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Motivated by the growing recognition of citrate as a central metabolite in a variety of biological processes associated with healthy and diseased cellular states, we have developed a series of high-performance genetically encoded citrate biosensors suitable for imaging of citrate concentrations in mammalian cells. The design of these biosensors was guided by structural studies of the citrate-responsive sensor histidine kinase and took advantage of the same conformational changes proposed to propagate from the binding domain to the catalytic domain. Following extensive engineering based on a combination of structure guided mutagenesis and directed evolution, we produced an inverse-response biosensor (ΔF/Fmin ≈ 18) designated Citroff1 and a direct-response biosensor (ΔF/Fmin ≈ 9) designated Citron1. We report the X-ray crystal structure of Citron1 and demonstrate the utility of both biosensors for qualitative and quantitative imaging of steady-state and pharmacologically perturbed citrate concentrations in live cells.Single-atom catalysts (SACs) have attracted widespread interest for many catalytic applications because of their distinguishing properties. However, general and scalable synthesis of efficient SACs remains significantly challenging, which limits their applications. Here we report an efficient and universal approach to fabricating a series of high-content metal atoms anchored into hollow nitrogen-doped graphene frameworks (M-N-Grs; M represents Fe, Co, Ni, Cu, etc.) at gram-scale. The highly compatible doped ZnO templates, acting as the dispersants of targeted metal heteroatoms, can react with the incoming gaseous organic ligands to form doped metal-organic framework thin shells, whose composition determines the heteroatom species and contents in M-N-Grs. We achieved over 1.2 atom % (5.85 wt %) metal loading content, superior oxygen reduction activity over commercial Pt/C catalyst, and a very high diffusion-limiting current (6.82 mA cm-2). Both experimental analyses and theoretical calculations reveal the oxygen reduction activity sequence of M-N-Grs. Additionally, the superior performance in Fe-N-Gr is mainly attributed to its unique electron structure, rich exposed active sites, and robust hollow framework. This synthesis strategy will stimulate the rapid development of SACs for diverse energy-related fields.Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. Buloxibutid molecular weight However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos' high noisiness and spatial heterogeneity. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. We apply this framework to three typical systems of colloidal nanoparticles, concerning their diffusion and interaction, reaction kinetics, and assembly dynamics, all resolved in real-time and real-space by liquid-phase TEM. A diversity of properties for differently shaped anisotropic nanoparticles are mapped, including the anisotropic interaction landscape of nanoprisms, curvature-dependent and staged etching profiles of nanorods, and an unexpected kinetic law of first-order chaining assembly of concave nanocubes. These systems representing properties at the nanoscale are otherwise experimentally inaccessible. Compared to the prevalent image segmentation methods, U-Net shows a superior capability to predict the position and shape boundary of nanoparticles from highly noisy and fluctuating background-a challenge common and sometimes inevitable in liquid-phase TEM videos. We expect our framework to push the potency of liquid-phase TEM to its full quantitative level and to shed insights, in high-throughput and statistically significant fashion, on the nanoscale dynamics of synthetic and biological nanomaterials.The constant demand for novel functional materials calls for efficient strategies to accelerate the materials discovery, and crystal structure prediction is one of the most fundamental tasks along that direction. In addressing this challenge, generative models can offer new opportunities since they allow for the continuous navigation of chemical space via latent spaces. In this work, we employ a crystal representation that is inversion-free based on unit cell and fractional atomic coordinates and build a generative adversarial network for crystal structures. The proposed model is applied to generate the Mg-Mn-O ternary materials with the theoretical evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The proposed generative HTVS framework predicts 23 new crystal structures with reasonable calculated stability and band gap. These findings suggest that the generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.Supramolecular polymers are known to form strong and resilient hydrogels which can take up large amounts of water while exhibiting ease of processing and self-healing. They also possess similarities with networks of biological macromolecules. The combination of these features makes supramolecular polymers ideal candidates for studying mechanisms and consequences of self-assembly, which are relevant to biological materials. At the same time, this renders investigations of mixed hydrogels based on different supramolecular compounds necessary, since this substantially widens their applicability. Here, we address unusual viscoelastic properties of a class of binary hydrogels made by mixing fibrillar supramolecular polymers that are formed from two compounds 1,3,5-benzene-tricarboxamide decorated with aliphatic chains terminated by tetra(ethylene glycol) (BTA) and a 20 kg/mol telechelic poly(ethylene glycol) decorated with the same hydrogen bonding BTA motif on both ends (BTA-PEG-BTA). Using a suite of experimental and simulation techniques, we find that the respective single-compound-based supramolecular systems form very different networks which exhibit drastically different rheology.
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