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Localization of CuO, NiO, and Co3O4 sintering additives in dense proton-conducting perovskite ceramics based on BaSnO3
No full textSintering additives have been widely employed to achieve good sinterability of barium-based proton-conducting perovskites (based on BaZrO3, BaCeO3, BaTiO3, BaHfO3, BaThO3, and BaSnO3). This is of particular importance for the fabrication of multilayered ceramic cells, in which the thin-film electrolyte layer can be primarily densified at relatively low sintering temperatures (1350–1500 °C). The introduction of sintering additives facilitates the fabrication of gas-tight ceramics; however, the precise nature of their localization and their effects on the functional properties remain uncertain and even questionable. In this study, we present a comprehensive characterization of ceramic materials based on Y-doped BaSnO3 prepared with the addition of three sintering additives (copper, cobalt, and nickel oxides) at 1 wt%. Although these introduced oxides belong to a group of compounds with similar physicochemical properties, each additive exerts a distinct influence on the microstructural and electrochemical properties of the ceramics owing to their own chemical localization features. These features are discussed in detail in the present work, providing useful information in the field of using sintering additives for the preparation of oxide ceramics for high-temperature applications
Borylative Desymmetrization of multifunctional haloarenes assisted by sodium dispersion
No full textMultiply halogenated aromatic compounds were selectively borylated by a boron alkoxide in the presence of sodium dispersion when the reaction was carried out at a low temperature, while multi-functionalization took place at an elevated temperature. The reaction of 1,4-dichlorobenzene with sodium dispersion (200–1200 mol%) in the presence of isopropyloxyboron pinacolate (120–240 mol%) afforded (4-chlorophenyl)boron pinacolate in up to 84% yield. Formation of diborylated product hardly accompanied under the reaction conditions at –78 °C for 1 h
SYNERGY OF ADVANCED MACHINE LEARNING AND DEEP NEURAL NETWORKS WITH CONSENSUS MOLECULAR DOCKING FOR ENHANCED POTENCY PREDICTION OF ALK INHIBITORS
No full textThis study addresses the urgent need for novel Anaplastic lymphoma kinase (ALK) inhibitors in Non-Small Cell Lung Cancer (NSCLC) treatment, focusing on the ALK-positive mutation variant (5% of the cases). As only five Food and Drug Administration (FDA)-approved ALK inhibitors are on the market, the demand for effective drugs persists. Leveraging the power of Artificial Intelligence (AI) including machine learning (ML), and deep learning, our research aimed to expedite the screening of novel ALK inhibitors. Notably, the machine learning-based XGBoost algorithm exhibited compelling results with an external validation (EV)-f1 score of 0.921, and an EV-Average Precision (AP) of 0.961, alongside a cross-validation (CV)-f1 score of 0.888±0.039 and a CV-AP of 0.939±0.032. Besides, the deep learning-based Artificial Neural Network (ANN) model demonstrated excellent performance with an EV-f1 score of 0.930 and an EV-AP of 0.955, complemented by a CV-f1 score of 0.891±0.037 and a CV-AP of 0.934±0.040. The present study undertook a comparative analysis between the traditional ML models, the ANN model, and the Graph Neural Network (GNN) model, which is a product of our recent research endeavors. The findings reveal that, despite the advancements in neural network models, traditional machine learning models exhibited superior performance over the GNN model. During this research, these models were employed in conjunction with a consensus molecular docking model to screen a total of 120,571 compounds virtually, leading to the identification of three promising ALK inhibitors: CHEMBL1689515, CHEMBL2380351, and CHEMBL102714. The study recommends further molecular dynamic simulations, in vitro tests, target-specific experimental data acquisition for active learning, and application of advanced AI models like geometric interaction GNN and generative AI for molecular optimization
Vibronic Coupling Effects in the Photoelectron Spectrum of Ozone: A Coupled-Cluster Approach
No full textOne of the most important areas of application for equation-of-motion coupled cluster (EOM-CC) theory is the prediction, simulation, and analysis of various types of electronic spectra. In this work, the EOM-CC method for ionized states, known as EOM-IP-CC, is applied to the closely lying and coupled pair of states of the ozone cation — X̃ 2 A1 and Ã2 B2 — using highly accurate treatments including up to the full single, double, triple, and quadruple excitations (EOM-IP-CCSDTQ). Combined with a venerable and powerful method for calculating vibronic spectra from the Hamiltonian produced by EOM-IP-CC calculations, the simulations yield a spectrum that is in good agreement with the photoelectron spectrum of ozone. Importantly, the calculations suggest that the adiabatic gap separating these two electronic states is somewhat smaller than currently thought; an assignment of the simulated spectrum together with the more precise band positions of the experimen- tal measurements suggests that this energy gap is 1,366±65 cm−
