The Chemistry of Mycotoxins

Mycotoxins – from the Greek μύκης (mykes, mukos) “fungus” and the Latin (toxicum) “poison” – are a large and growing family of secondary metabolites and hence natural products produced by fungi, in particular by molds (1). It is estimated that well over 1,000 mycotoxins have been isolated and characterized so far, but this number will increase over the next few decades due the availability of more specialized analytical tools and the increasing number of fungi being isolated. However, the most important classes of fungi responsible for these compounds are Alternaria, Aspergillus (multiple forms), Penicillium, and Stachybotrys. The biological activity of mycotoxins ranges from weak and/or sometimes positive effects such as antibacterial activity (e.g. penicillin derivatives derived from Penicillium strains) to strong mutagenic (e.g. aflatoxins, patulin), carcinogenic (e.g. aflatoxins), teratogenic, neurotoxic (e.g. ochratoxins), nephrotoxic (e.g. fumonisins, citrinin), hepatotoxic, and immunotoxic (e.g. ochratoxins, diketopiperazines) activities (1, 2), which are discussed in detail in this volume.

Macromolecular engineering via RAFT chemistry : from sequential to modular design

Sequential Design Molecular Weight Range Functional Monomers: Possibilities, Limits, and Challenges Block Copolymers: Combinations, Block Lengths, and Purities Modular Design End-Group Chemistry Ligation Protocols Conclusions

A combinatorial approach to anonymous membership broadcast

A set system (X, F ) with X= {x 1,...,x m}) and F = {B1...,B n }, where B i ⊆ X, is called an (n, m) cover-free set system (or CF set system) if for any 1 ≤ i, j, k ≤ n and j ≠ k, |B i >2 |B j ∩ B k | +1. In this paper, we show that CF set systems can be used to construct anonymous membership broadcast schemes (or AMB schemes), allowing a center to broadcast a secret identity among a set of users in a such way that the users can verify whether or not the broadcast message contains their valid identity. Our goal is to construct (n, m) CF set systems in which for given m the value n is as large as possible. We give two constructions for CF set systems, the first one from error-correcting codes and the other from combinatorial designs. We link CF set systems to the concept of cover-free family studied by Erdös et al in early 80’s to derive bounds on parameters of CF set systems. We also discuss some possible extensions of the current work, motivated by different application.

Plasma–surface interactions at nanoscales : a combinatorial theoretical, process diagnostics and surface microanalysis approach

This paper introduces an integral approach to the study of plasma-surface interactions during the catalytic growth of selected nanostructures (NSs). This approach involves basic understanding of the plasma-specific effects in NS nucleation and growth, theoretical modelling, numerical simulations, plasma diagnostics, and surface microanalysis. Using an example of plasma-assisted growth of surface-supported single-walled carbon nanotubes, we discuss how the combination of these techniques may help improve the outcomes of the growth process. A specific focus here is on the effects of nanoscale plasma-surface interactions on the NS growth and how the available techniques may be used, both in situ and ex situ to optimize the growth process and structural parameters of NSs.

Size- and orientation-selective Si nanowire growth : thermokinetic effects of naeoscale plasma chemistry

A multiscale, multiphase thermokinetic model is used to show the effective control of the growth orientation of thin Si NWs for nanoelectronic devices enabled by nanoscale plasma chemistry. It is shown that very thin Si NWs with [110] growth direction can nucleate at much lower process temperatures and pressures compared to thermal chemical vapor deposition where [111]-directed Si NWs are predominantly grown. These findings explain a host of experimental results and offer the possibility of energy- and matter-efficient, size- and orientation-controlled growth of [110] Si NWs for next-generation nanodevices.

Nanoscale plasma chemistry enables fast, size-selective nanotube nucleation

The possibility of fast, narrow-size/chirality nucleation of thin single-walled carbon nanotubes (SWCNTs) at low, device-tolerant process temperatures in a plasma-enhanced chemical vapor deposition (CVD) is demonstrated using multiphase, multiscale numerical experiments. These effects are due to the unique nanoscale reactive plasma chemistry (NRPC) on the surfaces and within Au catalyst nanoparticles. The computed three-dimensional process parameter maps link the nanotube incubation times and the relative differences between the incubation times of SWCNTs of different sizes/chiralities to the main plasma- and precursor gas-specific parameters and explain recent experimental observations. It is shown that the unique NRPC leads not only to much faster nucleation of thin nanotubes at much lower process temperatures, but also to better selectivity between the incubation times of SWCNTs with different sizes and chiralities, compared to thermal CVD. These results are used to propose a time-programmed kinetic approach based on fast-responding plasmas which control the size-selective, narrow-chirality nucleation and growth of thin SWCNTs. This approach is generic and can be used for other nanostructure and materials systems.

Made-to-order nanocarbons through deterministic plasma nanotechnology

Through a combinatorial approach involving experimental measurement and plasma modelling, it is shown that a high degree of control over diamond-like nanocarbon film sp3/sp2 ratio (and hence film properties) may be exercised, starting at the level of electrons (through modification of the plasma electron energy distribution function). Hydrogenated amorphous carbon nanoparticle films with high percentages of diamond-like bonds are grown using a middle-frequency (2 MHz) inductively coupled Ar + CH4 plasma. The sp3 fractions measured by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy in the thin films are explained qualitatively using sp3/sp2 ratios 1) derived from calculated sp3 and sp2 hybridized precursor species densities in a global plasma discharge model and 2) measured experimentally. It is shown that at high discharge power and lower CH4 concentrations, the sp3/sp2 fraction is higher. Our results suggest that a combination of predictive modeling and experimental studies is instrumental to achieve deterministically grown made-to-order diamond-like nanocarbons suitable for a variety of applications spanning from nano-magnetic resonance imaging to spin-flip quantum information devices. This deterministic approach can be extended to graphene, carbon nanotips, nanodiamond and other nanocarbon materials for a variety of applications

Effect of elemental composition and size on electron confinement in self-assembled SiC quantum dots : a combinatorial approach

A high level of control over quantum dot (QD) properties such as size and composition during fabrication is required to precisely tune the eventual electronic properties of the QD. Nanoscale synthesis efforts and theoretical studies of electronic properties are traditionally treated quite separately. In this paper, a combinatorial approach has been taken to relate the process synthesis parameters and the electron confinement properties of the QDs. First, hybrid numerical calculations with different influx parameters for Si1-x Cx QDs were carried out to simulate the changes in carbon content x and size. Second, the ionization energy theory was applied to understand the electronic properties of Si1-x Cx QDs. Third, stoichiometric (x=0.5) silicon carbide QDs were grown by means of inductively coupled plasma-assisted rf magnetron sputtering. Finally, the effect of QD size and elemental composition were then incorporated in the ionization energy theory to explain the evolution of the Si1-x Cx photoluminescence spectra. These results are important for the development of deterministic synthesis approaches of self-assembled nanoscale quantum confinement structures.

Colloidal semiconductor nanocrystals : controlled synthesis and surface chemistry in organic media

Colloidal semiconductor nanocrystals (CS-NCs) possess compelling benefits of low-cost, large-scale solution processing, and tunable optoelectronic properties through controlled synthesis and surface chemistry engineering. These merits make them promising candidates for a variety of applications. This review focuses on the general strategies and recent developments of the controlled synthesis of CS-NCs in terms of crystalline structure, particle size, dominant exposed facet, and their surface passivation. Highlighted are the organic-media based synthesis of metal chalcogenide (including cadmium, lead, and copper chalcogenide) and metal oxide (including titanium oxide and zinc oxide) nanocrystals. Current challenges and thus future opportunities are also pointed out in this review.

