Dejesushusted4099

9,10-Dihydroacridines are frequently encountered as key scaffolds in OLEDs. However, accessing those compounds from feedstock precursors typically requires multiple steps. Herein, a modular one-pot synthesis of 9,10-dihydroacridine frameworks is achieved through a reaction sequence featuring a selective ortho-C alkenylation of diarylamines with aryl alkynes followed by an intramolecular hydroarylation of the olefin formed as an intermediate. This transformation was accomplished by virtue of the combination of hexafluoroisopropanol and triflimide as a catalyst that triggers the whole process.Covalent organic frameworks (COFs) have received much attention in the biomedical area. However, little has been reported about stimuli-responsive COF for drug delivery. Herein, we synthesized a hypoxia-responsive azo bond-containing COF with nanoscale size and immobilized both photosensitizers chlorin e6 (Ce6) and hypoxia-activated drug tirapazamine (TPZ) into the COFs. When such a COF entered the hypoxic environment and tumor, the COF structure was ruptured and loaded drugs were released from the COF. Together, upon near-infrared (NIR) light irradiation, Ce6 consumed oxygen to produce cytotoxic reactive oxygen species, leading to elevated hypoxia. Such two-step hypoxia stimuli successively induced the deintegration of COF, drug release and activation of TPZ. This promoted the TPZ to generate massive biotoxic oxyradical. In vitro and in vivo evaluation indicated that this two-step hypoxia-activated COF drug delivery system could kill cancer cells and inhibit the growth of tumors effectively.Porous solids used in practical applications often possess structural disorder over broad length scales. This disorder strongly affects different properties of the substances confined in their pore spaces. Quantifying structural disorder and correlating it with the physical properties of confined matter is thus a necessary step toward the rational use of porous solids in practical applications and process optimization. The present work focuses on recent advances made in the understanding of correlations between the phase state and geometric disorder in nanoporous solids. We overview the recently developed statistical theory for phase transitions in a minimalistic model of disordered pore networks linear chains of pores with statistical disorder. By correlating its predictions with various experimental observations, we show that this model gives notable insight into collective phenomena in phase-transition processes in disordered materials and is capable of explaining self-consistently the majority of the experimental results obtained for gas-liquid and solid-liquid equilibria in mesoporous solids. The potentials of the theory for improving the gas sorption and thermoporometry characterization of porous materials are discussed.This study analyzes the adsorption behavior in two-dimensional heterogeneous slit pores using nonlocal density functional theory based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. Both chemical heterogeneity and surface roughness on small atomistic scales are investigated. The solid structure is considered as individual solid interaction sites whereby chemical heterogeneity is introduced through the presence of different solid-fluid sites and molecular roughness by varying the position of the interaction sites in the first solid layers. The effect of both forms of heterogeneity on the adsorption behavior is assessed individually. Effective one-dimensional solid-fluid potentials provide a way to reduce the dimensionality and computational demand of the density functional theory (DFT) calculations. We determine one-dimensional free-energy-averaged (FEA) solid-fluid potentials of methane and n-butane in the low-density limit for solid systems with molecular roughness and chemical heterogeneity. Using this effective one-dimensional solid-fluid potential at any density, we find excellent agreement of adsorption isotherms for both solid descriptions in systems with homogeneous slit pores. Subcritical adsorption isotherms of n-butane in slit pores with surface roughness show deviations at higher pressures due to the formation of fluid layers in the one-dimensional FEA potential. Chemical heterogeneity introduces a shift of the capillary condensation pressure below the saturation pressure of the bulk liquid, which is well described by the free-energy-averaged system.Recently, a quantum algorithm that is capable of directly calculating the energy gap between two electronic states having different spin quantum numbers without inspecting the total energy of the individual electronic states was proposed. This quantum algorithm guarantees an exponential speedup, like quantum phase estimation (QPE)-based full-CI, with much lower costs. In this work, we propose a modified quantum circuit for the direct calculations of spin state energy gaps to reduce the number of qubits and quantum gates, extending the quantum algorithm to the direct calculation of vertical ionization energies. Numerical quantum circuit simulations for the ionization of light atoms (He, Li, Be, B, C, and N) and small molecules (HF, BF, CF, CO, O2, NO, CN, F2, H2O, and NH3) revealed that the proposed quantum algorithm affords the vertical ionization energies within 0.1 eV of precision.Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering, for the first time. KPT-185 The filtration process proposed in the persistent homology is employed to generate a series of nested molecular graphs. Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as OPRC. Moreover, persistent attributes, which are statistical and combinatorial properties of OPRCs during the filtration process, are used as molecular descriptors and further combined with machine learning models, in particular, gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction of the protein-ligand binding affinity, which is one of the key steps in drug design. Based on three of the most commonly used data sets from the well-established protein-ligand binding databank, that is, PDBbind, we intensively test our model and compare with existing models.