Facile Synthesis of Hierarchical Nanosized Single‐Crystal Aluminophosphate Molecular Sieves from Highly Homogeneous and Concentrated Precursors

The synthesis of hierarchical nanosized zeolite materials without growth modifiers and mesoporogens remains a substantial challenge. Herein, we report a general synthetic approach to produce hierarchical nanosized single‐crystal aluminophosphate molecular sieves by preparing highly homogeneous and concentrated precursors and heating at elevated temperatures. Accordingly, aluminophosphate zeotypes of LTA (8‐rings), AEL (10‐rings), AFI (12‐rings), and ‐CLO (20‐rings) topologies, ranging from small to extra‐large pores, were synthesized. These materials show exceptional properties, including small crystallites (30–150 nm), good monodispersity, abundant mesopores, and excellent thermal stability. A time‐dependent study revealed a non‐classical crystallization pathway by particle attachment. This work opens a new avenue for the development of hierarchical nanosized zeolite materials and understanding their crystallization mechanism.     

Facile Synthesis of Hierarchical Nanosized Single-Crystal Aluminophosphate Molecular Sieves from Highly Homogeneous and Concentrated Precursors

The synthesis of hierarchical nanosized zeolite materials without growth modifiers and mesoporogens remains a substantial challenge. Herein, we report a general synthetic approach to produce hierarchical nanosized single-crystal aluminophosphate molecular sieves by preparing highly homogeneous and concentrated precursors and heating at elevated temperatures. Accordingly, aluminophosphate zeotypes of LTA (8-rings), AEL (10-rings), AFI (12-rings), and -CLO (20-rings) topologies, ranging from small to extra-large pores, were synthesized. These materials show exceptional properties, including small crystallites (30–150 nm), good monodispersity, abundant mesopores, and excellent thermal stability. A time-dependent study revealed a non-classical crystallization pathway by particle attachment. This work opens a new avenue for the development of hierarchical nanosized zeolite materials and understanding their crystallization mechanism.     

An evaluation of various computational methods for the treatment of organoselenium compounds

A reliable computational method for the prediction of organoselenium geometries and bond dissociation energies (BDEs) has been determined on the basis of the performance of density functional theory (DFT: B3LYP and B3PW91) and ab initio molecular orbital procedures (Hartree-Fock (HF)) in conjunction with various Pople basis sets including (but not limited to) the 6-31G(d), 6-31G(d,p), 6-311G(d), 6-311G(d,p), 6-311G(2df,p), and 6-311G(3df,3pd) sets. Predicted geometries and BDEs are compared with available experimental data and quadratic configuration interaction including single and double substitutions (QCISD) results. The B3PW91/6-311G(2df,p) level of theory is recommended for the prediction of the geometries and energetics of organoselenium compounds.     

聚异丁烯高活性端基含量及相对分子质量测定方法的研究

综合使用核磁，VPO和近红外光谱分析技术对聚异丁烯活性端基含量以及相对分子质量的测定进行了详细研究，分别建立核测定聚异丁烯活性端基含量以及其他烯键含量和近红外光谱快速测量聚异聚丁烯活性端基含量。其他烯键含量和相对分子质量的分析方法。成对t检验结果表明，近红外光谱分析方法测定结果与核磁和VPO方法测定结果之间无显著性差异。     

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.     

The application oft-measures of fuzziness

By introducing thet-measures of fuzziness we give a method for determination the degree of interference between the elements with different properties of a universe. Finally, we apply this method in geography of population.     

Molecular-dynamics simulation for the characterization of liquid chromatographic stationary phase: effect of temperature.

The influence of temperature on the surface structure of the octadecylsilica (ODS) bonded phase was investigated with a molecular dynamics (MD) simulation. The MD simulation was applied to a molecular model consisting of three parts: amorphous silica base, dimethyloctadecylsilyl ligands and n-hexane as a mobile phase solvent. More detailed information on the effect of temperature was obtained at the low temperature region than that reported in our previous study. The motion of ODS ligands could be estimated by the mean square displacement (MSD) of the terminal carbon atoms of ODS ligands. The gauche fraction in the ODS ligand conformation can also be estimated to obtain the ligand conformation for each simulation condition in detail. It can be seen that an elevated temperature induced the more bent ligand conformation. The trend has a good agreement to that of the results experimentally observed by using various spectroscopic techniques such as nuclear magnetic resonance (NMR), Fourier-transform infrared spectroscopy (FT-IR), and Raman spectroscopy.     

Conformational behavior of styrene–p-chlorostyrene triblock copolymers in dilute solutions. I. Composition dependence of conformational behavior

Dilute solution properties of (styrene-p-chlorostyrene) triblock copolymers in various solvents were studied over a wide range of molecular weight and composition. Viscosity and osmotic pressure results indicate that the conformational behavior of the B_mA_nB_m and A_mB_nA_m copolymers (A = styrene; B = p-chlorostyrene; m and n denote the number of units) are similar in nonselective solvents such as toluene and 2-butanone, but different in selective solvents such as carbon tetrachloride and cumene. Short-range and long-range interaction parameters of the block copolymers were determined by applying the Stockmayer–Fixman method to viscosity data and also by application of the equation relating the osmotic virial coefficient and the excluded volume. The results show that the unperturbed dimensions of the block copolymers vary linearly with composition, and long-range interaction parameters in nonselective solvents can be expressed by those of the parent homopolymers, the chemical composition, and values of the interaction parameter β_AB between styrene and p-chlorostyrene monomeric units.