The influence of mixed solvents volatility on charge state distribution of peptides during positive electrospray ionization mass spectrometry

Understanding the mechanisms that control and concentrate the observed electrospray ionisation (ESI) response from peptides is important. Controlling these mechanisms can improve signal-to-noise ratio in the mass spectrum, and enhances the generation of intact ions, and thus, improves the detection of peptides when analysing mixtures. The effects of different mixtures of aqueous: organic solvents (25, 50, 75%; v/v): formic acid solution (at pH 3.26) compositions on the ESI response and chargestate distribution (CSD) during mass spectrometry (MS) were determined in a group of biologically active peptides (molecular wt range 1.3 - 3.3 kDa). The ESI response is dependent on type of organic solvent in the mobile phase mixture and therefore, solvent choice affects optimal ion intensities. As expected, intact peptide ions gave a more intense ESI signal in polar protic solvent mixtures than in the low polarity solvent. However, for four out of the five analysed peptides, neither the ESI response nor the CSD were affected by the volatility of the solvent mixture. Therefore, in solvent mixtures, as the composition changes during the evaporation processes, the pKb of the amino acid composition is a better predictor of multiple charging of the peptides

Investigation of sorption of Cu₂₊, Zn₂₊ and Cd₂₊ ions by a composite adsorbent obtained from bentonite-like clay and hydroxyapatite

The article analyses the results of the measuring of pHpzc (point zero charge) of the surface of adsorbents by potentiometric titration are presente

ASSESSMENT OF DIOXIN CONTAMINATION IN THE ENVIRONMENT AND HUMAN POPULATION IN THE VICINITY OF DIOXIN HOTSPOT IN DA NANG AIRBASE, SOUTH VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Competition between Kondo and RKKY correlations in the presence of strong randomness

We propose that competition between Kondo and magnetic correlations results in a novel universality class for heavy fermion quantum criticality in the presence of strong randomness. Starting from an Anderson lattice model with disorder, we derive an effective local field theory in the dynamical mean-field theory (DMFT) approximation, where randomness is introduced into both hybridization and Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions. Performing the saddle-point analysis in the U(1) slave-boson representation, we reveal its phase diagram which shows a quantum phase transition from a spin liquid state to a local Fermi liquid phase. In contrast with the clean limit of the Anderson lattice model, the effective hybridization given by holon condensation turns out to vanish, resulting from the zero mean value of the hybridization coupling constant. However, we show that the holon density becomes finite when variance of hybridization is sufficiently larger than that of the RKKY coupling, giving rise to the Kondo effect. On the other hand, when the variance of hybridization becomes smaller than that of the RKKY coupling, the Kondo effect disappears, resulting in a fully symmetric paramagnetic state, adiabatically connected with the spin liquid state of the disordered Heisenberg model. .....

LED based lighting and communications: An emerging technology for a greener more sustainable future

The paper discusses on the effect that the growth in our energy consumption as a species is having upon the planet, and how the global lighting and telecommunications industries are major contributors. We demonstrate that through the adoption of LED based lighting combined with visible light communications, substantial economical and power savings by orders of magnitude can be made over existing technologies, contributing towards a greener more sustainable future. The future of LED technology is also discussed with a focus on organic technology, promising increased savings

Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i.e., action) space. Since the set of actions in the discretized semi-MDP can be very large, a straightforward approach based on explicit action-space construction fails to solve even simple instances of the problem. The presented algorithm uses an enhanced policy iteration on symbolic representations of the action space. The soundness of the algorithm is established for parametric ACTMCs with alarm-event distributions satisfying four mild assumptions that are shown to hold for uniform, Dirac and Weibull distributions in particular, but are satisfied for many other distributions as well. An experimental implementation shows that the symbolic technique substantially improves the efficiency of the synthesis algorithm and allows to solve instances of realistic size.Comment: This article is a full version of a paper accepted to the Conference on Quantitative Evaluation of SysTems (QEST) 201