Dual-stage lithography from a light-driven, plasmon-assisted process: a hierarchical approach to subwavelength features

A hierarchy of lithographic-type imaging generating 3 μm lines incorporating subdiffraction limit features was obtained through a novel two-step reaction process. Photochemically generated ketyl radicals were used to make defined lines of silver nanoparticles. The excitation of nanoparticle surface plasmons was then used to generate highly localized heat that causes polymerization selectively on the surfaces of excited particles. The nylon-6 polymer that is generated serves as a solubility switch used to retain the features on the substrate selectively; various imaging techniques were used to establish the nature of the nylon shells. This work shows that the heat generated by plasmon excitation can be exploited to generate negative-type lithographic features with dimensions well below the diffraction limit.     

氯代二苯并呋喃(PCDFs)毒性的QSAR研究

从1977年Oile首次报道城市垃圾及工业废弃物中的含氯化合物在焚烧过程中可能产生二噁英以来,人们对二噁英类化合物的产生及其对环境可能造成的危害进行了较为广泛的研究[1-3].氯代二苯并呋喃(PCDFs)是二噁英的一种,一方面由于它具有剧毒性,会导致内分泌系统和体内荷尔蒙平衡紊乱,另一方面它能对机体新陈代谢、免疫力和生殖系统造成长久的损伤,因此,有关此类化合物毒性的研究近年来受到了学术界的关注[4-7].     

Determining the validity of a QSAR model--a classification approach

The determination of the validity of a QSAR model when applied to new compounds is an important concern in the field of QSAR and QSPR modeling. Various scoring techniques can be applied to specific types of models. We present a technique with which we can state whether a new compound will be well predicted by a previously built QSAR model. In this study we focus on linear regression models only, though the technique is general and could also be applied to other types of quantitative models. Our technique is based on a classification method that divides regression residuals from a previously generated model into a good class and bad class and then builds a classifier based on this division. The trained classifier is then used to determine the class of the residual for a new compound. We investigated the performance of a variety of classifiers, both linear and nonlinear. The technique was tested on two data sets from the literature and a hand built data set. The data sets selected covered both physical and biological properties and also presented the methodology with quantitative regression models of varying quality. The results indicate that this technique can determine whether a new compound will be well or poorly predicted with weighted success rates ranging from 73% to 94% for the best classifier.     

Reactions of a stable silylene with halocarbons

An unusual product formation is observed for the insertion reaction of the thermally stable silylene Si[(NCH(2)Bu(t))(2)C(6)H(4)-1,2][abbrev. as Si(NN)] into the carbon-halogen bond of alkyl or aryl halides RHal (Hal=Cl, Br). In general, depending on the halogen, the reaction either results in a disilane of type (NN)Si(Hal)-(R)Si(NN) for Hal=Cl or a mixture of disilane and the monosilane (NN)Si(R)(Hal) for Hal=Br. The results are put into context to previously suggested mechanisms. The disilane (NN)Si(Hal)-(R)Si(NN)(Hal=Cl or Br) is thermally labile and mild thermolysis yields the corresponding monosilane (NN)Si(R)(Hal) and silylene 1. Additionally, strong evidence is presented for a radical pathway for the reaction of 1 and RHal.     

QSAR of chemical polarizability and nerve toxicity. 2

Polarizability is a property of molecules that has long been of interest to scientists from a variety of viewpoints. However, in the area of the QSAR of chemical-biological interactions, it has received little attention. Recently we have shown that one can use the simple summation of the valence electrons (H = 1, C = 4, O = 6, etc.) in a molecule as a measure of its polarizability. We have found this parameter to correlate nerve toxicity of a wide variety of chemicals acting on nerves of frogs, rabbits, cockroaches, and humans.     

A QSAR of the toxicity of amino-benzenes and their structures

The quantum chemical parameters and the topological indices have been calculated for the prediction of the toxicity of amino-benzenes in the environment, and work has been done on the multiple regression and neural networks. The combination of CoMFA with formation heat yields greatly improved results. A good model has been obtained which provides a basis for the studies of the toxic action mechanism.