State-of-the-Art Development in Liquid Crystal Biochemical Sensors

3D Directional Assembly of Liquid Crystal Molecules

The precise construction of hierarchically long-range ordered structures using molecules as fundamental building blocks can fully harness their anisotropy and potential. However, the 3D, high-precision, and single-step directional assembly of molecules is a long-pending challenge. Here, a 3D directional molecular assembly strategy via femtosecond laser direct writing (FsLDW) is proposed and the feasibility of this approach using liquid crystal (LC) molecules as an illustrative example is demonstrated. The physical mechanism for femtosecond (fs) laser-induced assembly of LC molecules is investigated, and precise 3D arbitrary assembly of LC molecules is achieved by defining the discretized laser scanning pathway. Additionally, an LC-based Fresnel zone plate array with polarization selection and colorization imaging functions is fabricated to further illustrate the potential of this method. This study not only introduces a 3D high-resolution alignment method for LC-based functional devices but also establishes a universal protocol for the precise 3D directional assembly of anisotropic molecules.

Real-Time pH Sensor in Bacterial Microenvironments Using Liquid Crystal Core-Shell Microspheres

It is well-known that the bacterial microenvironment imposes restrictions on the growth and behavior of bacteria. The localized monitoring of microenvironmental factors is appreciated when consulting bacterial adaptation and behavior in the presence of chemical or mechanical stimuli. Herein, we developed a novel liquid crystal (LC) biosensor in a microsphere configuration for real-time 3D monitoring of the bacteria microenvironment, which was implemented by a microfluidic chip. As a proof of concept, a LC gel (LC-Gel) microsphere biosensor was prepared and employed in the localized pH changes of bacteria by observing the configuration change of LC under polarized optical microscopy. Briefly, the microsphere biosensor was constructed in core-shell configuration, wherein the core contained LCE7 (a nematic LC) doped with 4-pentylbiphenyl-4'-carboxylic acid (PBA), and the shell encapsulated the bacteria. The protonation of carboxyl functional groups of the PBA induced a change in charge density on the surface of LCE7 and the orientation of E7 molecules, resulting in the transitions of the LC nucleus from axial to bipolar. The developed LC-Gel microspheres pH sensor exhibited its dominant performance on localized pH real-time sensing with a resolution of 0.1. An intriguing observation from the prepared pH biosensor was that the diverse bacteria impelled distinct acidifying or alkalizing effects. Overall, the facile LC-Gel microsphere biosensor not only provides a versatile tool for label-free, localized pH monitoring but also opens avenues for investigating the effects of chemical and mechanical stimuli on cellular metabolism within bacterial microenvironments.

Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals

Machine learning is becoming a valuable tool in the characterisation and property prediction of liquid crystals. It is thus worthwhile to be aware of the possibilities but also the limitations of current machine learning algorithms. In this study we investigated a phase sequence of isotropic - fluid smecticA - hexatic smectic B - soft crystal CrE - crystalline. This is a sequence of transitions between orthogonal phases, which are expected to be difficult to distinguish, because of only minute changes in order. As expected, strong first order transitions such as the liquid to liquid crystal transition and the crystallisation can be distinguished with high accuracy. It is shown that also the hexatic SmB to soft crystal CrE transition is clearly characterised, which represents the transition from short- to long-range order. Limitations of convolutional neural networks can be observed for the fluid to hexatic SmA to SmB transition, where both phases exhibit short-range ordering.

Chitosan functionalized gold nanostars as a theranostic platform for intracellular microRNA detection and photothermal therapy

The development of a theranostic platform that integrates both diagnostic and therapeutic capabilities is in great need for precise and personalized medicine. Here, we present a novel nanoplatform (AuNS@CS-hpDNA) formulated by chitosan functionalized gold nanostar composites and further complexed with fluorescent hairpin DNA (hpDNA) probes for tumor-related miRNA imaging and photothermal therapy (PTT). The optimized AuNS@CS-hpDNA nanoplatform mediated efficient hpDNA probe loading and intracellular delivery. Subsequently, the cytosol transfer of the hpDNA probe enabled specific hybridization using the targeted miRNA, which triggered the recovery of fluorescence for the precise detection of biomarker miR21 in living cells and realized the distinguishing cancer cell line MCF-7 and normal cells. Meanwhile, the AuNS@CS-hpDNA nanoplatform exhibited excellent photothermal conversion properties, which induced efficient cancer cell killing under laser irradiation. Thus, the developed AuNS@CS-hpDNA nanoplatform could simultaneously realize the precise detection of cancer cells and accurately initiate efficient PTT, which represents a promising strategy for precise cancer therapy.

