Characterization of military fog oil by comprehensive two-dimensional gas chromatography

The most commonly used military fog oil is characterized by comprehensive two-dimensional gas chromatography (GC×GC) coupled to either Flame Ionization Detection (FID) or Time-of-Flight Mass Spectrometric Detection (TOFMS) to advance the knowledge regarding the complete chemical makeup of this complex matrix. Two different GC×GC column sets were investigated, one employing a non-polar column combined with a shape selective column and the other an inverse column set (medium-polar/non-polar). The inverse set maximizes the use of the two-dimensional separation space and segregates aliphatic from aromatic fractions. The shape selective column best separates individual polycyclic aromatic hydrocarbons (PAHs) from the bulk oil. The results reveal that fog oil (FO) is composed mainly of aliphatic compounds ranging from C₁₀ to C₃₀, where naphthenes comprise the major fraction. Although many different species of aromatics are present, they constitute only a minor fraction in this oil, and no conjugated PAHs are found. The composition of chemically similar aliphatic constituents limits the analytical power of silica gel fractionation and GC–MS analysis to characterize FO. Among the aliphatic compounds identified are alkanes, cyclohexanes, hexahydroindanes, decalins, adamantanes, and bicyclohexane. The aromatic fraction is composed of alkylbenzene compounds, indanes, tetrahydronaphthalenes, partially hydrogenated PAHs, biphenyls, dibenzofurans and dibenzothiophenes. This work represents the best characterization of military fog oil to date. As the characterization process shows, information on such complex samples can only be parsed using a combination of sample preprocessing steps, multiple detection schemes, and an intelligent selection of column chemistries.     

Mechanical properties of attapulgite clay reinforced polyurethane shape-memory nanocomposites

Nanocomposites based on attapulgite clay and shape-memory polyurethane were fabricated by mechanical mixing. The mechanical properties of samples were evaluated using a micro-indentation tester. The untreated commercial attapulgite clay resulted in a significant decrease in glass transition temperature and hardness of the nanocomposite due to the presence of moisture as well as the clay’s amorphous structure and surface hydroxyl groups. The attapulgite nanoparticles were heat-treated at 850 °C, which resulted in crystallization of the particles and formation of layered attapulgite structure. The hardness of the nanocomposites composed of the heat treated clay powder dramatically increased as a function of clay content, which is attributed to the homogeneous dispersion of the nanofillers in the polymer matrix and strong filler-polymer interactions. Shape recovery of indentations has been demonstrated upon heating.     

Enantioselective synthesis of α,β-diamino ester derivatives: memory of chirality in imino-aldol reactions

A simple strategy for the synthesis of chiral α,β-diamino ester derivatives in good yields and ee (up to 92%) utilizing the ‘memory of chirality’ concept is reported. This methodology has been extended for the enantioselective synthesis of substituted aziridines with excellent ee (92%).     

Level-set based topology optimization for electromagnetic dipole antenna design

This paper presents a level-set framework for a typical electromagnetic design problem of dipole antenna. In this study, the geometrical configuration of an antenna is represented by the zero-level contour of a higher-dimensional level-set function. The governing equation for the induced current flow on a metal surface is the Electric Field Integral Equation (EFIE), which takes into account the electric component of the incident wave. The design objective is formulated in terms of the surface current and incident electric field. The normal velocity of the level-set model, which reflects the sensitivity of the objective function, is derived from the adjoint variable method and shape derivative. By optimizing the objective function, the area with the highest current density, to which the voltage feeding should be applied, can be reshaped. The advantages of adopting the level-set technique for electromagnetic design lie in its capacity for capturing sophisticated topological changes and facilitation in mathematical representation of the design configuration. The demonstrative examples of dipole antenna design show that the level-set method results in a fairly smooth optimization process, where the vacuum/metal interface gradually attains its optimal configuration. A series of design cases with self-adjoint and non-self-adjoint sensitivity analyses are studied and compared to the benchmarking problems in dipole antenna.     

Internal stress dependent structural transition in ferromagnetic Ni-Mn-Ga nanoparticles prepared by ball milling

Ni-Mn-Ga nanoparticles were prepared by ball milling technique. X-ray diffraction pattern of the milled powders has a broad peak near the location of the prominent peak for the Heusler phase of Ni₂MnGa, indicating very disordered structures with small particle sizes. Structural properties of milled Ni-Mn-Ga particles recover to those of the bulk state after appropriate annealing temperature. It is worth noting that particles with size above 50 nm exhibit martensitic transformation. The average internal stress was calculated to be 2.83-1.13 MPa stored in the distorted lattice. Saturation magnetization of the milled sample increases with annealing temperature due to re-crystallization and grain growth.     

Synthesis and magnetic properties of size-controlled FeNi alloy nanoparticles attached on multiwalled carbon nanotubes

FeNi alloy nanoparticles with controllable sizes were attached on the multiwalled carbon nanotubes by adjusting the atomic ratio of metal to carbon in the mixed solution of nitrate with Fe:Ni=1:1 (atomic ratio) via wet chemistry. Transmission electron microscopy (TEM) and high-resolution TEM indicated that quasi-spherical FeNi alloy nanoparticles with sizes in the range 12-25 nm are obtained. FeNi alloy composed of major face center cubic (fcc) and minor body center cubic (bcc) structures, which is proved by the X-ray powder diffraction (XRD). Magnetization measured by vibrating sample magnetometer demonstrated that both the coercive force and saturation magnetizations decrease as the size of the FeNi alloy nanoparticles decreased. The chemical method is promising for fabricating FeNi alloy nanoparticles attached on carbon nanotubes for magnetic storage and ultra high-density magnetic recording applications.     

A memory device sustained by noise

We propose a memory device consisting of a ring of two bistable coupled oscillators. We show that this system is capable of storing a single bit and its performance improves with noise, in agreement with previous experimental results obtained with discrete bistable electronic circuits. The stored bit can be retrieved asynchronously and, after a certain synchronization time, the probability of error does not depend on the interrogated oscillator. Memory persistence is shown to be maximized for the same noise range that both minimizes the probability of error and ensures synchronization.     

ICP-AES测定钽合金中的钨、铪

建立了ICP-AES直接测定钽合金中钨、铪的分析方法。采用基体匹配与背景扣除法消除钽基体对钨、铪的光谱干扰,选择多谱线平均值法进行了光谱分析。待测元素的回收率为98.0%—102.2%,相对标准偏差小于1.50%。本法简便、快速、准确度高,已用于钽合金产品的检验。     

The shape of data and probability measures

We introduce the notion of multiscale covariance tensor fields (CTF) associated with Euclidean random variables as a gateway to the shape of their distributions. Multiscale CTFs quantify variation of the data about every point in the data landscape at all spatial scales, unlike the usual covariance tensor that only quantifies global variation about the mean. Empirical forms of localized covariance previously have been used in data analysis and visualization, for example, in local principal component analysis, but we develop a framework for the systematic treatment of theoretical questions and mathematical analysis of computational models. We prove strong stability theorems with respect to the Wasserstein distance between probability measures, obtain consistency results for estimators, as well as bounds on the rate of convergence of empirical CTFs. These results show that CTFs are robust to sampling, noise and outliers. We provide numerous illustrations of how CTFs let us extract shape from data and also apply CTFs to manifold clustering, the problem of categorizing data points according to their noisy membership in a collection of possibly intersecting smooth submanifolds of Euclidean space. We prove that the proposed manifold clustering method is stable and carry out several experiments to illustrate the method.