Selective synthesis of 4-thiomethyl-1,3-dioxolan-2-ones under microwave irradiation using an environmentally benign KF/Al2O3/PEG-400 system

Research on Chemical Intermediates - We describe a simple and rapid method for selective synthesis of 4-thiomethyl-1,3-dioxolan-2-ones promoted by KF/Al2O3 using PEG-400 as solvent under microwave...     

Catalytic synthesis of biodiesel from high free fatty acid-containing feedstocks

Recyclable and reusable heterogeneous diarylammonium catalysts are highly effective in catalyzing the esterification of the free fatty acid (FFA) present in greases to methyl esters to reduce the FFA content from 12-40 wt% to 0.5-1 wt%; the resulting ester-glyceride mixture (pretreated grease) could then be readily converted to methyl esters by base-catalyzed transesterification.     

Preparation and coordination complex of the first imine-bridged tetrathiafulvalene-pyridine donor ligand

The first imine-bridged pyridyltetrathiafulvalene building block (TTF-CH=N-Py, 1) has been synthesized via the Schiff base condensation of formyltetrathiafulvalene and 2-aminopyridine. The preparation, X-ray crystal structure, electrochemical and magnetic characterization of a 1:1 copper complex [CuII(hfac)2(TTF-CH=N-Py)] (2) are reported. The crystal structure reveals that the imine N atom participates in chelation to the paramagnetic center, thus making this ligand an attractive precursor for the assembly of pi-d systems.     

Comparative Study using Raman and Visible Spectroscopy of Cretan Extra Virgin Olive Oil Adulteration with Sunflower Oil

The adulteration of extra virgin olive oil with low-quality and inexpensive seed oil is a serious problem in the industry. In recent years, the characterization of extra virgin olive oil adulteration with various techniques has been successfully implemented. In this work, a comparative study of Raman and visible spectroscopy is presented. These methods are rapid, noninvasive, and no sample pretreatment is required. We used both methods to study Cretan extra virgin olive oil adulterated with sunflower oil. Statistical analysis based on partial least square regression was used to determine the detection limits of the methods. Raman spectroscopy was superior in comparison to visible spectroscopy with adulteration detection limits of 3.5 and 5.5%, respectively, for the same samples. These results indicate that both techniques are suitable for olive oil quality control.     

Multicenter ligand transformations of thioureas on ruthenium clusters

The reaction of trirutheniumdodecacarbonyl with various thioureas was found to give, depending upon the substituents and the reaction conditions, a large variety of tri-, tetra-, penta-, and hexanuclear ruthenium clusters. The ligand systems observed in the products are derived from the thioureas employed; the fragmentation of the thiourea molecules involve N-H, C-S, C-H, and C-N activation processes.     

Self-assembled free-floating nanomaterials from sequence-defined polymers

Sequence-defined polymers can be programmed to self-assemble into precise nanostructures for applications in biosensing, drug delivery, optics, and molecular computation. Inspired by the natural self-assembly processes present in biological protein and DNA systems, sets of molecular design rules have emerged across materials classes as instructions to build a variety of tunable structures. This review highlights recent advances in self-assembled sequence-defined and sequence-specific polymers across peptides, peptoids, DNA, and non-biological synthetic materials, with a focus on synthesis, assembly processes and overall structure. Specifically, these self-assembled structures are free-floating, as such constructs can potentially serve as a platform for the aforementioned applications. Emphasis is placed on the molecular design of polymers that self-assemble into zero-dimensional, one-dimensional, two-dimensional, or three-dimensional nanostructures. With the development of automated syntheses and increasing control over self-assembly, future work may focus on emerging classes of compatible hybrid materials with exciting directions toward new architectures and applications.     

Mathematics and Science Integration: Models and Characterizations

The squeeze on instructional time and other factors increasingly leads educators to consider mathematics and science integration in an effort to be more efficient and effective. Unfortunately, the need for common understandings for what it means to integrate these disciplines, as well as the need for improving disciplinary knowledge, appears to continue to be significant obstacles to an integrated approach to instruction. In this study we report the results of a survey containing six instructional scenarios administered to thirty-three middle grades science and math teachers. Analysis of teacher responses revealed that while teachers applied similar criteria in their reasoning, they did not possess common characterizations for integration. Furthermore, analysis suggested that content knowledge serves as a barrier to recognizing integrated examples. Implications for professional development planners include the need to develop and provide teachers with constructs and parameters for what constitutes mathematics and science integration. Continued emphasis on improving teacher content knowledge in both mathematics and science is also a prerequisite to enabling teachers to integrate content.     

Sparse Penalized Forward Selection for Support Vector Classification

We propose a new binary classification and variable selection technique especially designed for high-dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an ?₁-type penalty to the loss function, common classification methods such as logistic regression or support vector machines (SVM) can perform variable selection. Existing penalized SVM methods all attempt to jointly solve all the parameters involved in the penalization problem altogether. When data dimension is very high, the joint optimization problem is very complex and involves a lot of memory allocation. In this article, we propose a new penalized forward search technique that can reduce high-dimensional optimization problems to one-dimensional optimization by iterating the selection steps. The new algorithm can be regarded as a forward selection version of the penalized SVM and its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained with intelligent search by exploiting convexity and a piecewise linear or quadratic structure of the criterion function. In each step, the predictor that is most able to predict the outcome is chosen in the model. The search is then repeatedly used in an iterative fashion until convergence occurs. Comparison of our new classification rule with ?₁-SVM and other common methods show very promising performance, in that the proposed method leads to much leaner models without compromising misclassification rates, particularly for high-dimensional predictors.     

On sparse estimation for semiparametric linear transformation models

Semiparametric linear transformation models have received much attention due to their high flexibility in modeling survival data. A useful estimating equation procedure was recently proposed by Chen et al. (2002) [21] for linear transformation models to jointly estimate parametric and nonparametric terms. They showed that this procedure can yield a consistent and robust estimator. However, the problem of variable selection for linear transformation models has been less studied, partially because a convenient loss function is not readily available under this context. In this paper, we propose a simple yet powerful approach to achieve both sparse and consistent estimation for linear transformation models. The main idea is to derive a profiled score from the estimating equation of Chen et al. [21], construct a loss function based on the profile scored and its variance, and then minimize the loss subject to some shrinkage penalty. Under regularity conditions, we have shown that the resulting estimator is consistent for both model estimation and variable selection. Furthermore, the estimated parametric terms are asymptotically normal and can achieve a higher efficiency than that yielded from the estimation equations. For computation, we suggest a one-step approximation algorithm which can take advantage of the LARS and build the entire solution path efficiently. Performance of the new procedure is illustrated through numerous simulations and real examples including one microarray data.