimentally observed that the photonic band gap (reflection band) of polymer stabilized cholesteric liquid crystals with negative dielectric anisotropies can be greatly broadened under DC electric fields. We explored the underlying mechanism. We found that the dispersed polymer network moved when DC voltages were applied across the liquid crystal cell. The motion of the polymer network stretched the helical pitch of the liquid crystal on one side of the cell and compressed the helical pitch on the other side of the cell. We proposed a phenomenological theory to explain the motion of the polymer network and the effect of the polymer network on the helical pitch, and this theoretical prediction agreed well with the experimental results.

Unusual diffusing regimes caused by different adsorbing surfaces.

A confined liquid with dispersed neutral particles is theoretically studied when the limiting surfaces present different dynamics for the adsorption-desorption phenomena. The investigation considers different non-singular kernels in the kinetic equations at the walls, where the suitable choice of the kernel can account for the relative importance of physisorption or chemisorption. We find that even a small difference in the adsorption-desorption rate of one surface (relative to the other) can drastically affect the behavior of the whole system. The surface and bulk densities and the dispersion are calculated when several scenarios are considered and anomalous-like behaviors are found. The approach described here is closely related to experimental situations, and can be applied in several contexts such as dielectric relaxation, diffusion-controlled relaxation in liquids, liquid crystals, and amorphous polymers.

Light-directing omnidirectional circularly polarized reflection from liquid-crystal droplets.

Constructing and tuning self-organized three-dimensional (3D) superstructures with tailored functionality is crucial in the nanofabrication of smart molecular devices. Herein we fabricate a self-organized, phototunable 3D photonic superstructure from monodisperse droplets of one-dimensional cholesteric liquid crystal (CLC) containing a photosensitive chiral molecular switch with high helical twisting power. The droplets are obtained by a glass capillary microfluidic technique by dispersing into PVA solution that facilitates planar anchoring of the liquid-crystal molecules at the droplet surface, as confirmed by the observation of normal incidence selective circular polarized reflection in all directions from the core of individual droplet. Photoirradiation of the droplets furnishes dynamic reflection colors without thermal relaxation, whose wavelength can be tuned reversibly by variation of the irradiation time. The results provided clear evidence on the phototunable reflection in all directions.

Light-driven reversible handedness inversion in self-organized helical superstructures.

We report here a fast-photon-mode reversible handedness inversion of a self-organized helical superstructure (i.e., a cholesteric liquid crystal phase) using photoisomerizable chiral cyclic dopants. The two light-driven cyclic azobenzenophanes with axial chirality show photochemically reversible trans to cis isomerization in solution without undergoing thermal or photoinduced racemization. As chiral inducing agents, they exhibit good solubility, high helical twisting power, and a large change in helical twisting power due to photoisomerization in three commercially available, structurally different achiral liquid crystal hosts. Therefore, we were able to reversibly tune the reflection colors from blue to near-IR by light irradiation from the induced helical superstructure. More interestingly, the different switching states of the two chiral cyclic dopants were found to be able to induce a helical superstructure of opposite handedness. In order to unambiguously determine the helical switching, we employed a new method that allowed us to directly determine the handedness of the long-pitched self-organized cholesteric phase.