Prediction models for the key mechanical properties of EPDM/PP blends as affected by processing parameters and their correlation with stress relaxation and phase morphologies

The optimum condition of processing parameters (mixing temperature, rotor speed, fill factor, and blend ratio) and prediction models for the best key mechanical properties of ethylene propylene diene terpolymer/polypropylene thermoplastic vulcanizates (EPDM/PP TPVs) was investigated by using the Taguchi's optimization technique and data analysis. The results reveal that all of the processing parameters affected significantly the mechanical properties of the EPDM/PP TPVs, but specifically the blend ratio contributed more than 90% in effect size on tensile strength and tension set. There were three main factors, the mixing temperature, the fill factor, and the blend ratio, influencing the elongation at break. Furthermore, the mathematic models were effective and reliable in predicting the properties of TPVs. The correlation of mechanical properties, stress relaxation, and phase morphologies of the TPVs prepared from the predicted models was also investigated. It can be summarized that the morphological structure and stress relaxation of the TPVs were strongly governed by the EPDM content in the blend ratio. That is, the higher the EPDM content, the better phase morphology having smaller size of the vulcanized EPDM particles distributed in the PP matrix and the higher rate of stress relaxation. Moreover, these two properties were then principally pushing the mechanical characteristics of the EPDM/PP TPVs. Copyright © 2015 John Wiley & Sons, Ltd.     

Intermolecular Dynamic Kinetic Resolution Cooperatively Catalyzed by an N‐Heterocyclic Carbene and a Lewis Acid

The ubiquitous structure of δ‐lactones makes the development of new methods for their enantioselective and stereoselective synthesis an important ongoing challenge. The intermolecular dynamic kinetic resolution (DKR) of β‐halo‐α‐ketoesters cooperatively catalyzed by an N‐heterocyclic carbene and a Lewis acid generates two contiguous stereocenters with remarkable diastereoselectivity through an oxidation/lactonization sequence.     

A Combined Experimental and Theoretical Study on the Stereodynamics of Monoaza[5]helicenes: Solvent‐Induced Increase of the Enantiomerization Barrier in 1‐Aza‐[5]helicene

Helicenes and heterohelicenes are attractive compounds with great potential in materials sciences to be used in optoelectronics as ligand backbones in enantioselective catalysis and as chiral sensors. The properties of these materials are related to the stereodynamics of these helical chiral compounds. However, little is known about features controlling stereodynamics in helicenes; in particular, for heterohelicenes the position of the heteroatom could be relevant in this respect. Herein the complete stereodynamic characterization of monoaza[5]helicenes is shown by enantioselective dynamic HPLC and DFT calculations. At variance with previous theoretical calculations, 1‐aza[5]helicene shows a surprisingly high enantiomerization barrier, which is triggered by specific solvent interactions.     

Dynamic Covalent Organocatalysts Discovered from Catalytic Systems through Rapid Deconvolution Screening

The first example of a bifunctional organocatalyst assembled through dynamic covalent chemistry (DCC) is described. The catalyst is based on reversible imine chemistry and can catalyze the Morita–Baylis–Hillman (MBH) reaction of enones with aldehydes or N‐tosyl imines. Furthermore, these dynamic catalysts were shown to be optimizable through a systemic screening approach, in which large mixtures of catalyst structures were generated, and the optimal catalyst could be directly identified by using dynamic deconvolution. This strategy allowed one‐pot synthesis and in situ evaluation of several potential catalysts without the need to separate, characterize, and purify each individual structure. The systems were furthermore shown to catalyze and re‐equilibrate their own formation through a previously unknown thiourea‐catalyzed transimination process.     

Cyclotetrabenzoin: Facile Synthesis of a Shape‐Persistent Molecular Square and Its Assembly into Hydrogen‐Bonded Nanotubes

Cyanide‐catalyzed benzoin condensation of terephthaldehyde produces a cyclic tetramer, which we propose to name cyclotetrabenzoin. Cyclotetrabenzoin is a square‐shaped macrocycle ornamented with four α‐hydroxyketone functionalities pointing away from the central cavity, the dimensions of which are 6.9×6.9 Å. In the solid state, these functional groups extensively hydrogen bond, resulting in a microporous three‐dimensional organic framework with one‐dimensional nanotube channels. This material exhibits permanent—albeit low‐porosity, with a Langmuir surface area of 52 m² g^?1. Cyclotetrabenzoin’s easy and inexpensive synthesis and purification may inspire the creation of other shape‐persistent macrocycles and porous molecular crystals by benzoin condensation.     

