The influence of physical and chemical linkage on the properties of nanocomposites

It has been shown by several groups that the mechanical and electrical behavior of composites changes quite substantially, and often beneficially, when the filler particle size is less than 100 nm in diameter. There is also good reason to believe that the interface between the embedded particulates and the polymer matrix holds the key to understanding the bulk phenomena observed. Materials based on an SiO2-polyolefin system have been formulated with functionalized particulates so as to affect the physical and chemical linkages. The agents used to achieve this include amino-silane, hexamethyl-disilazane and triethoxyvinylsilane. The emerging picture of the interface is supported by detailed dielectric spectroscopy and internal space charge assessment. The nature of the internal structure has been related to the bulk properties observed such as the breakdown strength, voltage endurance, and the measurement of internal charges resulting from interfacial polarization

Growth-Oriented Adjustment Strategies: The Role of Exchange Rate Policies and Trade Liberalization

This paper reviews some of the major strands of the debate on growth-oriented adjustment programmes. Section I looks at recent domestic and external trends in developing countries in order to highlight the motivation for increased emphasis on growth with adjustment. Section II discusses general issues underlying growth-oriented adjustment strategies, drawing upon the G-24 report and subsequent work in the Fund; Section III examines the role of the exchange rate in adjustment; and, Section IV examines the role of trade policies in adjustment. Section V offers some concluding thoughts.

Distribution of Canonical Determinants in QCD

The distribution of canonical determinants in QCD is determined by means of chiral perturbation theory. For a non-zero quark charge the canonical determinants take complex values. In the dilute pion gas approximation, we compute all moments of the magnitude of the canonical determinants, as well as the first nonvanishing moments of the real and imaginary parts. The non-trivial cancellation between the real and the imaginary parts of the canonical determinants is derived and the signal to noise ratio is discussed. The analytical distributions are compared to lattice data. The average density of the magnitude of the canonical determinants is determined as well and is shown to be given by a variant of the log-normal distribution.Comment: 23 pages, 4 figure

Application of finite element modeling and viscoelasticity theory in characterization and prediction of dielectric relaxation process in polymer nanodielectrics

Nanodielectrics, typically defined as polymer composites with nanosized ceramic fillers, have demonstrated significant improvements in electrical endurance, breakdown strength and dielectric constant relative to their constituent materials, which leads to enhanced energy storage capabilities. The key role played by the large interfacial area surrounding nanofillers proves to be essential to the enhancement, yet quantitative models to predict the altered dielectric properties in the interfacial area are rarely seen. In this presentation, we apply a finite element modeling approach, originally developed for viscoelasticity analysis, to predict the frequency and temperature dependence of dielectric permittivity spectra in polymer nanodielectrics containing functionalized silica fillers. The dispersion state of nanofillers in the finite element model is determined from descriptor-based analysis of scanning electron micrographs, and the interfacial area surrounding the fillers is explicitly configured into the geometry. The dielectric permittivity spectra of the polymer matrix are imported into the model using a series of Debye relaxation functions. The analogy between dielectric permittivity and viscoelastic modulus allows for a simple mathematical conversion between the two physically distinct quantities, which enables the usage of Prony Series when fitting the dielectric spectrum. With the assistance of a earlier developed algorithm to fit the viscoelastic modulus, the parameters of Debye relaxation series function are obtained. Using the above morphology and physical property inputs, dielectric spectroscopy experiments over a range of frequencies and temperatures can be simulated. Properties of the interfacial region are obtained through an iterative comparison between model output and experimental results. It is observed that the distribution of dielectric relaxation times of the interface could be expressed using those of the polymer matrix multiplied by frequency shift factors that vary with different functionalization of the silica filler surfaces. Our results indicate that surface energy parameters of the filler and the polymer matrix can vary the dielectric response of the composites, which is consistent with earlier observations of the viscoelastic properties of polymer nanocomposites. Further discussion on the results also provides insight into the underlying dielectric relaxation mechanism in the interfacial area

Impact of solid-electrolyte interphase reformation on capacity loss in silicon-based lithium-ion batteries

