Environmentally Responsive Emulsions of Thermotropic Liquid Crystals with Exceptional Long-Term Stability and Enhanced Sensitivity to Aqueous Amphiphiles

Biosensing with Oleosin-Stabilized Liquid Crystal Droplets

Liquid crystals (LCs) are emerging as novel platforms for chemical, physical, and biological sensing. They can be used to detect biological amphiphiles such as lipids, fatty acids, digestive surfactants, and bacterial endotoxins. However, designing LC-based sensors in a manner that preserves their sensitivity and responsiveness to these stimuli, and possibly improves biocompatibility, remains challenging. In this work, the stabilization of LC droplets by oleosins, plant-sourced and highly surface active proteins due to their extended amphipathic helix, is investigated. Purified oleosins, at sub-micromolar concentrations, are shown to readily stabilize nematic LC droplets without switching their alignment, allowing them to detect surfactants at micromolar concentrations. Direct evidence of localization of oleosins at the LC-water interface is provided with fluorescent labeling, and the stabilized droplets remain stable over months. Interestingly, chiral LC droplets readily switch in the presence of nanomolar oleosin concentrations, an unexpected behavior that is explained by accounting for the energy barriers required for switching the alignment between the two cases. This leads thus to a twofold conclusion: oleosin-stabilized nematic LC droplets present a biocompatible alternative for bioanalyte detection, while chiral LCs can be further investigated for use as highly sensitive sensors for detecting amphipathic helices in biological systems.

Pickering Emulsions of Thermotropic Liquid Crystals Stabilized by Amphiphilic Gold Nanoparticles

We report emulsions of thermotropic liquid crystals (LCs) in water that are stabilized using amphiphilic gold nanoparticles (AuNPs) and retain their ability to respond to aqueous analytes for extended periods (e.g., up to 1 year after preparation). These LC emulsions exhibit exceptional colloidal stability that results from the adsorption of AuNPs that are functionalized with thiol-terminated poly(ethylene glycol) (PEG-thiol) and hexadecanethiol (C16-thiol) to LC droplet interfaces. These stabilized LC emulsions respond to the presence of model anionic (SDS), cationic (C12TAB), and nonionic (C12E4) surfactants in the surrounding aqueous media, as evidenced by ordering transitions in the LC droplets that can be readily observed using polarized light microscopy. Our results reveal significant differences in the sensitivity of the stabilized LC droplets toward each of these analytes. In particular, these stabilized droplets can detect the cationic C12TAB at concentrations that are lower than those required for bare LC droplets under similar experimental conditions (0.5 and 2 mM, respectively). These results demonstrate an enhanced sensitivity of the LC toward C12TAB when the PEG/C16-thiol-coated AuNPs are adsorbed at LC droplet interfaces. In contrast, the concentrations of SDS required to observe optical transformations in the stabilized LC droplets are higher than those required for the bare LC droplets, suggesting that the presence of the PEG/C16-thiol AuNPs reduces the sensitivity of the LC toward this analyte. When combined, our results show that this Pickering stabilization approach using amphiphilic AuNPs as stabilizing agents for LC-in-water emulsions provides a promising platform for developing LC droplet-based optical sensors with long-term colloidal stability as well as opportunities to tune the sensitivity and selectivity of the response to target aqueous analytes.

Nanoparticle-Assisted Liquid Crystal Droplet Sensors Enable Analysis of Low-Concentration Species in Aqueous Medium

We introduce nanoparticle-assisted liquid crystal (LC) droplet-based sensors that allow determination of low-level concentrations of aqueous soluble species. The silica nanoparticles functionalized with mixed monolayers composed of two distinct groups, hydrophobic alkane tail- and charged group-terminated silanes, facilitated ternary physical interactions between the model analytes (methylene blue (MB) or methyl orange (MO)) and the nematic mesogens 5CB (4-cyano-4'-pentylbiphenyl), and the interfacial species of the nanoparticle. The response of the LC droplets was measured upon nanoparticle adsorption as a function of analyte concentration, which was characterized by the optical determination of the configuration distributions of the LC droplets. We highlight the importance of the charging and the composition of the nanoparticle interfaces for analytical purposes that allow accurate determination of the concentration of the analytes on the order of 0.01 ppb. Such a low concentration corresponds to a low interfacial coverage of nanoparticles, indicating the promisingly high sensitivity of the sensor platform to target analytes. Distinct from the past examples of the LC-based sensors, the nanoparticle-assisted LC sensors allow detection of the species that do not directly cause an ordering transition at the LC-water interfaces, which allow a broader range of analytical targets. The sensor platform that we report herein can be easily tunable for a range of target molecules and will find use in the determination of a wide range of micropollutants in aqueous environments.

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity

Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights into surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles.

Liquid crystal droplet design by using pseudopeptidic bottlebrush polymer additives

Liquid crystal (LC) droplets are promising candidates for sensing applications due to their high sensitivity to surface anchoring changes, resulting in readily detectable optical effects. Herein, we have designed and synthesized amino acid-based bottlebrush polymers and investigated their impact on LC director configurations in the droplets. The pseudopeptidic bottlebrush polymers with an aromatic (phenyl) and aliphatic appendages are synthesized using ring-opening metathesis polymerization (ROMP). Polymer dispersed liquid crystal (PDLC) samples are prepared by employing pseudopeptidic bottlebrush polymers and 4-cyano-4'-pentylbiphenyl (5CB) LC via solvent-induced phase separation (SIPS) technique. Due to π-π stacking, the phenyl group favours radial configuration, whereas the repulsion between 5CB and aliphatic groups induces molecular alignment leading to bipolar droplet arrangement. The impact of various pendant groups attached to the polymer on the prepared PDLC sample's surface characteristics and free energy components is illustrated. The sensing capability of 5CB dispersed in pseudopeptidic bottlebrush polymers for various pH solutions is investigated using polarizing optical microscopy (POM). The PDLC samples are moderately permeable to water and sensitive to different pH solutions. The results demonstrate a simplified and straightforward approach for preparing LC-based biosensors and chemical sensors.