A liquid crystal-based passive badge for personal monitoring of exposure to hydrogen sulfide

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.

Binding of Organophosphorus Nerve Agents and Their Simulants to Metal Salts

Nerve agents (NAs) pose a great threat to society because they are easy to produce and are deadly in nature, which makes developing methods to detect, adsorb, and destroy them crucial. To enable the development of these methods, we report the use of first principles electronic structure calculations to understand the binding properties of NAs and NA simulants on metal salt surfaces. We report calculated Gibbs free binding energies (G_BE) for four NAs (tabun (GA), sarin (GB), soman (GD), and venomous X (VX)) and five NA simulants (dimethyl methylphosphonate (DMMP), dimethyl chlorophosphate (DMCP), trimethyl phosphate (TMP), methyl dichlorophosphate (MDCP), and di-isopropyl methylphosphonate (DIMP)) on metal perchlorate and metal nitrate salts using density functional theory. Our results indicate a general trend in the binding strength of NAs and NA simulants to metal salt surfaces: MDCP < DMCP < GA < GD ≈ GB < TMP < VX ≈ DMMP < DIMP. Based on their binding properties on salt surfaces, we identify the most effective simulant for each of the studied NAs as follows: DMCP for GA, TMP for GB and GD, and DMMP for VX. To illustrate the utility of the binding energies calculated in our study, we address the design of NA sensors based on the competitive binding of NAs and liquid crystalline compounds on metal salts. We compare our results with previous experimental findings and provide a list of promising combinations of liquid crystal and metal salt systems to selectively and sensitively detect NAs. Our study highlights the great value of computational chemistry for designing selective and sensitive NA sensors while minimizing the number of very dangerous experiments involving NAs.

Seeing the Unseen: The Role of Liquid Crystals in Gas-Sensing Technologies

Fast, real-time detection of gases and volatile organic compounds (VOCs) is an emerging research field relevant to most aspects of modern society, from households to health facilities, industrial units, and military environments. Sensor features such as high sensitivity, selectivity, fast response, and low energy consumption are essential. Liquid crystal (LC)-based sensors fulfill these requirements due to their chemical diversity, inherent self-assembly potential, and reversible molecular order, resulting in tunable stimuliresponsive soft materials. Sensing platforms utilizing thermotropic uniaxial systems-nematic and smectic-that exploit not only interfacial phenomena, but also changes in the LC bulk, are demonstrated. Special focus is given to the different interaction mechanisms and tuned selectivity toward gas and VOC analytes. Furthermore, the different experimental methods used to transduce the presence of chemical analytes into macroscopic signals are discussed and detailed examples are provided. Future perspectives and trends in the field, in particular the opportunities for LC-based advanced materials in artificial olfaction, are also discussed.

Machine Learning Algorithms for Liquid Crystal-Based Sensors

We present a machine learning (ML) framework to optimize the specificity and speed of liquid crystal (LC)-based chemical sensors. Specifically, we demonstrate that ML techniques can uncover valuable feature information from surface-driven LC orientational transitions triggered by the presence of different gas-phase analytes (and the corresponding optical responses) and can exploit such feature information to train accurate and automatic classifiers. We demonstrate the utility of the framework by designing an experimental LC system that exhibits similar optical responses to a stream of nitrogen containing either 10 ppmv dimethyl-methylphosphonate (DMMP) or 30% relative humidity (RH). The ML framework is used to process and classify thousands of images (optical micrographs) collected during the LC responses and we show that classification (sensing) accuracies of over 99% can be achieved. For the same experimental system, we demonstrate that traditional feature information used in characterizing LC responses (such as average brightness) can only achieve sensing accuracies of 60%. We also find that high accuracies can be achieved by using time snapshots collected early in the LC response, thus providing the ability to create fast sensors. We also show that the ML framework can be used to systematically analyze the quality of information embedded in LC responses and to filter out noise that arises from imperfect LC designs and from sample variations. We evaluate a range of classifiers and feature extraction methods and conclude that linear support vector machines are preferred and that high accuracies can only be achieved by simultaneously exploiting multiple sources of feature information.

Liquid Crystals with Interfacial Ordering that Enhances Responsiveness to Chemical Targets

The development of stimuli-responsive materials suitable for use in wearable sensors is a key unresolved challenge. Liquid crystals (LCs) are particularly promising, as they do not require power, are light-weight, and can be tuned to respond to a range of targeted chemical stimuli. Here, an advance is reported in the design of LCs for chemical sensors with the discovery of LCs that assume parallel orientations at free surfaces and yet retain their chemoresponsiveness. The resulting LC-based sensors are more sensitive and exhibit faster responses than previous LC sensor designs.

Surface-Controlled Orientational Transitions in Elastically Strained Films of Liquid Crystal That Are Triggered by Vapors of Toluene

We report the fabrication of chemically patterned microwells that enable the rapid and facile preparation (by spin coating and patterned dewetting) of thin films of liquid crystals (LCs) that have precise thicknesses (0.7-30 μm), are supported on chemically defined substrates, and have free upper surfaces. We use these microwells to prepare elastically strained nematic LC films supported on silica glass, gold, or polystyrene substrates and thereby characterize the response of the strained LC films to vapors of toluene. We report that low concentrations of toluene vapor (<500 ppm) can partition into the LC to lower the anchoring energy of the LC on these substrates, thus allowing the elastic energy of the strained LC film to drive the LC films through an orientational transition. The central role of the toluene-induced change in surface anchoring energy is supported by additional experiments in which the response of the nematic LC to changes in film thickness and substrate identity are quantified. A simple thermodynamic model captures these trends and yielded estimates of anchoring energies (8-22 μJ/m(2)). Significantly, the orientational transitions observed in these strained LC thin films occur at concentrations of toluene vapor that are almost 1 order of magnitude below those which lead to bulk phase transitions, and they are not triggered by exposure to water vapor. Overall, these results hint at principles for the design of responsive LC-based materials that can be triggered by concentrations of aromatic, volatile organic compounds that are relevant to human health.