Enhancement of the solubilities of polycyclic aromatic hydrocarbons by weak hydrogen bonds with water

Summary The thermodynamics of mobile order is applied to predict the aqueous solubility of liquid and solid aliphatic and polycyclic aromatic hydrocarbons. The solubility values are mainly determined by the magnitude of the hydrophobic effect. However, contrary to the solubilities of the alkanes, the solubilities of polycyclic aromatic hydrocarbons in water predicted in absence of solute-solvent hydrogen (H) bonds are systematically too low. This shows the contribution of weak specific interactions between the OH groups and the electrons of the aromatic substances. According to the theory, these interactions are characterized by a stability bility constant K_o which can be derived from solubility data. At 25°C, this constant amounts to 80 cm³/mol, the order of magnitude of which can be explained by the competition of these intermolecular bonds with the rather weak self-association bonds in the secondary chains of water.     

A Family of Fitness Landscapes Modeled through Gene Regulatory Networks

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.     

Quantum Stream Cipher Based on Holevo–Yuen Theory

In this review paper, we first introduce the basic concept of quantum computer-resistant cryptography, which is the cornerstone of security technology for the network of a new era. Then, we will describe the positioning of mathematical cryptography and quantum cryptography, that are currently being researched and developed. Quantum cryptography includes QKD and quantum stream cipher, but we point out that the latter is expected as the core technology of next-generation communication systems. Various ideas have been proposed for QKD quantum cryptography, but most of them use a single-photon or similar signal. Then, although such technologies are applicable to special situations, these methods still have several difficulties to provide functions that surpass conventional technologies for social systems in the real environment. Thus, the quantum stream cipher has come to be expected as one promising countermeasure, which artificially creates quantum properties using special modulation techniques based on the macroscopic coherent state. In addition, it has the possibility to provide superior security performance than one-time pad cipher. Finally, we introduce detailed research activity aimed at putting the quantum stream cipher into practical use in social network technology.     

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.     

An Exploration of the Effect of the Kleier Model and Carrier-Mediated Theory to Design Phloem-Mobile Pesticides Based on Researching the N-Alkylated Derivatives of Phenazine-1-Carboxylic Acid-Glycine

The Kleier model and Carrier-mediated theory are effective for molecularly designing pesticides with phloem mobility. However, the single Kleier model or Carrier-mediated theory cannot achieve a reliable explanation of the phloem mobility of all exogenous substances. A detailed investigation of the two models and the scope of their applications can provide a more accurate and highly efficient basis for the guidance of the design and development of phloem-mobile pesticides. In the present paper, a strategy using active ingredient-amino acid conjugates as mode compounds is developed based on Carrier-mediated theory. An N-alkylated amino acid is used to improve the pesticide’s physicochemical properties following the Kleier model, thus allowing the conjugates to fall on the predicted and more accessible transportation region of phloem. Moreover, the influence of this movement on phloem is inspected by the Kleier model and Carrier-mediated theory. To verify this strategy, a series of N-alkylated phenazine-1-carboxylic acid-glycine compounds (PCA-Gly) were designed and synthesized. The results related to the castor bean seeds (R. communis L.) indicated that all the target compounds (4a–4f) had phloem mobility. The capacity for phloem mobility shows that N-alkylated glycine containing small substituents can significantly improve PCA phloem mobility, such as 4c(i-C₃H₇-N) > 4a(CH₃-N) ≈ 4b(C₂H₅-N) > 4d (t-C₄H₉-N) > PCA-Gly > 4e(C₆H₅-N) > 4f(CH₂COOH-N), with an oil–water partition coefficient between 1.2~2.5. In particular, compounds 4a(CH₃-N), 4b(C₂H₅-N), and 4c(i-C₃H₇-N) present better phloem mobility, with the average concentrations in phloem sap of 14.62 μΜ, 13.98 μΜ, and 17.63 μΜ in the first 5 h, which are 8 to 10 times higher than PCA-Gly (1.71 μΜ). The results reveal that the Kleier model and Carrier-mediated theory play a guiding role in the design of phloem-mobile pesticides. However, the single Kleier model or Carrier-mediated theory are not entirely accurate. Still, there is a synergism between Carrier-mediated theory and the Kleier model for promoting the phloem transport of exogenous compounds. Therefore, we suggest the introduction of endogenous plant compounds as a promoiety to improve the phloem mobility of pesticides through Carrier-mediated theory. It is necessary to consider the improvement of physicochemical properties according to the Kleier model, which can contribute to a scientific theory for developing phloem-mobile pesticides.     

