HYBRID STAGED THERMOLYSIS TO VALORISE BIOMASS

The need for a renewable ‘green’ chemistry is adamant because of the adverse effects of the increasing use of fossil fuels on our society, like global warming and the depletion of fossil fuel resources. Therefore, the use of ligno-cellulosic (woody) types of biomass as a renewable source for chemicals and energy is increasingly becoming important. Unfortunately, the heterogeneity of biomass presents a major obstacle to chemical utilization. The main constituents hemicellulose, cellulose and lignin are strongly interconnected by a variety of physico-chemical bonds that makes it difficult to extract individual chemicals in high yields. So an efficient and cost-effective fractionation technology to cleanly split the biomass into its main constituents is a valuable asset. It opens up the possibility to treat each constituent separately, using dedicated conversion technologies to get specific target chemicals. Thermolysis is a heat-treatment option to convert the biomass into chemicals according to differences in thermochemical stability between the main biomass constituents. However, since these thermochemical stabilities –at least partially- overlap, a careful selection of thermolysis process conditions is necessary to ensure a selective degradation of the chosen biomass constituent. At the same time a premature degradation of the other fractions should be prevented. Main parameters are temperature, heating rate, residence times, reaction medium and the application of catalysts. At present there is a lack of integrated processes that valorise the whole of the biomass in a cost-effective manner. The synergistic combination of aqua-thermolysis (heat-treatment in water at elevated pressure) and pyrolysis (thermal degradation in the absence of oxygen) is a promising thermolysis option in which the fractionation of the lignocellulosic biomass is integrated with the production of valuable chemicals. Preliminary non-catalytic experiments with beech, poplar and spruce wood and wheat straw have indicated the potential of this combination of aqua-thermolysis and pyrolysis to valorise lignocellulosic biomass. The relatively low-temperature (150 – 250°C) aqua-thermolysis focusses on the production of e.g. furfural from exclusively the hemicellulose fraction of the biomass. In a subsequent (fast) pyrolysis of the hemicellulose-free residu at slightly higher temperatures (300 – 500°C), the cellulose fraction can be selectively depolymerised into e.g. levoglucosan, a promising chemical building block for a variety of products. Finally, the resulting refractory lignin-residu (char) may be used as fuel, soil-improver or converted into monomeric phenols by an oxidative pyrolysis. Up to 7 weight percent (dry base) of furfural, 10 weight percent (dry base) of levoglucosan and 20 weight percent (dry base) of char have been obained in a two-step process consisting of a batch-type aqua-thermolysis in an autoclave and a bubbling fluidised bed (fast) pyrolysis. The use of specific catalysts is considered to boost these yields. Another important issue that will be addressed, is the separation of the target chemicals from the crude product mixture. Finally, to better understand the relations between the chemical changes in the biomass during the course of the integrated process and type and amount of the resulting chemical products that are formed an extensive C13-solid state NMR study has been conducted

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data

Wigner crystals in two-dimensional transition-metal dichalcogenides: Spin physics and readout

Wigner crystals are prime candidates for the realization of regular electron lattices under minimal requirements on external control and electronics. However, several technical challenges have prevented their detailed experimental investigation and applications to date. We propose an implementation of two-dimensional electron lattices for quantum simulation of Ising spin systems based on self-assembled Wigner crystals in transition-metal dichalcogenides. We show that these semiconductors allow for minimally invasive all-optical detection schemes of charge ordering and total spin. For incident light with optimally chosen beam parameters and polarization, we predict a strong dependence of the transmitted and reflected signals on the underlying lattice periodicity, thus revealing the charge order inherent in Wigner crystals. At the same time, the selection rules in transition-metal dichalcogenides provide direct access to the spin degree of freedom via Faraday rotation measurements.Comment: 15 pages, 12 figure

Non-locality of non-Abelian anyons

Topological systems, such as fractional quantum Hall liquids, promise to successfully combat environmental decoherence while performing quantum computation. These highly correlated systems can support non-Abelian anyonic quasiparticles that can encode exotic entangled states. To reveal the non-local character of these encoded states we demonstrate the violation of suitable Bell inequalities. We provide an explicit recipe for the preparation, manipulation and measurement of the desired correlations for a large class of topological models. This proposal gives an operational measure of non-locality for anyonic states and it opens up the possibility to violate the Bell inequalities in quantum Hall liquids or spin lattices.Comment: 7 pages, 3 figure

From spin to anyon notation: The XXZ Heisenberg model as a D3D_{3} (or su(2)4su(2)_{4}) anyon chain

We discuss a relationship between certain one-dimensional quantum spin chains and anyon chains. In particular we show how the XXZ Heisenberg chain is realised as a $D_{3}$ (alternately $su(2)_{4}$) anyon model. We find the difference between the models lie primarily in choice of boundary condition.Comment: 13 page

Defect mediated melting and the breaking of quantum double symmetries

In this paper, we apply the method of breaking quantum double symmetries to some cases of defect mediated melting. The formalism allows for a systematic classification of possible defect condensates and the subsequent confinement and/or liberation of other degrees of freedom. We also show that the breaking of a double symmetry may well involve a (partial) restoration of an original symmetry. A detailed analysis of a number of simple but representative examples is given, where we focus on systems with global internal and external (space) symmetries. We start by rephrasing some of the well known cases involving an Abelian defect condensate, such as the Kosterlitz-Thouless transition and one-dimensional melting, in our language. Then we proceed to the non-Abelian case of a hexagonal crystal, where the hexatic phase is realized if translational defects condense in a particular rotationally invariant state. Other conceivable phases are also described in our framework.Comment: 10 pages, 4 figures, updated reference

The modular S-matrix as order parameter for topological phase transitions

We study topological phase transitions in discrete gauge theories in two spatial dimensions induced by the formation of a Bose condensate. We analyse a general class of euclidean lattice actions for these theories which contain one coupling constant for each conjugacy class of the gauge group. To probe the phase structure we use a complete set of open and closed anyonic string operators. The open strings allow one to determine the particle content of the condensate, whereas the closed strings enable us to determine the matrix elements of the modular $S$-matrix, also in the broken phase. From the measured broken $S$-matrix we may read off the sectors that split or get identified in the broken phase, as well as the sectors that are confined. In this sense the modular $S$-matrix can be employed as a matrix valued non-local order parameter from which the low-energy effective theories that occur in different regions of parameter space can be fully determined. To verify our predictions we studied a non-abelian anyon model based on the quaternion group $H=\bar{D_2}$ of order eight by Monte Carlo simulation. We probe part of the phase diagram for the pure gauge theory and find a variety of phases with magnetic condensates leading to various forms of (partial) confinement in complete agreement with the algebraic breaking analysis. Also the order of various transitions is established.Comment: 37 page

Non-Abelian anyonic interferometry with a multi-photon spin lattice simulator

Recently a pair of experiments demonstrated a simulation of Abelian anyons in a spin network of single photons. The experiments were based on an Abelian discrete gauge theory spin lattice model of Kitaev. Here we describe how to use linear optics and single photons to simulate non-Abelian anyons. The scheme makes use of joint qutrit-qubit encoding of the spins and the resources required are three pairs of parametric down converted photons and 14 beam splitters.Comment: 13 pages, 5 figures. Several references added in v

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs