The liquid crystalline state of matter arises from orientation-dependent, non-covalent interaction between molecules within condensed phases. Because the balance of intermolecular forces that underlies formation of liquid crystals is delicate, this state of matter can, in general, be easily perturbed by external stimuli (such as an electric field in a display). In this review, we present an overview of recent efforts that have focused on exploiting the responsiveness of liquid crystals as the basis of chemical and biological sensors. In this application of liquid crystals, the challenge is to design liquid crystalline systems that undergo changes in organization when perturbed by targeted chemical and biological species of interest. The approaches described below revolve around the design of interfaces that selectively bind targeted species, thus leading to surface-driven changes in the organization of the liquid crystals. Because liquid crystals possess anisotropic optical and dielectric properties, a range of different methods can be used to read out the changes in organization of liquid crystals that are caused by targeted chemical and biological species. This review focuses on principles for liquid crystal-based sensors that provide an optical output.

Hydrogen is an ideal alternative energy carrier to generate power for all of society's energy demands including grid, industrial, and transportation sectors. Among the hydrogen production methods, water electrolysis is a promising method because of its zero greenhouse gas emission and its compatibility with all types of electricity sources. Alkaline electrolyzers (AELs) and proton exchange membrane electrolyzers (PEMELs) are currently used to produce hydrogen. AELs are commercially mature and are used in a variety of industrial applications, while PEMELs are still being developed and find limited application. In comparison with AELs, PEMELs have more compact structure and can achieve higher current densities. Recently, however, an alternative technology to PEMELs, hydroxide exchange membrane electrolyzers (HEMELs), has gained considerable attention due to the possibility to use platinum group metal (PGM)‐free electrocatalysts and cheaper membranes, ionomers, and construction materials and its potential to achieve performance parity with PEMELs. Here, the state‐of‐the‐art AELs and PEMELs along with the current status of HEMELs are discussed in terms of their positive and negative aspects. Additionally discussed are electrocatalyst, membrane, and ionomer development needs for HEMELs and benchmark electrocatalysts in terms of the cost–performance tradeoff.

In this study, a new group contribution-based model is presented for the prediction of the upper flash point temperature of pure compounds based on a large data set containing 1294 pure compounds. The model is a neural network using a number of occurrences of 122 chemical groups in a pure compound to predict its related UFLT (Upper Flash Point Limit). The squared correlation coefficient, average percent error, mean average error, and root-mean-square error of the model over the main data set containing 1294 pure compounds are 0.99, 1.7%, 6, and 8.5, respectively.

In this work, a new model is presented for estimation of Henry’s law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using log H based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models.

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