Areas of opportunity related to design of chemical and biological sensors based on liquid crystals

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.

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.

Aquatic Functional Liquid Crystals: Design, Functionalization, and Molecular Simulation

Aquatic functional liquid crystals, which are ordered molecular assemblies that work in water environment, are described in this review. Aquatic functional liquid crystals are liquid-crystalline (LC) materials interacting water molecules or aquatic environment. They include aquatic lyotropic liquid crystals and LC based materials that have aquatic interfaces, for example, nanoporous water treatment membranes that are solids preserving LC order. They can remove ions and viruses with nano- and subnano-porous structures. Columnar, smectic, bicontinuous LC structures are used for fabrication of these 1D, 2D, 3D materials. Design and functionalization of aquatic LC sensors based on aqueous/LC interfaces are also described. The ordering transitions of liquid crystals induced by molecular recognition at the aqueous interfaces provide distinct optical responses. Molecular orientation and dynamic behavior of these aquatic functional LC materials are studied by molecular dynamics simulations. The molecular interactions of LC materials and water are key of these investigations. New insights into aquatic functional LC materials contribute to the fields of environment, healthcare, and biotechnology.

Non-spherical assemblies of chitin nanocrystals by drop impact assembly

Preparing complex non-spherical assemblies of elongated nanoparticles and exploring their topological conformations is a challenge due to liquid crystals' mobility and elastic distortion. Here, we fabricated a variety of non-spherical liquid crystal assemblies of chitin nanocrystals (ChNCs) in a coagulation bath containing sodium triphosphate (STP) by drop impact assembly method, and the forming mechanism and internal topology were systematically investigated. The collection height, ChNCs concentration, and STP concentration have significant influence on the shape and size of the assembled structures. Long-range ordered structures and long-lived topological textures of the ChNCs liquid crystal can be obtained since a molecular interaction of hydrogen bonding and electrostatic attractions between ChNCs and STP occur during the impact assembly. Rheological and kinetic analysis suggested the shear thinning behavior of the ChNCs liquid crystals and the rapid gelation phenomenon of ChNCs induced by STP. Morphology results showed that the rod-like ChNCs in the non-spherical assemblies were orderly and closely arranged with periodic repetition and layered structure. The non-spherical assemblies of ChNCs liquid crystals can be used as carriers of carbon nanotubes, magnetic Fe3O4 nanoparticles, synthesized polymers, and anticancer drugs for functional composite applications. The drop impact assembly method of ChNCs liquid crystal structure is highly controllable on the composition, morphology, and function, which shows promising applications in energy, environmental-friendly, and bioactive materials.

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.

Liquid Crystal Emulsions: A Versatile Platform for Photonics, Sensing, and Active Matter

The self-assembly of liquid crystals (LCs) is a fascinating method for controlling the organization of discrete molecules into nanostructured functional materials. Although LCs are traditionally processed in thin films, their confinement within micrometre-sized droplets has recently revealed new properties and functions, paving the way for next-generation soft responsive materials. These recent findings have unlocked a wealth of unprecedented applications in photonics (e.g. reflectors, lasing materials), sensing (e.g. biomolecule and pathogen detection), soft robotics (e.g. micropumps, artificial muscles), and beyond. This Minireview focuses on recent developments in LC emulsion designs and highlights a variety of novel potential applications. Perspectives on the opportunities and new directions for implementing LC emulsions in future innovative technologies are also provided.

Advanced Functional Liquid Crystals

Liquid crystals have been intensively studied as functional materials. Recently, integration of various disciplines has led to new directions in the design of functional liquid-crystalline materials in the fields of energy, water, photonics, actuation, sensing, and biotechnology. Here, recent advances in functional liquid crystals based on polymers, supramolecular complexes, gels, colloids, and inorganic-based hybrids are reviewed, from design strategies to functionalization of these materials and interfaces. New insights into liquid crystals provided by significant progress in advanced measurements and computational simulations, which enhance new design and functionalization of liquid-crystalline materials, are also discussed.

