Thomassendamsgaard6647

Lead-free double perovskites (DPs) with excellent moisture, light, and heat stability have been explored as alternatives to toxic lead halide perovskite (APbX3) (A for monovalent cation and X for Cl, Br, or I). However, the bandgaps of the current DPs are generally larger and either indirect or direct forbidden, which leads to weak visible light absorption and limitation for photovoltaic and other optoelectronic applications. Herein, we demonstrate the first synthesis of Cu2+-doped Cs2AgInCl6 double perovskite nanocrystals via a facile hot-injection solution approach. The electronic bandgap can be dramatically tuned from ∼3.60 eV (Cs2AgInCl6, parent) to ∼2.19 eV (Cu2+-doped Cs2AgInCl6) by varying the Cu2+ doping amount. We conclude that the decrease of bandgap is attributed to the overlap of the Ag-d/In-p/Cl-p orbitals and the Cu-3d orbitals in the valence band. The wide tunability of the optical and electronic properties makes Cu2+-Doped Cs2AgInCl6 DP NCs promising candidates for future optoelectronic device applications.Self-adjuvanting vaccines, wherein an antigenic peptide is covalently bound to an immunostimulating agent, have been shown to be promising tools for immunotherapy. Synthetic Toll-like receptor (TLR) ligands are ideal adjuvants for covalent linking to peptides or proteins. We here introduce a conjugation-ready TLR4 ligand, CRX-527, a potent powerful lipid A analogue, in the generation of novel conjugate-vaccine modalities. Effective chemistry has been developed for the synthesis of the conjugation-ready ligand as well as the connection of it to the peptide antigen. Different linker systems and connection modes to a model peptide were explored, and in vitro evaluation of the conjugates showed them to be powerful immune-activating agents, significantly more effective than the separate components. Mounting the CRX-527 ligand at the N-terminus of the model peptide antigen delivered a vaccine modality that proved to be potent in activation of dendritic cells, in facilitating antigen presentation, and in initiating specific CD8+ T-cell-mediated killing of antigen-loaded target cells in vivo. Synthetic TLR4 ligands thus show great promise in potentiating the conjugate vaccine platform for application in cancer vaccination.N-acetyl-d-neuraminic acid (NeuAc) has attracted considerable attention because of its wide-ranging applications. The use of cheap carbon sources such as glucose without the addition of any precursor in microbial NeuAc production has many advantages. In this study, improved NeuAc production was attained through the optimization of amino sugar metabolism pathway kinetics and reservation of a phosphoenolpyruvate (PEP) pool in Escherichia coli. N-acylglucosamine 2-epimerase and N-acetylneuraminate synthase from different sources and their best combinations were used to obtain optimized enzyme kinetics and expression intensity, which resulted in a significant increase in NeuAc production. Next, after a design was engineered for enabling the PEP metabolic pathway to retain the PEP pool, the production of NeuAc reached 16.7 g/L, which is the highest NeuAc production rate that has been reported from using glucose as the sole carbon source.The growth of uterine fibroids is sex hormone-dependent and commonly associated with highly incapacitating symptoms. Most treatment options consist of the control of these hormonal effects, ultimately blocking proliferative estrogen signaling (i.e., oral contraceptives/antagonization of human gonadotropin-releasing hormone receptor [hGnRH-R] activity). Full hGnRH-R blockade, however, results in menopausal symptoms and affects bone mineralization, thus limiting treatment duration or demanding estrogen add-back approaches. To overcome such issues, we aimed to identify novel, small-molecule hGnRH-R antagonists. This led to the discovery of compound BAY 1214784, an orally available, potent, and selective hGnRH-R antagonist. Altering the geminal dimethylindoline core of the initial hit compound to a spiroindoline system significantly improved GnRH-R antagonist potencies across several species, mandatory for a successful compound optimization in vivo. In a first-in-human study in postmenopausal women, once daily treatment with BAY 1214784 effectively lowered plasma luteinizing hormone levels by up to 49%, at the same time being associated with low pharmacokinetic variability and good tolerability.Metabotropic glutamate receptor 2 (mGlu2) is a known target for treating several central nervous system (CNS) disorders. To develop a viable positron emission tomography (PET) ligand for mGlu2, we identified new candidates 5a-i that are potent negative allosteric modulators (NAMs) of mGlu2. Among these candidates, 4-(2-fluoro-4-methoxyphenyl)-5-((1-methyl-1H-pyrazol-3-yl)methoxy)picolinamide (5i, also named as [11C]MG2-1812) exhibited high potency, high subtype selectivity, and favorable lipophilicity. Compound 5i was labeled with positron-emitting carbon-11 (11C) to obtain [11C]5i in high radiochemical yield and high molar activity by O-[11C]methylation of the phenol precursor 12 with [11C]CH3I. In vitro autoradiography with [11C]5i showed heterogeneous radioactive accumulation in the brain tissue sections, ranked in the order cortex > striatum > hippocampus > cerebellum ≫ thalamus > pons. PET study of [11C]5i indicated in vivo specific binding of mGlu2 in the rat brain. Based on the [11C]5i scaffold, further optimization for new candidates is underway to identify a more suitable ligand for imaging mGlu2.Many traditional quantitative structure-activity relationship (QSAR) models are based on correlation with high-dimensional, highly variable molecular features in their raw form, limiting their generalizing capabilities despite the use of large training sets. They also lack elements of causality and reasoning. With these issues in mind, we developed a method for learning higher-level abstract representations of the effects of the interactions between molecular features and biology. We named the representations as the reason vectors. They are composed of a series of computed activity of substructures obtained from stepwise reconstruction of the molecule. This representation is very different from fingerprints, which are composed of molecular features directly. These vectors capture reasons of bioactivity of chemicals (or absence thereof) in an abstract form, uncover causality in interactions between chemical features, and generalize beyond specific chemical classes or bioactivity. Reason vectors contain only a few key attributes and are much smaller than molecular fingerprints. They allow vague and conceptual similarity searches, less susceptible to failure on novel combinations of query molecule features and more likely to identify reasons of activity in chemical classes that are absent in training data. Reason vectors can be compared with each other and their activity can be computed by matching with vectors from molecules with known bioactivity. A single molecule produces as many reason vectors as heavy atoms in it, and a simple count of these vectors in a series of activity ranges is all what is needed to predict its bioactivity. Thus, the prediction method is devoid of gradient optimization or statistical fitting.The splitting of dinitrogen into nitride complexes emerged as a key reaction for nitrogen fixation strategies at ambient conditions. However, the impact of auxiliary ligands or accessible spin states on the thermodynamics and kinetics of N-N cleavage is yet to be examined in detail. We recently reported N-N bond splitting of a Mo(μ2η1η1-N2)Mo-complex upon protonation of the diphosphinoamide auxiliary ligands. The reactivity was associated with a low-spin to high-spin transition that was induced by the protonation reaction in the coordination periphery, mainly based on computational results. Here, this proposal is evaluated by an XAS study of a series of linearly N2 bridged Mo pincer complexes. Structural characterization of the transient protonation product by EXAFS spectroscopy confirms the proposed spin transition prior to N-N bond cleavage.Lithium-ion batteries (LIBs) are of tremendous importance for our society, but their limited lifetime still poses a great challenge. For a better understanding of battery cycling and degradation, operando analytical measurements are invaluable. In this work, we demonstrate that operando 7Li nuclear magnetic resonance (NMR) spectroscopy can be applied to full LIBs. We exemplify this on LiNi0.8Mn0.1Co0.1O2 (NMC811)/graphite cells, which are typical high-energy LIBs. Employing industry-standard electrodes, our operando cells show realistic cycling performance at practical rates, which allows us to conduct experiments at different rates and temperatures and to draw conclusions on the performance of LIBs. The NMR experiments monitor processes in both electrodes individually, including Li-ion mobility and its changes with temperature. Moreover, Li metal deposition on graphite is observed at low temperature, which is an important degradation mechanism in LIBs and a severe safety hazard. Our experiments offer unique insights into this Li metal deposition process under different charging conditions.Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe3+ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe3+ ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.The continued emergence and spread of antimicrobial resistance (AMR), particularly multidrug resistant (MDR) bacteria, are increasing threats driving the search for additional and alternative antimicrobial agents. The World Health Organization (WHO) has categorized bacterial risk levels and includes Escherichia coli among the highest priority, making this both a convenient model bacterium and a clinically highly relevant species on which to base investigations of antimicrobials. Among many compounds examined for use as antimicrobials, Ga(III) complexes have shown promise. Nonetheless, the spectrum of activities, susceptibility of bacterial species, mechanisms of antimicrobial action, and bacterial characteristics influencing antibacterial actions are far from being completely understood; these are important considerations for any implementation of an effective antibacterial agent. In this investigation, we show that an alteration in growth conditions to physiologically relevant lowered oxygen (anaerobic) conditions substantially increases the minimum inhibitory concentrations (MICs) of Ga(III) required to inhibit growth for 46 wild-type E.