The work deduces orientational order parameters in the nematic and twist-bend nematic phases. An homologous series of dimers is studied using Polarized Raman Spectroscopy.

Due to high volatility and water solubility, 2,4-dichlorophenoxyacetic acid (2,4-D) can easily enter into the atmosphere and water bodies by volatilization, drift, leaching, or runoff, which results in potential threats to the environment and human health. The physicochemical properties of pesticides can be regulated by preparing their ionic liquids. In this work, a series of dicationic ionic liquids (DILs) of 2,4-D were prepared to reduce its environmental risk and enhance herbicidal activity. The solubility, octanol-water partition coefficient, surface tension, and volatilization rate results of DILs showed that these properties could be optimized by choosing appropriate countercations. Compared to 2,4-D ammonium salt, DILs have lower volatility, water solubility, and surface tension as well as higher lipophilicity. Benefiting from optimized physicochemical properties, DILs HIL8-12 exhibited better herbicidal activity against three typical broadleaf weeds than 2,4-D ammonium salt, and their fresh weight inhibition rates increased by 2.74-46.84%. The safety assessment experiment indicated that DILs were safer to wheat than commercialized forms of 2,4-D. The DILs could reduce the environmental risk of 2,4-D caused by high volatility and water solubility and would be potential alternatives to its commercialized formulations.

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Background: Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated. </P><P> Objective: We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors. </P><P> Results: While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types. </P><P> Conclusion: Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.

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