Spectroscopic properties of the low-lying electronic states and laser cooling feasibility for the SrI molecule

Laser cooling of a molecule with heavy nuclei is often complicated because of the density distribution of the electronic states. Here, we evaluate the feasibility of the laser cooling of the SrI molecule by calculating the potential energy curves and transition dipole moments of the ground and low-lying excited states using the multi-reference configuration interaction plus Davidson corrections (MRCI + Q) and the all-electron basis sets of ANO-RCC. The relativistic effect and the spin-orbit coupling splits are included, because both Sr and I are heavy atoms. Based on the obtained potential energy curves, we solve the Schrödinger equation of nuclear motion to determine the rovibrational energy levels and the Franck-Condon factors. The spectroscopic parameters are obtained by fitting the rovibrational energy levels with the Dunham expression. The radiation lifetimes, the Doppler and recoil temperatures between the X²Σ⁺ and the ²Π_1/2/²Π_3/2/B²Σ⁺ states are calculated. 5-color laser cooling schemes for the molecule are proposed, which can lead to the total effective Franck-Condon factors being 0.99983, 0.99979, and 0.99941 for the three transitions, respectively. All the obtained results suggest that the SrI molecule is a feasible candidate for laser cooling.     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

Q过程的不变分布(Ⅱ)

设E为一个可数集,Q=（qi,j;i,j∈E）为E×E上的矩阵,满足m为E上的概率分布满足何时存在Q过程,使得m是它的不变分布? 这个问题由Williams（1979）作为一个开问题提出.文[15]对全稳定情形,解决了这个问题;本文对单瞬时情形,完整地解决了该问题.     

Impact of particle size on conductivity and storage capacity as derived from the core–space charge model

The impact of interfaces on variety of materials properties scales with the density of interfaces within a material. This statement holds true independent of the specific interfacial mechanism, as long as the density of interfaces is rather low. If the spacing between interfaces is being further reduced, interesting non-trivial effects are expected and have also been observed. In this paper, the ionic conductivity in ionic conductors and the storage capacity (non-stoichiometry) of mixed conductors as a function of size are considered. The discussion is based on the core–space charge model in which we assume that only the core of an interface exhibits its own defect energetics, while the energetics of the space charge layers remain unaltered (compared to the bulk). It is shown that in the case of Schottky profiles anomalous conductivity effects are predicted. As regards the non-stoichiometry effects, it is demonstrated that at sizes small compared to the Debye length the difference between a composite consisting of an ionic and an electronic conductor and a «true» mixed conductor becomes blurred. The latter effect has recently been detected in the field of Li-batteries and is here commented on.     

Lower Bounds from State Space Relaxations for Concave Cost Network Flow Problems

In this paper we obtain Lower Bounds (LBs) to concave cost network flow problems. The LBs are derived from state space relaxations of a dynamic programming formulation, which involve the use of non-injective mapping functions guaranteing a reduction on the cardinality of the state space. The general state space relaxation procedure is extended to address problems involving transitions that go across several stages, as is the case of network flow problems. Applications for these LBs include: estimation of the quality of heuristic solutions; local search methods that use information of the LB solution structure to find initial solutions to restart the search (Fontes et al., 2003, Networks, 41, 221–228); and branch-and-bound (BB) methods having as a bounding procedure a modified version of the LB algorithm developed here, (see Fontes et al., 2005a). These LBs are iteratively improved by penalizing, in a Lagrangian fashion, customers not exactly satisfied or by performing state space modifications. Both the penalties and the state space are updated by using the subgradient method. Additional constraints are developed to improve further the LBs by reducing the searchable space. The computational results provided show that very good bounds can be obtained for concave cost network flow problems, particularly for fixed-charge problems.     

The interfacial conformation of polypropylene glycols and foam behaviour

The foam behaviour of low molecular weight polypropylene glycols (PPG) was investigated as a function of concentration and molecular weight (190–2000 g mol⁻¹). For each polypropylene glycol, foam stability increases with concentration and passes through a maximum, beyond which foamability is suppressed as the solubility limit of the glycol is exceeded and droplets of glycol form. Light-scattering data as well as static and dynamic surface tension results provide the key information leading to these interpretations. A maximum in foamability was observed for the PPG molecules with increasing molecular weight (caused by a change in molecular conformation at the interface). This suppresses the Marangoni effect and leads to a decrease in foam stability.     

Synthesis of enantiomerically pure β,β-diphenylalanine (Dip) and fluorenylglycine (Flg)

A new strategy for the preparation of both enantiomers of two phenylalanine analogues, β,β-diphenylalanine and fluorenylglycine, has been developed. The combination of a high yielding racemic synthesis and a very efficient resolution procedure has provided significant amounts of each amino acid in enantiomerically pure form and suitably protected for use in peptide synthesis. This methodology can be easily applied to the preparation of larger quantities of enantiopure compounds.