国内外化学相异构想研究的进展与启示*

国内外有关化学学科的相异构想研究主要包括对学生相异构想进行诊断及其原因分析,以及开发有效的教学模式或策略对学生相异构想进行概念转变。通过对国内外相关研究现状的分析,为相异构想的后续研究提供思路。     

CO2-fixation into carbonate anions for the construction of 3d-4f cluster complexes with salen-type Schiff base ligands: from molecular magnetic refrigerants to luminescent single-molecule magnets

Six new carbonate-bridged Zn₂Ln₂ cluster complexes derived from salen-type Schiff base ligands [H₂L^a = N, N′-bis(3-methoxysalicylidene)-1,3-diaminopropane and H₂L^b = N, N′-bis(3-methoxysalicylidene)- 1,2-diaminoethane] have been synthesized. The bis-imine chain in Schiff base ligands have an obvious influence on the cluster complexes' structures, magnetic and luminescence properties. The carbonate bridging ligand exactly comes from autoimmobilization of carbon dioxide, which may mediate ferromagnetic coupling between Ln³⁺ ions, favoring magnetocaloric effects and single molecule magnet (SMM) properties. Complexes Zn₂Dy₂(μ³-CO₃)₂(L^a)₂(NO₃)₂(MeOH)₂ ( 1 ) and [Zn₂Dy₂(μ³-CO₃)₂(L^b)₂(NO₃)₂]·2MeOH ( 2) show field-induced SMM properties; complexes Zn₂Tb₂(μ³-CO₃)₂(L^a)₂(NO₃)₂(MeOH)₂ ( 3 ) and [Zn₂Tb₂(μ³-CO₃)₂(L^b)₂(NO₃)₂]·2MeOH ( 4 ) display both luminescence and field-induced SMM behaviors; while complexes [Zn₂Gd₂(μ³-CO₃)₂(L^a)₂(NO₃)₂]·2MeOH ( 5 ) and [Zn₂Gd₂(μ³-CO₃)₂(L^b)₂(NO₃)₂]·2MeOH ( 6 ) exhibit medium magnetic entropy changes, which are candidates for cryogenic molecular magnetic refrigerants.     

Simultaneous analysis of current‐use pesticides and their transformation products in water using mixture‐sorbent solid phase extraction and high‐performance liquid chromatography–tandem mass spectrometry

Pesticides have posed significant threats to aquatic ecosystems, yet little is known about their transformation products. The challenge is to simultaneously analyze various pesticides and transformation products in water as they have distinct physicochemical properties. A mix‐mode solid phase extraction method was established to simultaneously analyze current‐use pesticides and their transformation products using a mixture of hydrophile–lipophile balance, weak anion, and cation exchange resins (2:1:1, w/w/w) in combination with high‐performance liquid chromatography and tandem mass spectrometry for chemical quantification. Neutral, acidic, and alkaline methanol were used as the elution solvent. Box‐Behnken design was applied to optimize extraction conditions. Optimal conditions were as follows: sorbent mass, 200 mg; volume of elution solvent, 5 mL × 3; pH 4. The method was validated for compounds at concentrations from 20 to 1000 ng/L in different types of water samples, with recovery being from 43.5 ± 3.1 to 141 ± 35%. Low method detection limits (0.02?5.6 ng/L) implied that the developed method was sensitive. Finally, the method was applied to monitor current‐use pesticides and their transformation products in natural waters. Frequent detection of transformation products of pesticides indicated that their contribution to aquatic risk should not be ignored.     

Molecular signal suppression by in situ microextraction in nuclear magnetic resonance spectroscopy

The detailed characterization of complex mixtures by NMR is often hampered by the presence of signals from uninformative compounds, the resonances of which overlap with those of the molecules of interest. We provide here a proof of principle for an approach to NMR signal suppression in complex samples using Molecularly Imprinted Polymers (MIPS). Addition of a few milligrams of polymer to a solution traps the target molecule in typical micromolar to millimolar concentration, thus achieving in situ signal suppression, without altering any other spectral features. This method minimized any manipulation or perturbation of the spectrum and was applied to a complex mixture of known compounds and to a plant extract, in both cases spiked with a compound (bisphenol A), which was subsequently removed by selective binding to a complementary MIP. What is described in this report is comparable with microextraction and may in due course be applied to a large number of analytical challenges. Copyright © 2014 John Wiley & Sons, Ltd.     

Study of Viscosity-temperature Properties of Oil and Gas-condensate Mixtures in Critical Temperature Ranges of Phase Transitions

Transport of oil and gas-condensate mixtures of various compositions is found to be accompanied by a slight increase in viscosity in the coldest period when ground temperatures at depth of a condensate pipeline reach 0 – minus 4°С. Fall in temperature of oil fluids under study to minus 10 – minus 30°С is accompanied by a sharp increase in all structural and rheological parameters of the mixture. Even a slight amount of oil added to a gas-condensate mixture causes a significant decrease in viscosity in the negative temperature range. As a result, cloud and pour point of a mixture falls, its amount decreases, the structure of paraffin deposits changes.     

Absolute Minimal Sampling of Homonuclear 2D NMR TOCSY Spectra for High-Throughput Applications of Complex Mixtures

Modern applications of 2D NMR spectroscopy to diagnostic screening, metabolomics, quality control, and other high-throughput applications are often limited by the time-consuming sampling requirements along the indirect time domain t₁. 2D total correlation spectroscopy (TOCSY) provides unique spin connectivity information for the analysis of a large number of compounds in complex mixtures, but standard methods typically require >100 t₁ increments for an accurate spectral reconstruction, rendering these experiments ineffective for high-throughput applications. For a complex metabolite mixture it is demonstrated that absolute minimal sampling (AMS), based on direct fitting of resonance frequencies and amplitudes in the time domain, yields an accurate spectral reconstruction of TOCSY spectra using as few as 16 t₁ points. This permits the rapid collection of homonuclear 2D NMR experiments at high resolution with measurement times that previously were only the realm of 1D experiments.     

Identifying Mixtures of Mixtures Using Bayesian Estimation

The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online.     

Maximum likelihood estimation for stochastic volatility in mean models with heavy‐tailed distributions

In this article, we introduce a likelihood‐based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions. Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. Likelihood‐based estimation of the parameters of stochastic volatility models, in general, and SVM models with SMN distributions, in particular, is usually regarded as challenging as the likelihood is a high‐dimensional multiple integral. However, the HMM approximation, which is very easy to implement, makes numerical maximum of the likelihood feasible and leads to simple formulae for forecast distributions, for computing appropriately defined residuals, and for decoding, that is, estimating the volatility of the process. Copyright © 2017 John Wiley & Sons, Ltd.     

奇异超线性半正定二阶周期边值问题的正解

基于格林函数理论,主要利用Lerray-Schauder抉择定理和上下解方法针对半正定条件下,奇异超线性二阶周期边值问题正解的存在性进行推理证明,获取了奇异超线性二阶周期边值问题的一个正解.