Nannocystin A: an Elongation Factor 1 Inhibitor from Myxobacteria with Differential Anti‐Cancer Properties

Cultivation of myxobacteria of the Nannocystis genus led to the isolation and structure elucidation of a class of novel cyclic lactone inhibitors of elongation factor 1. Whole genome sequence analysis and annotation enabled identification of the putative biosynthetic cluster and synthesis process. In biological assays the compounds displayed anti‐fungal and cytotoxic activity. Combined genetic and proteomic approaches identified the eukaryotic translation elongation factor 1α (EF‐1α) as the primary target for this compound class. Nannocystin A ( 1 ) displayed differential activity across various cancer cell lines and EEF1A1 expression levels appear to be the main differentiating factor. Biochemical and genetic evidence support an overlapping binding site of 1 with the anti‐cancer compound didemnin B on EF‐1α. This myxobacterial chemotype thus offers an interesting starting point for further investigations of the potential of therapeutics targeting elongation factor 1.     

Biocatalytic Feedback‐Driven Temporal Programming of Self‐Regulating Peptide Hydrogels

Switchable self‐assemblies respond to external stimuli with a transition between near‐equilibrium states. Although being a key to present‐day advanced materials, these systems respond rather passively, and do not display autonomous dynamics. For autonomous behavior, approaches must be found to orchestrate the time domain of self‐assemblies, which would lead to new generations of dynamic and self‐regulating materials. Herein, we demonstrate catalytic control of the time domain of pH‐responsive peptide hydrogelators in a closed system. We program transient acidic pH states by combining a fast acidic activator with the slow, enzymatic, feedback‐driven generation of a base (dormant deactivator). This transient state can be programmed over orders of magnitude in time. It is coupled to dipeptides to create autonomously self‐regulating, dynamic gels with programmed lifetimes, which are used for fluidic guidance, burst release, and self‐erasing rapid prototyping.     

Interactions of DNA with graphene and sensing applications of graphene field-effect transistor devices: A review

Graphene field-effect transistors (GFET) have emerged as powerful detection platforms enabled by the advent of chemical vapor deposition (CVD) production of the unique atomically thin 2D material on a large scale. DNA aptamers, short target-specific oligonucleotides, are excellent sensor moieties for GFETs due to their strong affinity to graphene, relatively short chain-length, selectivity, and a high degree of analyte variability. However, the interaction between DNA and graphene is not fully understood, leading to questions about the structure of surface-bound DNA, including the morphology of DNA nanostructures and the nature of the electronic response seen from analyte binding. This review critically evaluates recent insights into the nature of the DNA graphene interaction and its affect on sensor viability for DNA, small molecules, and proteins with respect to previously established sensing methods. We first discuss the sorption of DNA to graphene to introduce the interactions and forces acting in DNA based GFET devices and how these forces can potentially affect the performance of increasingly popular DNA aptamers and even future DNA nanostructures as sensor substrates. Next, we discuss the novel use of GFETs to detect DNA and the underlying electronic phenomena that are typically used as benchmarks for characterizing the analyte response of these devices. Finally, we address the use of DNA aptamers to increase the selectivity of GFET sensors for small molecules and proteins and compare them with other, state of the art, detection methods.     

“Parallel factor analysis of multi-excitation ultraviolet resonance Raman spectra for protein secondary structure determination”

Protein secondary structural analysis is important for understanding the relationship between protein structure and function, or more importantly how changes in structure relate to loss of function. The structurally sensitive protein vibrational modes (amide I, II, III and S) in deep-ultraviolet resonance Raman (DUVRR) spectra resulting from the backbone C–O and N–H vibrations make DUVRR a potentially powerful tool for studying secondary structure changes. Experimental studies reveal that the position and intensity of the four amide modes in DUVRR spectra of proteins are largely correlated with the varying fractions of α-helix, β-sheet and disordered structural content of proteins. Employing multivariate calibration methods and DUVRR spectra of globular proteins with varying structural compositions, the secondary structure of a protein with unknown structure can be predicted. A disadvantage of multivariate calibration methods is the requirement of known concentration or spectral profiles. Second-order curve resolution methods, such as parallel factor analysis (PARAFAC), do not have such a requirement due to the “second-order advantage.” An exceptional feature of DUVRR spectroscopy is that DUVRR spectra are linearly dependent on both excitation wavelength and secondary structure composition. Thus, higher order data can be created by combining protein DUVRR spectra of several proteins collected at multiple excitation wavelengths to give multi-excitation ultraviolet resonance Raman data (ME-UVRR). PARAFAC has been used to analyze ME-UVRR data of nine proteins to resolve the pure spectral, excitation and compositional profiles. A three factor model with non-negativity constraints produced three unique factors that were correlated with the relative abundance of helical, β-sheet and poly-proline II dihedral angles. This is the first empirical evidence that the typically resolved “disordered” spectrum represents the better defined poly-proline II type structure.