Overview of Liquid Crystal Biosensors: From Basic Theory to Advanced Applications

Liquid crystals (LCs), as the remarkable optical materials possessing stimuli-responsive property and optical modulation property simultaneously, have been utilized to fabricate a wide variety of optical devices. Integrating the LCs and receptors together, LC biosensors aimed at detecting various biomolecules have been extensively explored. Compared with the traditional biosensing technologies, the LC biosensors are simple, visualized, and efficient. Owning to the irreplaceable superiorities, the research enthusiasm for the LC biosensors is rapidly rising. As a result, it is necessary to overview the development of the LC biosensors to guide future work. This article reviews the basic theory and advanced applications of LC biosensors. We first discuss different mesophases and geometries employed to fabricate LC biosensors, after which we introduce various detecting mechanisms involved in biomolecular detection. We then focus on diverse detection targets such as proteins, enzymes, nucleic acids, glucose, cholesterol, bile acids, and lipopolysaccharides. For each of these targets, the development history and state-of-the-art work are exhibited in detail. Finally, the current challenges and potential development directions of the LC biosensors are introduced briefly.

Nanoscale Events on Cyanobiphenyl-Based Self-Assembled Droplets Triggered by Gas Analytes

Liquid crystals (LCs) are prime examples of dynamic supramolecular soft materials. Their autonomous self-assembly at the nanoscale level and the further nanoscale events that give rise to unique stimuli-responsive properties have been exploited for sensing purposes. One of the key features to employ LCs as sensing materials derives from the fine-tuning between stability and dynamics. This challenging task was addressed in this work by studying the effect of the alkyl chain length of cyanobiphenyl LCs on the molecular self-assembled compartments organized in the presence of ionic liquid molecules and gelatin. The resulting multicompartment nematic and smectic gels were further used as volatile organic compound chemical sensors. The LC structures undergo a dynamic sequence of phase transitions, depending on the nature of the LC component, yielding a variety of optical signals, which serve as optical fingerprints. In particular, the materials incorporating smectic compartments resulted in unexpected and rich optical textures that have not been reported previously. Their sensing capability was tested in an in-house-assembled electronic nose and further assessed via signal collection and machine-learning algorithms based on support vector machines, which classified 12 different gas analytes with high accuracy scores. Our work expands the knowledge on controlling LC self-assembly to yield fast and autonomous accurate chemical-sensing systems based on the combination of complex nanoscale sensing events with artificial intelligence tools.

Controlling Liquid Crystal Configuration and Phase Using Multiple Molecular Triggers

Liquid crystals are able to transform a local molecular interaction into a macroscopic change of state, making them a valuable "smart" material. Here, we investigate a novel polymeric amphiphile as a candidate for molecular triggering of liquid crystal droplets in aqueous background. Using microscopy equipped with crossed polarizers and optical tweezers, we find that the monomeric amphiphile is able to trigger both a fast phase change and then a subsequent transition from nematic to isotropic. We next include sodium dodecyl sulfate (SDS), a standard surfactant, with the novel amphiphilic molecules to test phase transitioning when both were present. As seen previously, we find that the activity of SDS at the surface can result in configuration changes with hysteresis. We find that the presence of the polymeric amphiphile reverses the hysteresis previously observed during such transitions. This work demonstrates a variety of phase and configuration changes of liquid crystals that can be controlled by multiple exogenous chemical triggers.

