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Chiroptical properties of pure inorganic material have been achieved by dispersing small amounts of achiral Ag nanoparticles into highly ordered chiral mesoporous silica. There are three types of chirality in chiral mesoporous silica: i) the helical hexagonal surface, ii) the helical pore orientation, and iii) the helical arrangement of aminopropyl groups on the surface of the mesopores, all of which impart plasmonic circular dichroism and have been investigated by introducing Ag nanoparticles into the as‐made, calcined and extracted chiral mesoporous silica, respectively. The three types of optical response originate from asymmetric plasmon‐plasmon interactions of achiral Ag nanoparticles in three types of chiral environments. Among the three sources of chirality, the helical pore orientation was considered to be predominantly responsible for the optical response owing to the high efficiency of nanoscale chirality. Interestingly, large Ag nanoparticles aggregation as a result of calcination still resulted in a strong optical activity, even the chiral mesostructure was destroyed completely. Rather than the pitch length, the length of helical channel was more effective for increasing the intensity of plasmonic circular dichroism due to longitudinal propagation of Ag nanoparticles along helical channel. Such novel chiral inorganic material will bring new opportunities in non‐linear optics, biosensors and chiral recognition.

Long-term, real-time, and comfortable epidermal electronics are of great practical importance for healthcare monitoring and human–machine interaction. However, traditional physiological signal monitoring confined by the specific clinical sites and unreliability of the epidermal electrodes leads to great restrictions on its application. Herein, we constructed a solution-processed submicron (down to 230 nm), free-standing, breathable sandwich-structured hybrid electrode composed of a silver nanowire network with a conductive polymer film, which is conformal, water-permeable, and noninvasive to the skin while achieving good signal acquisition ability. The free-standing hybrid electrode is prepared via an in situ capillary force lift-off process and can be transferred onto complex surfaces. The whole process is a complete solution process that facilitates large-area preparation and application. The light-weight hybrid electrodes exhibit high optical transmittance, high electrical conductivity, and high gas/ion permeability. When the hybrid electrodes are attached onto the skin, the imperceptible films show high conformality with low electrical impedance, thus exhibiting significantly improved electrocardiology and electromyogram signal monitoring performance compared to that of the commercial gel electrodes.

Determining the three-dimensional (3D) structures of ribonucleic acid (RNA)−small molecule ligand complexes is critical to understanding molecular recognition in RNA. Computer docking can, in principle, be used to predict the 3D structure of RNA−small molecule complexes. Unfortunately, retrospective analysis has shown that the scoring functions that are typically used for pose prediction tend to misclassify nonnative poses as native and vice versa. Here, we use machine learning to train a set of pose classifiers that estimate the relative "nativeness" of a set of RNA− ligand poses. At the heart of our approach is the use of a pose "fingerprint" (FP) that is a composite of a set of atomic FPs, which individually encode the local "RNA environment" around ligand atoms. We found that by ranking poses based on classification scores from our machine learning classifiers, we were able to recover native-like poses better than when we ranked poses based on their docking scores. With a leave-one-out training and testing approach, we found that one of our classifiers could recover poses that were within 2.5 Å of the native poses in ∼80% of the 80 cases we examined, and, on two separate validation sets, we could recover such poses in ∼60% of the cases. Our set of classifiers, which we refer to as RNAPosers, should find utility as a tool to aid in RNA−ligand pose prediction, and so we make RNAPosers open to the academic community via https://github.com/atfrank/RNAPosers.

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