Star-like docking to F mutations of respiratory syncytial virus
No full textThe respiratory syncytial virus (RSV) causes abundant annual fatalities on young children and elder adults by infecting human cells mediated by RSV fusion (F) surface homotrimeric proteins. Despite their RSV fusion in vitro inhibitors, anti-F therapeutic molecules are not yet clinically available because of emergent resistant mutations. Here, alternative therapeutic strategies are explored to dock mutated F protein models. For that thousands of trimeric drug-like candidates were computationally generated/selected by parent-children co-evolution. New top-children candidates may help experimental tests since they display 3-fold star-like molecules fitting similar trimeric F cavities than previous in vitro inhibitors but improved higher sub-nanoMolar affinities. Additionally, some top-children also successfully targeted F mutations previously implicated in RSV fusion drug-resistance
Hydrate-based Carbon Capture via Pressure Swing in a Packed Bed of Ice
No full textThe formation of mixed gas hydrates for pre- or post-combustion capture of carbon dioxide is considered a promising alternative to conventional carbon capture technologies. Yet, to keep up with conventional technologies or even reduce the cost of capture associated with them, a hydrate-based technology must have (1) a short induction time, (2) fast formation kinetics, and (3) moderate process conditions. To date, these requirements can only be met by adding promoters to the system, which comes at its own cost and disadvantages. Here, we show that the requirements can also be met without promoters by forming mixed gas hydrates in a packed bed of ice stabilized by fumed silica. While the high specific surface area of the packed bed warrants short induction times and fast kinetics, low temperatures ensure both moderate formation pressures and a high CO2 selectivity. The favorable properties can be maintained and even improved upon over many capture/regeneration cycles when operated at temperatures lower than 253 K, as this ensures a continuous formation of pores in the ice. We demonstrate the advantages of this route for carbon capture on a bench scale through batch, semi-batch, and continuous experiments. In semi-batch operation at 233 K and 40 bar, the mole fraction of CO2 in a synthetic flue gas is reduced from 15 mol% to 2.5 mol%. At the same thermodynamic conditions, a split fraction of 70% and a specific energy consumption below 3.0 GJ/tCO2 are achieved in continuous operation. The inherent advantages and simplicity of this process, a specific energy consumption comparable with the state of the art even though entirely based on the bench-scale experiment, as well as environmental harmlessness, emphasize the potential of this hydrate-based process to meet the demands of the industry at a minimal cost of capture
Core-Shell Nanotubes from Tungsten/Molybdenum Ditelluride-Tungsten Disulfide via Van der Waals Epitaxy
No full textTungsten ditelluride and molybdenum ditelluride in the form of nanotubes were materialized as core-shell structures with tungsten disulfide layers. The variability of the described protocols allows the formation of WS2@WTe2, reversed WTe2@WS2, and, by combining both approaches, multilayer WTe2@WS2@WTe2. Alternatively, WS2@MoTe2 or even WTe2@WS2@MoTe2 nanotubes were synthesized. Deposition of the telluride layers was achieved via Van der Waals epitaxial growth. Obtained nanostructures were characterized by advanced imaging techniques revealing structural composition hexagonal 2H and orthorhombic 1T’ for WS2 and WTe2 layers, respectively. In the case of MoTe2 nanotubes, the structural cascade of 2H-WS2@2H-MoTe2@1T’-MoTe2 was elucidated. Complex heterostructures were profoundly inspected by 4D scanning transmission electron microscopy position resolved diffraction, revealing their chiral nature. Mechanistic aspects of the synthesis were briefly studied. Moreover, tellurium-substituted WS2 nanotubes were synthesized and characterized. Importantly, WTe2 nanotubes contain inner tension due to layers bending that promise greatly modified electronic structure. Tungsten ditelluride-disulfide nanotubes complete the family of tungsten dichalcogenide nanotubular structures.
