Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks

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.

Effect of Polymer Hydrophobicity in the Performance of Hybrid Gel Gas Sensors for E-Noses

Relative humidity (RH) is a common interferent in chemical gas sensors, influencing their baselines and sensitivity, which can limit the performance of e-nose systems. Tuning the composition of the sensing materials is a possible strategy to control the impact of RH in gas sensors. Hybrid gel materials used as gas sensors contain self-assembled droplets of ionic liquid and liquid crystal molecules encapsulated in a polymeric matrix. In this work, we assessed the effect of the matrix hydrophobic properties in the performance of hybrid gel materials for VOC sensing in humid conditions (50% RH). We used two different polymers, the hydrophobic PDMS and the hydrophilic bovine gelatin, as polymeric matrices in hybrid gel materials containing imidazolium-based ionic liquids, [BMIM][Cl] and [BMIM][DCA], and the thermotropic liquid crystal 5CB. Better accuracy of VOC prediction is obtained for the hybrid gels composed of a PDMS matrix combined with the [BMIM][Cl] ionic liquid, and the use of this hydrophobic matrix reduces the effect of humidity on the sensing performance when compared to the gelatin counterpart. VOCs interact with all the moieties of the hybrid gel multicomponent system; thus, VOC correct classification depends not only on the polymeric matrix used, but also on the IL selected, which seems to be key to achieve VOCs discrimination at 50% RH. Thus, hybrid gels' tunable formulation offers the potential for designing complementary sensors for e-nose systems operable under different RH conditions.

Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks

We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when performed using a machine learning methodology, can advance the sensing of gas mixtures and provide important insights into the physical processes that underlie the sensor response. We develop the methodology using O₃ and Cl₂ mixtures (representative of an important class of analytes) and LCs supported on metal perchlorate-decorated surfaces as a model system. Although O₃ and Cl₂ both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar initial and final optical states of the LCs, we show that a three-dimensional convolutional neural network can extract feature information that is encoded in the spatiotemporal color patterns of the LCs to detect the presence of both O₃ and Cl₂ species in mixtures and to quantify their concentrations. Our analysis reveals that O₃ detection is driven by the transition time over which the brightness of the LC changes, while Cl₂ detection is driven by color fluctuations that develop late in the optical response of the LC. We also show that we can detect the presence of Cl₂ even when the concentration of O₃ is orders of magnitude greater than the Cl₂ concentration. The proposed methodology is generalizable to a wide range of analytes, reactive surfaces, and LCs and has the potential to advance the design of portable LC monitoring devices (e.g., wearable devices) for analyzing gas mixtures using spatiotemporal color fluctuations.

Liquid Crystal Droplet-Based Biosensors: Promising for Point-of-Care Testing

The development of biosensing platforms has been impressively accelerated by advancements in liquid crystal (LC) technology. High response rate, easy operation, and good stability of the LC droplet-based biosensors are all benefits of the long-range order of LC molecules. Bioprobes emerged when LC droplets were combined with biotechnology, and these bioprobes are used extensively for disease diagnosis, food safety, and environmental monitoring. The LC droplet biosensors have high sensitivity and excellent selectivity, making them an attractive tool for the label-free, economical, and real-time detection of different targets. Portable devices work well as the accessory kits for LC droplet-based biosensors to make them easier to use by anyone for on-site monitoring of targets. Herein, we offer a review of the latest developments in the design of LC droplet-based biosensors for qualitative target monitoring and quantitative target analysis.

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

As an emerging stimuli-responsive material, liquid crystal (LC) has attracted great attentions beyond display applications, especially in the area of biochemical sensors. Its high sensitivity and fast response to various biological or chemical analytes make it possible to fabricate a simple, real-time, label-free, and cost-effective LC-based detection platform. Advancements have been achieved in the development of LC-based sensors, both in fundamental research and practical applications. This paper briefly reviews the state-of-the-art research on LC sensors in the biochemical field, from basic properties of LC material to the detection mechanisms of LC sensors that are categorized into LC-solid, LC-aqueous, and LC droplet platforms. In addition, various analytes detected by LCs are presented as a proof of the application value, including metal ions, nucleic acids, proteins, glucose, and some toxic chemical substances. Furthermore, a machine-learning-assisted LC sensing platform is realized to provide a foundation for device intelligence and automatization. It is believed that a portable, convenient, and user-friendly LC-based biochemical sensing device will be achieved in the future.

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.