Liquid Crystals: Versatile Self-Organized Smart Soft Materials

Smart soft materials are envisioned to be the building blocks of the next generation of advanced devices and digitally augmented technologies. In this context, liquid crystals (LCs) owing to their responsive and adaptive attributes could serve as promising smart soft materials. LCs played a critical role in revolutionizing the information display industry in the 20th century. However, in the turn of the 21st century, numerous beyond-display applications of LCs have been demonstrated, which elegantly exploit their controllable stimuli-responsive and adaptive characteristics. For these applications, new LC materials have been rationally designed and developed. In this Review, we present the recent developments in light driven chiral LCs, i.e., cholesteric and blue phases, LC based smart windows that control the entrance of heat and light from outdoor to the interior of buildings and built environments depending on the weather conditions, LC elastomers for bioinspired, biological, and actuator applications, LC based biosensors for detection of proteins, nucleic acids, and viruses, LC based porous membranes for the separation of ions, molecules, and microbes, living LCs, and LCs under macro- and nanoscopic confinement. The Review concludes with a summary and perspectives on the challenges and opportunities for LCs as smart soft materials. This Review is anticipated to stimulate eclectic ideas toward the implementation of the nature's delicate phase of matter in future generations of smart and augmented devices and beyond.

Quantitative volatile organic compound sensing with liquid crystal core fibers

Polymer fibers with liquid crystals (LCs) in the core have potential as autonomous sensors of airborne volatile organic compounds (VOCs), with a high surface-to-volume ratio enabling fast and sensitive response and an attractive non-woven textile form factor. We demonstrate their ability to continuously and quantitatively measure the concentration of toluene, cyclohexane, and isopropanol as representative VOCs, via the impact of each VOC on the LC birefringence. The response is fully reversible and repeatable over several cycles, the response time can be as low as seconds, and high sensitivity is achieved when the operating temperature is near the LC-isotropic transition temperature. We propose that a broad operating temperature range can be realized by combining fibers with different LC mixtures, yielding autonomous VOC sensors suitable for integration in apparel or in furniture that can compete with existing consumer-grade electronic VOC sensors in terms of sensitivity and response speed.

Applications of Microfluidics in Liquid Crystal-Based Biosensors

Liquid crystals (LCs) with stimuli-responsive configuration transition and optical anisotropic properties have attracted enormous interest in the development of simple and label-free biosensors. The combination of microfluidics and the LCs offers great advantages over traditional LC-based biosensors including small sample consumption, fast analysis and low cost. Moreover, microfluidic techniques provide a promising tool to fabricate uniform and reproducible LC-based sensing platforms. In this review, we emphasize the recent development of microfluidics in the fabrication and integration of LC-based biosensors, including LC planar sensing platforms and LC droplets. Fabrication and integration of LC-based planar platforms with microfluidics for biosensing applications are first introduced. The generation and entrapment of monodisperse LC droplets with different microfluidic structures, as well as their applications in the detection of chemical and biological species, are then summarized. Finally, the challenges and future perspectives of the development of LC-based microfluidic biosensors are proposed. This review will promote the understanding of microfluidic techniques in LC-based biosensors and facilitate the development of LC-based microfluidic biosensing devices with high performance.

Stable and Oriented Liquid Crystal Droplets Stabilized by Imidazolium Ionic Liquids

Liquid crystals represent a fascinating intermediate state of matter, with dynamic yet organized molecular features and untapped opportunities in sensing. Several works report the use of liquid crystal droplets formed by microfluidics and stabilized by surfactants such as sodium dodecyl sulfate (SDS). In this work, we explore, for the first time, the potential of surface-active ionic liquids of the imidazolium family as surfactants to generate in high yield, stable and oriented liquid crystal droplets. Our results show that [C₁₂MIM][Cl], in particular, yields stable, uniform and monodisperse droplets (diameter 74 ± 6 µm; PDI = 8%) with the liquid crystal in a radial configuration, even when compared with the standard SDS surfactant. These findings reveal an additional application for ionic liquids in the field of soft matter.

