Microscale generation and control of nanosecond light by light in a liquid crystal.

The softness of liquid crystals, their anisotropic material properties, their strong response to external fields and their ability to align on patterned surfaces makes them unsurpassable for a number of photonic applications, such as flat-panel displays, light modulators, tunable filters, entangled photon light sources, lasers and many others. However, the microscale integration of liquid crystals into microphotonic devices that not only perform like silicon photonic chips but also use less energy, operate exclusively on light, are biocompatible and can self-assemble has not been explored. Here we demonstrate a soft-matter photonic chip that integrates tunable liquid-crystal microlasers and laser microprinted polymer waveguides.

Photonic defect modes in cholesteric liquid crystal resonators with embedded isotropic layers.

Photonic defect modes are explored as a viable alternative to standard photonic band edge modes in photonic crystal applications, especially due to their typically high Q-factors and local density of states. For example, they can be used in nonlinearity enhancement, lasing, and cavity quantum electrodynamics. However, they are strongly dependent on any structural change and need to be well-controlled to ensure the desired resonance frequency.

Photonic eigenmodes and transmittance of finite-length 1D cholesteric liquid crystal resonators.

Cholesteric liquid crystals exhibit a periodic helical structure that partially reflects light with wavelengths comparable to the period of the structure, thus performing as a one-dimensional photonic crystal. Here, we demonstrate a combined experimental and numerical study of light transmittance spectra of finite-length helical structure of cholesteric liquid crystals, as affected by the main system and material parameters, as well as the corresponding eigenmodes and frequency eigenspectra with their Q-factors. Specifically, we have measured and simulated transmittance spectra of samples with different thicknesses, birefringences and for various incident light polarisation configurations as well as quantified the role of refractive index dispersion and the divergence of the incident light beam on transmittance spectra.

Neural networks determination of material elastic constants and structures in nematic complex fluids.

Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light.