Bending nematic liquid crystal membranes with phospholipids

Dissipative Particle Dynamics Simulation of the Sensitive Anchoring Behavior of Smectic Liquid Crystals at Aqueous Phase

Rational design of thermotropic liquid crystal (LC)-based sensors utilizing different mesophases holds great promise to open up novel detection modalities for various chemical and biological applications. In this context, we present a dissipative particle dynamics study to explore the unique anchoring behavior of nematic and smectic LCs at amphiphile-laden aqueous-LC interface. By increasing the surface coverage of amphiphiles, two distinct anchoring sequences, a continuous planar-tilted-homeotropic transition and a discontinuous planar-to-homeotropic transition, can be observed for the nematic and smectic LCs, respectively. More importantly, the latter occurs at a much lower surface coverage of amphiphiles, demonstrating an outstanding sensitivity for the smectic-based sensors. The dynamics of reorientation further reveals that the formation of homeotropic smectic anchoring is mainly governed by the synchronous growth of smectic layers through the LCs, which is significantly different from the mechanism of interface-to-bulk ordering propagation in nematic anchoring. Furthermore, the smectic LCs have also been proven to possess a potential selectivity in response to a subtle change in the chain rigidity of amphiphiles. These simulation findings are promising and would be valuable for the development of novel smectic-based sensors.

Chiral Liquid Crystal Microdroplets for Sensing Phospholipid Amphiphiles

Designing simple, sensitive, fast, and inexpensive readout devices to detect biological molecules and biomarkers is crucial for early diagnosis and treatments. Here, we have studied the interaction of the chiral liquid crystal (CLC) and biomolecules at the liquid crystal (LC)-droplet interface. CLC droplets with high and low chirality were prepared using a microfluidic device. We explored the reconfiguration of the CLC molecules confined in droplets in the presence of 1,2-diauroyl-sn-glycero3-phosphatidylcholine (DLPC) phospholipid. Cross-polarized optical microscopy and spectrometry techniques were employed to monitor the effect of droplet size and DLPC concentration on the structural reorganization of the CLC molecules. Our results showed that in the presence of DLPC, the chiral LC droplets transition from planar to homeotropic ordering through a multistage molecular reorientation. However, this reconfiguration process in the low-chirality droplets happened three times faster than in high-chirality ones. Applying spectrometry and image analysis, we found that the change in the chiral droplets’ Bragg reflection can be correlated with the CLC–DLPC interactions.

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.

Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning

Rapid, robust virus-detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the SARS ssRNA, with a 3-bp mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we design a LC-based diagnostic kit and a smartphone-based application (app) to enable automatic detection of SARS-CoV-2 ssRNA, which could be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.

Ionic-surfactants-based thermotropic liquid crystals

Ionic surfactants can be combined with various functional groups through electrostatic interaction, resulting in a series of thermotropic liquid crystals (TLCs).