一例含阴离子水簇的钴配合物的合成、结构及理论研究

金属配合物中的水簇研究为研究宏观意义上的水以及与蛋白质分子有关的水分子提供了有效途径。本文合成了一个含有阴离子水簇的带状超分子配合物[Co(2,2-bipy)₂(N₃)₂](N₃)_0.5Cl_0.5·2H₂O(1,2, 2-bipy=2, 2-联吡啶)。单晶结构解析表明,配合物属于三斜晶系,P-1空间群,晶胞参数为:a=0.822 54(7) nm,b=1.175 58(9) nm,c=1.237 06(10) nm,α=91.379 0(10)°,β=92.151 0(10)°,γ=108.119 0(10)°,V=1.135 27(16) nm³,由一个单核[Co(2,2-bipy)₂(N₃)₂]⁺配合物阳离子、两个非配位水分子、0.5个游离的叠氮离子和0.5个氯离子组成,叠氮离子和氯离子位置无序,占有率各为50%。两个客体水分子通过强烈的分子间氢键作用形成了环状水四聚体,且与无序的N^-₃和Cl^-通过氢键作用形成了一个[(H₂O)₄(N₃)Cl]^2- 阴离子水簇。此外,本文基于密度泛函理论(DFT)对配合物[Co(2,2-bipy)₂(N₃)₂]⁺阳离子进行了量子化学计算,分析了其单点能和原子电荷,并计算了中心金属离子的氧化态,计算结果与实验相吻合。     

2,2-双(4-氯-2-氟苄基)丙二酸二乙酯的晶体结构和密度泛函理论研究

标题化合物是一个重要的精细化工中间体,可用于制备嘧啶类、吡唑类等产品.本文利用红外光谱(IR)、质谱(MS)、核磁共振氢谱(1 H NMR)、核磁共振碳谱(13 C NMR)和X-射线单晶衍射对此化合物进行了表征,并在B3LYP/6-311G(d,p)模式下使用密度泛函理论(DFT)计算了此化合物的最稳定晶体结构以及最...     

金催化乙炔氢氯化的密度泛函理论研究进展

乙炔氢氯化(AH)是生产氯乙烯的主要途径之一,传统上使用高毒性的汞催化剂,因此开发无汞催化剂迫在眉睫。金(Au)催化剂是最有潜力的替代催化剂之一,然而其活性Au物种、反应物的活化过程或反应过渡态结构等催化机理仍不够清晰。密度泛函理论（DFT）在研究由Au催化AH的反应机理中发挥了极其重要的作用。本文综述了DFT对金催化剂活性位点、反应物在催化剂上的吸附性质及反应机理的研究进展。重点讨论了DFT对阳离子金和金簇催化AH反应过程的模拟计算,包括Au电子状态、其它原子掺杂及金簇尺寸和形状对催化AH反应影响的模拟。结果表明DFT模拟计算在微观分子尺度上研究反应物的吸附、反应中间体及过渡态等方面发挥了关键作用,对理解Au催化AH反应机理做出了重要贡献。     

Surface Modification Strategy for Enhanced NO2 Capture in Metal–Organic Frameworks

The interaction strength of nitrogen dioxide (NO₂) with a set of 43 functionalized benzene molecules was investigated by performing density functional theory (DFT) calculations. The functional groups under study were strategically selected as potential modifications of the organic linker of existing metal–organic frameworks (MOFs) in order to enhance their uptake of NO₂ molecules. Among the functional groups considered, the highest interaction energy with NO₂ (5.4 kcal/mol) was found for phenyl hydrogen sulfate (-OSO₃H) at the RI-DSD-BLYP/def2-TZVPP level of theory—an interaction almost three times larger than the corresponding binding energy for non-functionalized benzene (2.0 kcal/mol). The groups with the strongest NO₂ interactions (-OSO₃H, -PO₃H₂, -OPO₃H₂) were selected for functionalizing the linker of IRMOF-8 and investigating the trend in their NO₂ uptake capacities with grand canonical Monte Carlo (GCMC) simulations at ambient temperature for a wide pressure range. The predicted isotherms show a profound enhancement of the NO₂ uptake with the introduction of the strongly-binding functional groups in the framework, rendering them promising modification candidates for improving the NO₂ uptake performance not only in MOFs but also in various other porous materials.     

Molecular Simulation to Explore the Dissolution Behavior of Sulfur in Carbon Disulfide

Soluble sulfur (S₈) and insoluble sulfur (IS) have different application fields, and molecular dynamics simulation can reveal their differences in solubility in solvents. It is found that in the simulated carbon disulfide (CS₂) solvent, soluble sulfur in the form of clusters mainly promotes the dissolution of clusters through van der Waals interaction between solvent molecules (CS₂) and S₈, and the solubility gradually increases with the increase in temperature. However, the strong interaction between polymer chains of insoluble sulfur in the form of polymer hinders the diffusion of IS into CS₂ solvent, which is not conducive to high-temperature dissolution. The simulated solubility parameter shows that the solubility parameter of soluble sulfur is closer to that of the solvent, which is consistent with the above explanation that soluble sulfur is easy to dissolve.