High-density silicon composite anodes show large volume changes upon charging/discharging triggering the reformation of the solid electrolyte interface (SEI), an interface initially formed at the silicon surface. The question remains how the reformation process and accompanied material evolution, in particular for industrial up-scalable cells, impacts cell performance. Here, we develop a correlated workflow incorporating X-ray microscopy, field-emission scanning electron microscopy tomography, elemental imaging and deep learning-based microstructure quantification suitable to witness the structural and chemical progression of the silicon and SEI reformation upon cycling. The nanometer-sized SEI layer evolves into a micron-sized silicon electrolyte composite structure at prolonged cycles. Experimental-informed electrochemical modelling endorses an underutilisation of the active material due to the silicon electrolyte composite growth affecting the capacity. A chemo-mechanical model is used to analyse the stability of the SEI/silicon reaction front and to investigate the effects of material properties on the stability that can affect the capacity loss

Free energy for parameterized Polyakov loops in SU(2) and SU(3) lattice gauge theory

We present a study of the free energy of parameterized Polyakov loops P in SU(2) and SU(3) lattice gauge theory as a function of the parameters that characterize P. We explore temperatures below and above the deconfinement transition, and for our highest temperatures T > 5 T_c we compare the free energy to perturbative results.Comment: Minor changes. Final version to appear in JHE

Compression Behavior of Single-layer Graphene

Central to most applications involving monolayer graphene is its mechanical response under various stress states. To date most of the work reported is of theoretical nature and refers to tension and compression loading of model graphene. Most of the experimental work is indeed limited to bending of single flakes in air and the stretching of flakes up to typically ~1% using plastic substrates. Recently we have shown that by employing a cantilever beam we can subject single graphene into various degrees of axial compression. Here we extend this work much further by measuring in detail both stress uptake and compression buckling strain in single flakes of different geometries. In all cases the mechanical response is monitored by simultaneous Raman measurements through the shift of either the G or 2D phonons of graphene. In spite of the infinitely small thickness of the monolayers, the results show that graphene embedded in plastic beams exhibit remarkable compression buckling strains. For large length (l)-to-width (w) ratios (> 0.2) the buckling strain is of the order of -0.5% to -0.6%. However, for l/w <0.2 no failure is observed for strains even higher than -1%. Calculations based on classical Euler analysis show that the buckling strain enhancement provided by the polymer lateral support is more than six orders of magnitude compared to suspended graphene in air

Posterior Association Networks and Functional Modules Inferred from Rich Phenotypes of Gene Perturbations

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the ‘search space’ for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html

Preclinical Efficacy of Endoglin-Targeting Antibody–Drug Conjugates for the Treatment of Ewing Sarcoma

[EN] Endoglin (ENG; CD105) is a coreceptor of the TGFb family that is highly expressed in proliferating endothelial cells. Often coopted by cancer cells, ENG can lead to neo-angiogenesis and vasculogenic mimicry in aggressive malignancies. It exists both as a transmembrane cell surface protein, where it primarily interacts with TGFb, and as a soluble matricellular protein (sENG) when cleaved by matrix metal-loproteinase 14 (MMP14). High ENG expression has been associated with poor prognosis in Ewing sarcoma, an aggressive bone cancer that primarily occurs in adolescents and young adults. However, the therapeutic value of ENG targeting has not been fully explored in this disease. Experimental Design: We characterized the expression pattern of transmembrane ENG, sENG, and MMP14 in preclinical and clinical samples. Subsequently, the antineoplastic potential of two novel ENG-targeting monoclonal antibody–drug conjugates (ADC), OMTX503 and OMTX703, which differed only by their drug payload (nigrin-b A chain and cytolysin, respectively), was assessed in cell lines and preclinical animal models of Ewing sarcoma. Results: Both ADCs suppressed cell proliferation in proportion to the endogenous levels of ENG observed in vitro. Moreover, the ADCs significantly delayed tumor growth in Ewing sarcoma cell line–derived xenografts and patient-derived xenografts in a dose-dependent manner. Conclusions: Taken together, these studies demonstrate potent preclinical activity of first-in-class anti-ENG ADCs as a nascent strategy to eradicate Ewing sarcoma