Tunable electric field enhancement and redox chemistry on TiO2 island films via covalent attachment to Ag or Au nanostructures

Abstract Ag-TiO2 and Au-TiO2 hybrid electrodes were designed by covalent attachment of TiO2 nanoparticles to Ag or Au electrodes via an organic linker. The optical and electronic properties of these systems were investigated using the cytochrome b5 (Cyt b5) domain of sulfite oxidase, exclusively attached to the TiO2 surface, as a Raman marker and model redox enzyme. Very strong SERR signals of Cyt b 5 were obtained for Ag-supported systems due to plasmonic field enhancement of Ag. Time-resolved surface-enhanced resonance Raman spectroscopic measurements yielded a remarkably fast electron transfer kinetic (k = 60 s -1) of Cyt b5 to Ag. A much lower Raman intensity was observed for Au-supported systems with undefined and slow redox behavior. We explain this phenomenon on the basis of the different potential of zero charge of the two metals that largely influence the electronic properties of the TiO2 island film. © 2013 American Chemical Society.

Further development of discrete computational techniques for calculation of restricted diffusion propagators in porous media

Magnetic resonance is a well-established tool for structural characterisation of porous media. Features of pore-space morphology can be inferred from NMR diffusion-diffraction plots or the time-dependence of the apparent diffusion coefficient. Diffusion NMR signal attenuation can be computed from the restricted diffusion propagator, which describes the distribution of diffusing particles for a given starting position and diffusion time. We present two techniques for efficient evaluation of restricted diffusion propagators for use in NMR porous-media characterisation. The first is the Lattice Path Count (LPC). Its physical essence is that the restricted diffusion propagator connecting points A and B in time t is proportional to the number of distinct length-t paths from A to B. By using a discrete lattice, the number of such paths can be counted exactly. The second technique is the Markov transition matrix (MTM). The matrix represents the probabilities of jumps between every pair of lattice nodes within a single timestep. The propagator for an arbitrary diffusion time can be calculated as the appropriate matrix power. For periodic geometries, the transition matrix needs to be defined only for a single unit cell. This makes MTM ideally suited for periodic systems. Both LPC and MTM are closely related to existing computational techniques: LPC, to combinatorial techniques; and MTM, to the Fokker-Planck master equation. The relationship between LPC, MTM and other computational techniques is briefly discussed in the paper. Both LPC and MTM perform favourably compared to Monte Carlo sampling, yielding highly accurate and almost noiseless restricted diffusion propagators. Initial tests indicate that their computational performance is comparable to that of finite element methods. Both LPC and MTM can be applied to complicated pore-space geometries with no analytic solution. We discuss the new methods in the context of diffusion propagator calculation in porous materials and model biological tissues.

Second-order quantile methods for experts and combinatorial games

We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.

Investigation into chemistry of new particle formation and growth in subtropical urban environment

The role of different chemical compounds, particularly organics, involved in the new particle formation (NPF) and its consequent growth are not fully understood. Therefore, this study was conducted to investigate the chemistry of aerosol particles during NPF events in an urban subtropical environment. Aerosol chemical composition was measured along with particle number size distribution (PNSD) and several other air quality parameters at five sites across an urban subtropical environment. An Aerodyne compact Time-of-Flight Aerosol Mass Spectrometer (c-TOF-AMS) and a TSI Scanning Mobility Particle Sizer (SMPS) measured aerosol chemical composition and PNSD, respectively. Five NPF events, with growth rates in the range 3.3-4.6 nm, were detected at two sites. The NPF events happened on relatively warmer days with lower humidity and higher solar radiation. Temporal percent fractions of nitrate, sulphate, ammonium and organics were modelled using the Generalised Additive Model (GAM), with a basis of penalised spline. Percent fractions of organics increased after the NPF events, while the mass fraction of ammonium and sulphate decreased. This uncovered the important role of organics in the growth of newly formed particles. Three organic markers, factors f43, f44 and f57, were calculated and the f44 vs f43 trends were compared between nucleation and non-nucleation days. f44 vs f43 followed a different pattern on nucleation days compared to non-nucleation days, whereby f43 decreased for vehicle emission generated particles, while both f44 and f43 decreased for NPF generated particles. It was found for the first time that vehicle generated and newly formed particles cluster in different locations on f44 vs f43 plot and this finding can be used as a tool for source apportionment of measured particles.