Label-Free Detection and Spectrometrically Quantitative Analysis of the Cancer Biomarker CA125 Based on Lyotropic Chromonic Liquid Crystal

Compared with thermotropic liquid crystals (LCs), the biosensing potential of lyotropic chromonic liquid crystals (LCLCs), which are more biocompatible because of their hydrophilic nature, has scarcely been investigated. In this study, the nematic phase, a mesophase shared by both thermotropic LCs and LCLCs, of disodium cromoglycate (DSCG) was employed as the sensing mesogen in the LCLC-based biosensor. The biosensing platform was constructed so that the LCLC was homogeneously aligned by the planar anchoring strength of polyimide, but was disrupted in the presence of proteins such as bovine serum albumin (BSA) or the cancer biomarker CA125 captured by the anti-CA125 antibody, with the level of disturbance (and the optical signal thus produced) predominated by the amount of the analyte. The concentration- and wavelength-dependent optical response was analyzed by transmission spectrometry in the visible light spectrum with parallel or crossed polarizers. The concentration of CA125 can be quantified with spectrometrically derived parameters in a linear calibration curve. The limit of detection for both BSA and CA125 of the LCLC-based biosensor was superior or comparable to that of thermotropic LC-based biosensing techniques. Our results provide, to the best of our knowledge, the first evidence that LCLCs can be applied in spectrometrically quantitative biosensing.

Complex Liquid Crystal Emulsions for Biosensing

Herein we describe a highly responsive optical biosensor based on dynamic complex liquid crystal (LC) emulsions. These emulsions are simple to prepare and consist of immiscible chiral nematic liquid crystals (N*) and fluorocarbon oils. In this work, we exploit the N* selective reflection to build a new sensing paradigm. Our detection strategy is based on changes in the LC/water interfacial activity of boronic acid polymeric surfactants caused by reversible interactions with IgG antibodies at the LC interface. Such biomolecular recognition events can vary the pitch length of the N* organization due to the presence of binaphthyl units in the polymeric structure, which are known to be powerful chiral dopants. We demonstrate that these interface-triggered reflection changes can be used as an effective optical read-out for the detection of the foodborne pathogen Salmonella.

Holographic metasurface gas sensors for instantaneous visual alarms

The rapid detection of biological and chemical substances in real time is particularly important for public health and environmental monitoring and in the military sector. If the process of substance detection to visual reporting can be implemented into a single miniaturized sensor, there could be a profound impact on practical applications. Here, we propose a compact sensor platform that integrates liquid crystals (LCs) and holographic metasurfaces to autonomously sense the existence of a volatile gas and provide an immediate visual holographic alarm. By combining the advantage of the rapid responses to gases realized by LCs with the compactness of holographic metasurfaces, we develop ultracompact gas sensors without additional complex instruments or machinery to report the visual information of gas detection. To prove the applicability of the compact sensors, we demonstrate a metasurface-integrated gas sensor on safety goggles via a one-step nanocasting process that is attachable to flat, curved, and flexible surfaces.

Conventional and unconventional ionic phenomena in tunable soft materials made of liquid crystals and nanoparticles

A great variety of tunable multifunctional materials can be produced by combining nanoparticles and liquid crystals. Typically, the tunability of such soft nanocomposites is achieved via external electric fields resulting in the field-induced reorientation of liquid crystals. This reorientation can be altered by ions normally present in liquid crystals in small quantities. In addition, nanomaterials dispersed in liquid crystals can also affect the behavior of ions. Therefore, an understanding of ionic phenomena in liquid crystals doped with nanoparticles is essential for future advances in liquid crystal-aided nanoscience and nanotechnology. This paper provides an overview of the ionic effects observed in liquid crystals doped with nanomaterials. An introduction to liquid crystals is followed by a brief overview of nanomaterials in liquid crystals. After giving a basic description of ions in liquid crystals and experimental methods to measure them, a wide range of ionic phenomena in liquid crystals doped with different types of nanomaterials is discussed. After that, both existing and emerging applications of tunable soft materials made of liquid crystals and nanodopants are presented with an emphasis on the role of ionic effects in such systems. Finally, the discussion of unsolved problems and future research directions completes the review.