Multimode sensing based on optical microcavities

Optical microcavities have the ability to confine photons in small mode volumes for long periods of time, greatly enhancing light-matter interactions, and have become one of the research hotspots in international academia. In recent years, sensing applications in complex environments have inspired the development of multimode optical microcavity sensors. These multimode sensors can be used not only for multi-parameter detection but also to improve measurement precision. In this review, we introduce multimode sensing methods based on optical microcavities and present an overview of the multimode single/multi-parameter optical microcavities sensors. Expected further research activities are also put forward.

Machine learning classification of polar sub-phases in liquid crystal MHPOBC

Experimental polarising microscopy texture images of the fluid smectic phases and sub-phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*), ferroelectric (SmC*), ferrielectric (SmC1/3*), and antiferroelectric (SmCA*) using convolutional neural networks, CNNs. Two neural network architectures were tested, a sequential convolutional neural network with varying numbers of layers and a simplified inception model with varying number of inception blocks. Both models are successful in binary classifications between different phases as well as classification between all four phases. Optimised architectures for the multi-phase classification achieved accuracies of (84 ± 2)% and (93 ± 1)% for sequential convolutional and inception networks, respectively. The results of this study contribute to the understanding of how CNNs may be used in classifying liquid crystal phases. Especially the inception model is of sufficient accuracy to allow automated characterization of liquid crystal phase sequences and thus opens a path towards an additional method to determine the phases of novel liquid crystals for applications in electro-optics, photonics or sensors. The outlined procedure of supervised machine learning can be applied to practically all liquid crystal phases and materials, provided the infrastructure of training data and computational power is provided.

Sensitive detection of 17β-estradiol at a picomolar level using an aptamer-assisted liquid crystal-based optical sensor

A liquid crystal (LC)-based aptasensor was developed that can detect 17β-estradiol (E2) at the picomolar level. This aptasensor is based on competitive reactions of the aptamer that interacts with cetyl trimethyl ammonium bromide (CTAB) and E2 at the aqueous/LC interface. The long alkyl chain of CTAB anchored the 4-cyano-4'-pentylbiphenyl (5CB) to a homeotropic state and controls the local anchoring depending on the extent of electrostatic interaction with the aptamer. Upon addition of the aptamer solution to the CTAB-saturated LC layer, LCs change from dark to bright optical response. This is due to the perturbed orientation of 5CB at the aqueous/LC interface as a result of electrostatic attraction of the cationic group of CTAB and the phosphate group of the aptamer. The conformational change of the aptamer due to specific binding with E2 weakens the electrostatic attraction between CTAB and aptamer. When specific binding becomes relatively dominant, CTAB induces the orientation of LCs to the homeotropic state, resulting in a dark optical image observed. We also analyzed the change in the optical response of LCs according to the interfacial events and compared the grayscale values of the optical image for each concentration of E2 to determine the detection limit. Accordingly, the detection limit of the E2 sensor was found to be 3.1 pM (0.8 pg/ml) in Tris-buffered saline (TBS), and 6.8 pM (1.9 pg/ml) in human urine. The LC-based optical aptasensor was thus shown to be highly sensitive and selective with no requirement for complex analysis equipment.

Effect of airflow rate and drainage on the properties of 2D smectic liquid crystal foams

As a two-phase system, foams are widely applied in the industry and exist ubiquitously in our daily lives. For this reason, studying them and investigating the parameters that affect their properties is crucial for the development of new and improved foam-based products. In this paper, we create 2D foam using an ordered fluid, the smectic liquid crystal (LC), and discuss the experimental parameters that affect their fabrication, including temperature and confining conditions. Then, we examine the influence of the injected airflow rate and drainage on their structure, size, liquid fraction, and stability. Finally, we compare their behavior to that of low-viscosity liquid foams and discuss the difference between them. Our findings indicate that surface tension is the dominant parameter in LC foam systems. Despite the strong elasticity of LCs, surface tension plays a crucial role in determining the properties of elastic foams. These results provide valuable insights that can be applied to different industrial applications. For instance, they may find relevance in the fields of cosmetics, thermal insulation, oil recovery, and sensing, where the fabrication of foams with high-viscosity fluids is required.