Search for: All records

Liquid crystals (LCs), when supported on reactive surfaces, undergo changes in ordering that can propagate over distances of micrometers, thus providing a general and facile mechanism to amplify atomic-scale transformations on surfaces into the optical scale. While reactions on organic and metal substrates have been coupled to LC ordering transitions, metal oxide substrates, which offer unique catalytic activities for reactions involving atmospherically important chemical species such as oxidized sulfur species, have not been explored. Here we investigate this opportunity by designing LCs that contain 4′-cyanobiphenyl-4-carboxylic acid (CBCA) and respond to surface reactions triggered by parts-per-billion concentrations of SO2 gas on anatase (101) substrates. We used electronic structure calculations to predict that the carboxylic acid group of CBCA binds strongly to anatase (101) in a perpendicular orientation, a prediction that we validated in experiments in which CBCA (0.005 mol%) was doped into a LC (4’-n-pentyl-4-biphenylcarbonitrile). Both experiment and computational modeling further demonstrated that SO3-like species, produced by a surface-catalyzed reaction of SO2 with H2O on anatase (101), displace CBCA from the anatase surface, resulting in an orientational transition of the LC. Experiments also reveal the LC response to be highly selective to SO2 over other atmospheric chemical species (including H2O, NH3, H2S, and NO2), in agreement with our computational predictions for anatase (101) surfaces. Overall, we establish that the catalytic activities of metal oxide surfaces offer the basis of a new class of substrates that trigger LCs to undergo ordering transitions in response to chemical species of relevance to atmospheric chemistry. 

We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when per-formed 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. Whereas O3 and Cl2¬ both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar ini-tial and final optical states of the LCs, we show that a 3-dimensional convolutional neural network (3D CNN) 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 as well as 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 us-ing spatiotemporal color fluctuations. 

Soft matter that undergoes programmed macroscopic responses to molecular analytes has potential utility in a range of health and safety-related contexts. In this study, we report the design of a nematic liquid crystal (LC) composition that forms through dimerization of carboxylic acids and responds to the presence of vapors of organoamines by undergoing a visually distinct phase transition to an isotropic phase. Specifically, we screened mixtures of two carboxylic acids, 4-butylbenzoic acid and trans-4-pentylcyclohexanecarboxylic acid, and found select compositions that exhibited a nematic phase from 30.6 to 111.7 °C during heating and 110.6 to 3.1 °C during cooling. The metastable nematic phase formed at ambient temperatures was found to be long-lived (>5 days), thus enabling the use of the LC as a chemoresponsive optical material. By comparing experimental infrared (IR) spectra of the LC phase with vibrational frequencies calculated using density functional theory (DFT), we show that it is possible to distinguish between the presence of monomers, homodimers and heterodimers in the mixture, leading us to conclude that a one-to-one heterodimer is the dominant species within this LC composition. Further support for this conclusion is obtained by using differential scanning calorimetry. Exposure of the LC to 12 ppm triethylamine (TEA) triggers a phase transition to an isotropic phase, which we show by IR spectroscopy to be driven by an acid-base reaction, leading to the formation of ammonium carboxylate salts. We characterized the dynamics of the phase transition and found that it proceeds via a characteristic spatiotemporal pathway involving the nucleation, growth, and coalescence of isotropic domains, thus amplifying the atomic-scale acid-base reaction into an information-rich optical output. In contrast to TEA, we determined via both experiment and computation that neither hydrogen bonding donor or acceptor molecules, such as water, dimethyl methylphosphonate, ethylene oxide or formaldehyde, disrupt the heterodimers formed in the LC, hinting that the phase transition (including spatial-temporal characteristics of the pathway) induced in this class of hydrogen bonded LC may offer the basis of a facile and chemically selective way of reporting the presence of volatile amines. This proposal is supported by exploratory experiments in which we show that it is possible to trigger a phase transition in the LC by exposure to volatile amines emitted from rotting fish. Overall, these results provide new principles for the design of chemoresponsive soft matter based on hydrogen bonded LCs that may find use as the basis of low-cost visual indicators of chemical environments. 

We provide an in-depth convolutional neural network (CNN) analysis of optical responses of liquid crystals (LCs) when exposed to different chemical environments. Our aim is to identify informative features that can be used to construct automated LC-based chemical sensors and shed some light on the underlying phenomenon that governs and distinguishes LC responses. Previous work demonstrated that, by using features extracted from AlexNet, grayscale micrographs of different LC responses can be classified with an accuracy of 99%. Reaching such high levels of accuracy, however, required the use of a large number of features (on the order of thousands), which was computationally intensive and clouded the physical interpretability of the dominant features. To address these issues, here we report a study on the effectiveness of using features extracted from color micrographs using VGG16, which is a more compact CNN than Alexnet. Our analysis reveals that features extracted from the first and second convolutional layers of VGG16 are sufficient to achieve a perfect classification accuracy while reducing the number of features to less than 100. The number of features is further reduced to 10 via recursive elimination with a minimal loss in classification accuracy (5–10%). This reduction procedure reveals that differences in spatial color patterns are developed within seconds in the LC response. From this, we conclude that hue distributions provide an informative set of features that can be used to characterize LC sensor responses. We also hypothesize that differences in the spatial correlation length of LC textures detected by VGG16 with DMMP and water likely reflect differences in the anchoring energy of the LC on the surface of the sensor. Our results hint at fresh approaches for the design of LC-based sensors based on the characterization of spontaneous fluctuations in the orientation (as opposed to changes in time-average orientations reported in the literature).