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.
Control of light is of fundamental importance across all fields of science and technology. Next‐generation devices require nonmechanical capabilities and easy tuning for adapting to fastchanging situations. How, over the last 50 years, liquid crystals have been used as switchable diffraction gratings, along with fabrication, designs, challenges, and the future of liquid crystal gratings are discussed.
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.
Blue rays: Photoirradiation of self‐organized, phototunable 3D photonic superstructures of monodisperse droplets of a cholesteric liquid crystal (CLC) containing a photosensitive chiral molecular switch gives dynamic reflection colors. The colors can be tuned reversibly upon light irradiation, and in groups of droplets blue‐ray patterns arise from photonic cross‐communication between neighboring droplets.
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.
Controllable dynamic zigzag‐shaped pattern formation in a photoresponsive soft self‐organized helical superstructure is accomplished via the coupled stimuli effect of electric field and light, which has enabled a unique diffraction pattern not found before.
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.
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.
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.
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.
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.
The comb model is a simplified description for anomalous diffusion under geometric constraints. It represents particles spreading out in a two-dimensional space where the motions in the x-direction ...are allowed only when the y coordinate of the particle is zero. Here, we propose an extension for the comb model via Langevin-like equations driven by fractional Gaussian noises (long-range correlated). By carrying out computer simulations, we show that the correlations in the y-direction affect the diffusive behavior in the x-direction in a non-trivial fashion, resulting in a quite rich diffusive scenario characterized by usual, superdiffusive or subdiffusive scaling of second moment in the x-direction. We further show that the long-range correlations affect the probability distribution of the particle positions in the x-direction, making their tails longer when noise in the y-direction is persistent and shorter for anti-persistent noise. Our model thus combines and allows the study/analysis of the interplay between different mechanisms of anomalous diffusion (geometric constraints and long-range correlations) and may find direct applications for describing diffusion in complex systems such as living cells.
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