Barkerwaters3385

The MnO2-based aqueous Zn cell can meet the requirements of safety, flexibility, and low cost for portable/wearable electronics; however, its low intrinsic conductivity, weak kinetics, and poor high-loading capacity restrict its practical performance. In this study, the synergistic architecture of MoS2-loaded, oxygen-defect-rich MnO2-x nanocrystals with a carbon coating (M-PM2-x-H2 aerogel) was prepared. As corevealed by various characterizations, this synergistic design not only improves the electronic/ionic conductivity but also motivates the conversion kinetics of the surficial electrochemical reaction. As a result, the M-PM2-x-H2 cathode delivers a much improved capacity of 567 mA h·g-1 at 0.1 A·g-1 and shows a high capacity retention of 176% after 150 cycles at 0.5 A·g-1. More impressively, the high areal loading (3.97 mg·cm-1) of the M-PM2-x-H2 electrode also displays a high capacity of 367 mA h·g-1 at 0.1 A·g-1. AHPN agonist chemical structure In addition, the derived all-solid-state cell exhibits excellent flexibility and safety under the conditions of weight loading, cutting, and bending.Cellular senescence, a process that arrests the cell cycle, is a cellular response mechanism for various stresses and is implicated in aging and various age-related diseases. However, the understanding of senescence in living organisms is insufficient, largely due to the scarcity of sensitive tools for the detection of cellular senescence in vivo. Herein, we describe the development of a self-immobilizing near-infrared (NIR) fluorogenic probe that can be activated by senescence-associated β-galactosidase (SA-β-Gal), the most widely used senescence marker. The NIR signal is turned on only in the presence of SA-β-Gal, and the fluorescence signal is retained to the site of activation via in situ labeling, significantly enhancing the sensitivity of the probe. We demonstrate its efficient noninvasive imaging of senescence in mice xenograft models.A rich body of literature has emerged in recent years that discusses the extraction of structured information from materials science text through named entity recognition models. Relatively little work has been done to address the "normalization" of extracted entities, that is, recognizing that two or more seemingly different entities actually refer to the same entity in reality. In this work, we address the normalization of polymer named entities, polymers being a class of materials that often have a variety of common names for the same material in addition to the IUPAC name. We have trained supervised clustering models using Word2Vec and fastText word embeddings reported in previous work so that named entities referring to the same polymer are categorized within the same cluster in the word embedding space. We report the use of parameterized cosine distance functions to cluster and normalize textually derived entities, achieving an F1 score of 0.85. Furthermore, a labeled data set of polymer names was utilized to train our model and to infer the true total number of unique polymers that are actively reported in the literature. For ∼15,500 polymer named entities extracted from our corpus of 0.5 million papers, we detected 6734 unique clusters (i.e., unique polymers), 632 of which were manually curated to train the normalization model. This work will serve as a critical ingredient in a natural language processing-based pipeline for the automatic and efficient extraction of knowledge from the polymer literature.Photoswitches are molecules that undergo a reversible, structural isomerization after exposure to certain wavelengths of light. The dynamic control offered by molecular photoswitches is favorable for materials chemistry, photopharmacology, and catalysis applications. Ideal photoswitches absorb visible light and have long-lived metastable isomers. We used high-throughput virtual screening to predict the absorption maxima (λmax) of the E-isomer and half-life (t1/2) of the Z-isomer. However, computing the photophysical and kinetic stabilities with density functional theory of each entry of a virtual molecular library containing thousands or millions of molecules is prohibitively time-consuming. We applied active search, a machine-learning technique, to intelligently search a chemical search space of 255 991 photoswitches based on 29 known azoarenes and their derivatives. We iteratively trained the active search algorithm on whether a candidate absorbed visible light (λmax > 450 nm). Active search was found to triple the discovery rate compared to random search. Further, we projected 1962 photoswitches to 2D using the Uniform Manifold Approximation and Projection algorithm and found that λmax depends on the core, which is tunable by substituents. We then incorporated a second stage of screening to predict the stabilities of the Z-isomers for the top candidates of each core. We identified four ideal photoswitches that concurrently satisfy the following criteria λmax > 450 nm and t1/2 > 2 h.These candidates had λmax and t1/2 range from 465 to 531 nm and hours to days, respectively.Herein, we report a protocol for PtI2-catalyzed formal three-component cascade cycloaddition reactions between γ-aminoalkynes and electron-deficient alkynes to afford highly substituted cyclohexadiene-b-pyrrolidines in good yields. link2 On the