Spin waves and revised crystal structure of honeycomb iridate Na2IrO3

We report inelastic neutron scattering measurements on Na2IrO3, a candidate for the Kitaev spin model on the honeycomb lattice. We observe spin-wave excitations below 5 meV with a dispersion that can be accounted for by including substantial further-neighbor exchanges that stabilize zigzag magnetic order. The onset of long-range magnetic order below T(N)=15.3 K is confirmed via the observation of oscillations in zero-field muon-spin rotation experiments. Combining single-crystal diffraction and density functional calculations we propose a revised crystal structure model with significant departures from the ideal 90° Ir-O-Ir bonds required for dominant Kitaev exchange.     

Invariant slow manifold approach for exact dynamic inversion of singularly perturbed linear mechanical systems with admissible output constraints

We propose an approach for the exact dynamic inversion of singularly perturbed second-order linear systems through asymptotic expansion in a singular parameter. We show that the inversion solution, corresponding to the invariant slow manifold, can be expressed as a converging infinite series under desired output constraints composed of exponential support functions in the complex domain. We provide systematic mathematical procedures to obtain the closed-form invariant slow manifold, along with required admissible boundary conditions. Numerical examples are given to validate the proposed approach.     

Separation and identification of volatile compounds from liquid cultures of Trichoderma harzianum by GC-MS using three different capillary columns

A simple, fast, repeatable and less laborious sample preparation protocol was developed and applied for the analysis of biocontrol fungus Trichoderma harzianum strain FA1132 by using gas chromatography-mass spectrometry. The match factors for sample spectra with respect to the mass spectra library of fungal volatile compounds were determined and used to study the complex hydrocarbons and other volatile compounds, which were separated by using different capillary columns with nonpolar, medium polar and high polar stationary phases. To date, more than 278 volatile compounds (with spectral match factor at least 90%) such as normal saturated hydrocarbons (C7-C30), cyclohexane, cyclopentane, fatty acids, alcohols, esters, sulfur-containing compounds, simple pyrane and benzene derivatives have been identified. Most of these compounds have not previously been reported. The method described in this paper is a more convenient research tool for the detection of volatile compounds from the cultures of T. harzianum.     

Stripe Order and Spin Dynamics in Nickelates

We report zero field μSR experiments on the lanthanum nickelate La_2−x Sr _x NiO_4+δ system with strontium doping level x between 0 and 0.6. In a single crystal with x=0.33 the internal field distribution shows two separated signals reflecting the antiferromagnetic domains and the non magnetic antiphase domain walls in the spin and charge ordered state below 190 K. Below 70 K a broadening of the low field line is observed. A similar behavior is observed in polycrystalline samples with 0.2≤x≤0.5. This revised version was published online in September 2006 with corrections to the Cover Date.     

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.     

Calculation of molecular weight distribution in a batch thermal polymerization of styrene

Molecular weight averages have long been used as a measure of polymer molecular weight properties in industrial polymer manufacturing processes. With a kinetic model, it is possible to directly calculate the polymer chain length distribution by integrating an infinite number of the polymer population balance equations. However, when the polymer chain length is very large, such a direct integration of polymer population balance equations can be computationally demanding. In this paper, the method of finite molecular weight moments is applied to the calculation of polymer chain length distribution in a batch free radical thermal polymerization of styrene. The weight fraction of a finite chain length interval is directly calculated in conjunction with a kinetic model. The method of calculation is illustrated through model simulations.     

Molecularly imprinted polymers as artificial steroid receptors

The polymers selective to six different steroids (testosterone, Δ⁴-androstene-3,17-dione, 1,4-androstadiene-3,17-dione, β-estradiol, progesterone, testosterone propionate) have been synthesized using molecular imprinting based on noncovalent interactions. Analysis of the influence of structural features of the steroids under study has shown that molecules with a relatively rigid structure and the OH group at C-17 position are the most efficient templates for methacrylic acid-containing imprinted polymers. The chromatographic study of the polymers synthesized has demonstrated a strong dependence of the selectivity and intensity of interaction with analytes on the composition of solvents used both as porogen and chromatographic mobile phase. To obtain polymers with highly selective recognition sites and to create the optimal conditions for molecular recognition, all possible interactions (between template and functional monomer, template and solvent, solvent and functional monomer) should be taken into account. <?TF="palat-i"> The batch rebinding study of testosterone by the imprinted polymer in acetonitrile has revealed some heterogeneity of recognition sites, and permitted determination of K_ass = 1.05 × 10⁴ M ⁻¹, ΔG° = −5.4 kcal/mol and N = 1.2 μmol/g for high-affinity sites and K_ass = 0.33 × 10⁴ M ⁻¹, ΔG° = −4.8 kcal/mol and N = 2.2 μmol/g for low-affinity sites. <?TF="palat-i"> The results obtained show how it is possible to regulate in different modes the molecular recognition by imprinted polymers as well as to fabricate polymers possessing the necessary properties depending on their practical application.© 1998 John Wiley & Sons, Ltd.     

Atomically dispersed Fe atoms anchored on COF-derived N-doped carbon nanospheres as efficient multi-functional catalysts

Non-noble metal isolated single atom site (ISAS) catalysts have attracted much attention due to their low cost, ultimate atom efficiency and outstanding catalytic performance. Herein, atomically dispersed Fe atoms are prepared by a covalent organic framework (COF)-absorption–pyrolysis strategy. The obtained Fe ISASs anchored on COF-derived N-doped carbon nanospheres (Fe-ISAS/CN) served as a multi-functional catalyst in electro-catalysis and organic catalysis, exhibiting better catalytic performance than commercial Pt/C for the ORR with good stability and methanol tolerance. Besides electro-catalysis, the Fe-ISAS/CN also showed outstanding catalytic performance in organic reactions, such as the selective oxidation of ethylbenzene to acetophenone and dehydrogenation of 1,2,3,4-tetrahydroquinoline with excellent reactivity, selectivity, stability and recyclability. Co and Ni ISAS materials can also be prepared by this method, suggesting that it is a general strategy to obtain metal ISAS catalysts. This work will provide new insight into the design of COF-derived metal ISAS multi-functional catalysts for electro-catalysis and organic reactions using rationally designed synthetic routes and the optimized structure of substrates.

Fe isolated single-atom sites anchored on COF-derived N-doped carbon nanospheres as efficient multi-functional catalysts.