Design and Crystallographic Screening of a Highly Sociable and Diverse Fragment Library Towards Novel Antituberculotic Drugs
No full textMissing synthetic tractability is a common pitfall that is often impeding fragment-to-lead campaigns. Ideally, the follow-up fragment extension would be performed quickly and exhaustively, leading to novel hit or lead compounds in rapid succession without the need for tediously developing synthetic methodologies. However, no fragment library currently has this so-called “sociability” as its primary design principle. Herein, we describe the development of a 96-membered, highly diverse, and entirely sociable fragment library suitable for crystallographic screening. Hundreds to thousands of follow-up compounds modified at all growth vectors are available for each fragment from Enamine’s REAL Space. Additionally, tens to hundreds of thousands of larger and more complex leadlike molecules are accessible per library member, further expanded by scaffold-modified, alternative fragments. This allows for rapid exploration of the chemical space around a fragment of interest without much effort. Here, this library was used for a crystallographic fragment screening on a mycobacterial thioredoxin reductase to identify new starting points for developing new anti-tuberculotic agents. Several hits have been identified in a preliminary analysis of the screening
A bench-stable fluorophosphine nickel(0) complex and its catalytic application
No full textWe herein present a bench-stable fluorophosphine-based nickel(0) complex [Ni(PFPh2)4] (1), which is highly stable in air and water. This complex does not only incorporate a nickel centre in the zero-oxidation state, but also includes fluorophosphine ligands. Since these ligands typically tend to disproportionation in solution, they represent a vastly underexplored ligand class. [Ni(PFPh2)4] can be obtained from a one-pot reaction of [Ni(MeCN)4](BF4)2 with Ph2P(=O)–PPh2, involving a unique in-situ reduction of Ni(II) to Ni(0) and the simultaneous fluorination by the BF4– anion. The application of [Ni(PFPh2)4] as highly stable Ni(0) pre-catalyst in combination with additional phosphine ligands, such as dppf (1,1\u27-bis(diphenylphosphino)ferrocene) in Suzuki-Miyaura coupling reactions uncovers its high catalytic activity, which is greatly superior to the conventional Ni(0) source [Ni(COD)2]. A remarkable catalytic activity is achieved through the combination of 1 and dppf after a light-induced activation of the complex
Strategic Electrolyte Design to Address Solubility Competition between Redox-Active Molecules and Supporting Salts
No full textThe solubility of redox-active organic molecules (ROMs) in non-aqueous redox flow
batteries (NRFBs) is a critical factor determining the energy density of the system. However,
the scarcity of comprehensive solubility data has hindered electrolyte development. In this
study, we systematically investigate the solubility behavior of ROMs in the presence of
supporting salts to propose practical electrolyte formulations for NRFBs. Using automated
high-throughput experimentation, we screen the solubility of 2,1,3-benzothiadiazole (BTZ)
and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in various organic solvents.
Leveraging a Random Forest inference model, we identify a binary solvent mixture of mxylene
and acetonitrile, which dissolves 3 M of both BTZ and LiTFSI—exceeding the
previously reported 2 M limit in pure acetonitrile. This enhanced solubility is achieved by
the inclusion of a LiTFSI-phobic yet BTZ-philic solvent, which counterbalances the solubility
competition between BTZ and LiTFSI, with the latter favoring highly polar solvents. This
work introduces a promising electrolyte design strategy for NRFBs and highlights the
effectiveness of high-throughput screening combined with advanced data analysis for
optimizing complex multi-component systems. Furthermore, it emphasizes the urgent need
for more comprehensive solubility data to facilitate the development of practical NRFB
electrolytes