basis of the results of the control experiments and density functional theory calculations, we present a plausible mechanism that proceeds via two key intermediates. The overall transformation involves the cleavage and formation of multiple C-C and C-N bonds and a previously unreported reaction mode of a seven-membered nitrogen heterocyclic intermediate.CsPbI3 perovskite nanocrystals (NCs) are emerging as promising materials for optoelectronic devices because of their superior optical properties. However, the poor stability of CsPbI3 NCs has become a huge bottleneck for practical applications. Herein, we report an effective strategy of Mg2+-assisted passivation of surface defects to obtain high emission efficiency and stability in CsPbI3 NCs. It is found that the introduced Mg2+ ions are mainly distributed on the surface of NCs and then passivate the NC defects, enhancing radiative decay rate and reducing nonradiative decay rate. As a result, the as-prepared Mg2+-treated CsPbI3 (Mg-CsPbI3) NCs exhibit the highest photoluminescence quantum yield (PLQY) of 95%. The Mg-CsPbI3 NC colloidal solution retains 80% of its original PLQY after 80 days of atmosphere exposure. The red perovskite light-emitting diodes based on the Mg-CsPbI3 NCs demonstrate an external quantum efficiency of 8.4%, which shows an almost 4-fold improvement compared to the devices based on the untreated NCs.Several fluorene derivatives exhibit excited-state reactivity and relaxation dynamics that remain to be understood fully. We report here the spectral relaxation dynamics of two fluorene derivatives to evaluate the role of structural modification in the intramolecular relaxation dynamics and intermolecular interactions that characterize this family of chromophores. We have examined the time-resolved spectral relaxation dynamics of two compounds, NCy-FR0 and MK-FR0, in protic and aprotic solvents using steady-state and time-resolved emission spectroscopy and quantum chemical computations. Both compounds exhibit spectral relaxation characteristics similar to those seen in FR0, indicating that hydrogen bonding interactions between the chromophore and solvent protons play a significant role in determining the relaxation pathways available to three excited electronic states.In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. link3 However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters, tripeptides, and by de-redundancy of a sub-dataset of the ANI-1 database. We believe that the ESOINN-DP method provides a novel idea for the construction of NNPES and, especially, the reference datasets, and it can be used for molecular dynamics (MD) simulations of various gas-phase and condensed-phase chemical systems.Structural, energetic, and spectroscopic data derived in this work aim at the setup of an "experimentally validated" database for amino acids and polypeptides conformers. First, the "cheap" composite scheme (ChS, CCSD(T)/(CBS+CV)MP2) is tested for evaluation of conformational energies of all eight stable conformers of glycine, by comparing to the more accurate CCSD(T)/CBS+CV computations (Phys. Chem. Chem. Phys. 2013, 15, 10094-10111 and J Mol. Model. 2020, 26, 129). The recently proposed jun-ChS (J. Chem. Theory and Comput. 2020, 16, 988-1006), employing the jun-cc-pVnZ basis set family for CCSD(T) computations and CBS extrapolation, yields conformational energies accurate to 0.2 kJ·mol-1, at reduced computational cost with respect to aug-ChS employing aug-cc-pVnZ basis sets. The jun-ChS composite scheme is further applied to derive conformational energies for three dipeptide analogues Ac-Gly-NH2, Ac-Ala-NH2, and Gly-Gly. Finally, dipeptide conformational energies and semiexperimental equilibrium rotational constants along with the CCSD(T)/(CBS+CV)MP2 structural parameters (J. Phys. Chem. Lett. 2014, 5, 534-540) stand as the reference for benchmarking of selected density functional methodologies. The double-hybrid functionals B2-PLYP-D3(BJ) and DSD-PBEP86, perform best for structural and energetic characterization of all dipeptide analogues. From hybrid functionals CAM-B3LYP-D3(BJ) and ωB97X-D3(BJ) represent promising methods applicable for larger peptide-based systems for which computations with double-hybrid functionals are not feasible.With the aim of constructing efficient photoelectric organic materials, a pyrido[3,2-g]quinoline derivative named LA17b has been synthesized, and its photodynamic relaxation processes in solvents and films were studied by time-resolved fluorescence and femtosecond transient absorption techniques. The steady-state fluorescence spectra show pronounced red-shift with the increase of the solvent polarity as well as in binary solvent hexane/ethanol by increasing ethanol concentration. However, the strong red-shift does not lead to quenching of the fluorescence. This is explained in terms of a twisted intramolecular charge transfer (TICT) state. The TICT state of LA17b in ethanol is highly emissive with a long fluorescence lifetime 1.1 ns. TICT state was shown to play an important role in enhancement of intersystem crossing rate. TD-DFT calculations confirm the pathways of relaxation of locally excited state via TICT and triplet states. In films, the photodynamic properties are similar to that of LA17b in hexane and the TICT state vanishes due to the rigid environment.