Stabilization of Therapeutic Proteins

We present results of molecular simulations, quantum mechanical calculations, and experimental data aimed towards the rational design of solvent formulations. In particular, we have found that the rate limitation of oxidation of methionine groups is determined by the breaking of O-O bonds in hydrogen peroxide, not by the rate of acidic catalysis as previously thought. We have used this understanding to design molecular level parameters which are correlated to experimental data. Rate data has been determined both for G-CSF and for hPTH(1-34).Singapore-MIT Alliance (SMA

Normal-State Spin Dynamics and Temperature-Dependent Spin Resonance Energy in an Optimally Doped Iron Arsenide Superconductor

The proximity of superconductivity and antiferromagnetism in the phase diagram of iron arsenides, the apparently weak electron-phonon coupling and the "resonance peak" in the superconducting spin excitation spectrum have fostered the hypothesis of magnetically mediated Cooper pairing. However, since most theories of superconductivity are based on a pairing boson of sufficient spectral weight in the normal state, detailed knowledge of the spin excitation spectrum above the superconducting transition temperature Tc is required to assess the viability of this hypothesis. Using inelastic neutron scattering we have studied the spin excitations in optimally doped BaFe1.85Co0.15As2 (Tc = 25 K) over a wide range of temperatures and energies. We present the results in absolute units and find that the normal state spectrum carries a weight comparable to underdoped cuprates. In contrast to cuprates, however, the spectrum agrees well with predictions of the theory of nearly antiferromagnetic metals, without complications arising from a pseudogap or competing incommensurate spin-modulated phases. We also show that the temperature evolution of the resonance energy follows the superconducting energy gap, as expected from conventional Fermi-liquid approaches. Our observations point to a surprisingly simple theoretical description of the spin dynamics in the iron arsenides and provide a solid foundation for models of magnetically mediated superconductivity.Comment: 8 pages, 4 figures, and an animatio

Magnetic interactions in iron superconductors: A review

High temperature superconductivity in iron pnictides and chalcogenides emerges when a magnetic phase is suppressed. The multi-orbital character and the strength of correlations underlie this complex phenomenology, involving magnetic softness and anisotropies, with Hund's coupling playing an important role. We review here the different theoretical approaches used to describe the magnetic interactions in these systems. We show that taking into account the orbital degree of freedom allows us to unify in a single phase diagram the main mechanisms proposed to explain the (\pi,0) order in iron pnictides: the nesting-driven, the exchange between localized spins, and the Hund induced magnetic state with orbital differentiation. Comparison of theoretical estimates and experimental results helps locate the Fe superconductors in the phase diagram. In addition, orbital physics is crucial to address the magnetic softness, the doping dependent properties, and the anisotropies.Comment: Invited review article for a focus issue of Comptes Rendus Physique: 26 pages, 10 figures. Revised version, as accepted. Small changes throughout the text plus new subsection (Sec. IIIE

Fluorescent nanoparticles for sensing

Nanoparticle-based fluorescent sensors have emerged as a competitive alternative to small molecule sensors, due to their excellent fluorescence-based sensing capabilities. The tailorability of design, architecture, and photophysical properties has attracted the attention of many research groups, resulting in numerous reports related to novel nanosensors applied in sensing a vast variety of biological analytes. Although semiconducting quantum dots have been the best-known representative of fluorescent nanoparticles for a long time, the increasing popularity of new classes of organic nanoparticle-based sensors, such as carbon dots and polymeric nanoparticles, is due to their biocompatibility, ease of synthesis, and biofunctionalization capabilities. For instance, fluorescent gold and silver nanoclusters have emerged as a less cytotoxic replacement for semiconducting quantum dot sensors. This chapter provides an overview of recent developments in nanoparticle-based sensors for chemical and biological sensing and includes a discussion on unique properties of nanoparticles of different composition, along with their basic mechanism of fluorescence, route of synthesis, and their advantages and limitations

Prediction of the radiative heat transfer in small and large scale oxy-coal furnaces

Predicting thermal radiation for oxy-coal combustion highlights the importance of the radiation models for the spectral properties of gases and particles. This study numerically investigates radiation behaviours in small and large scale furnaces through refined radiative property models, using the full-spectrum correlated k (FSCK) model and Mie theory based data, compared with the conventional use of the weighted sum of grey gases (WSGG) model and the constant values of the particle radiation properties. Both oxy-coal combustion and air-fired combustion have been investigated numerically and compared with combustion plant experimental data. Reasonable agreements are obtained between the predicted results and the measured data. Employing the refined radiative property models achieves closer predicted heat transfer properties to the measured data from both furnaces. The gas-phase component of the radiation energy source term obtained from the FSCK property model is higher within the flame region than the values obtained by using the conventional methods. The impact of using non-grey radiation behaviour of gases through the FSCK is enhanced in the large scale furnace as the predicted gas radiation source term is approximately 2-3 times that obtained when using the WSGG, while the same term is in much closer agreement between the FSCK and the WSGG for the pilot-scale furnace. The predicted total radiation source term (from both gases and particles) is lower in the flame region after using the refined models, which results in a hotter flame (approximately 50-150 K higher in this study) compared with results obtained from conventional methods. In addition, the predicted surface incident radiation reduces by using the refined radiative property models for both furnaces, in which the difference is relevant with the difference in the predicted radiation properties between the two modelling techniques. Numerical uncertainties resulting from the influences of combustion model, turbulent particle dispersion and turbulence modelling on the radiation behaviours are discussed

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure