Tailoring Graphene with Metals on Top

We study the effects of metallic doping on the electronic properties of graphene using density functional theory in the local density approximation in the presence of a local charging energy (LDA+U). The electronic properties are sensitive to whether graphene is doped with alkali or transition metals. We estimate the the charge transfer from a single layer of Potassium on top of graphene in terms of the local charging energy of the graphene sheet. The coating of graphene with a non-magnetic layer of Palladium, on the other hand, can lead to a magnetic instability in coated graphene due to the hybridization between the transition-metal and the carbon orbitals.Comment: 5 pages, 4 figure

Mapping Materials and Molecules

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the “big data” revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities. It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses. The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Urine Proteomics Analysis of Patients with Neuronal Ceroid Lipofuscinoses

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of 13 rare neurodegenerative disorders characterised by accumulation of cellular storage bodies. There are few therapeutic options and existing tests do not monitor disease progression and treatment response. However, urine biomarkers could address this need. Proteomic analysis of CLN2 patient urine revealed activation of immune response pathways and pathways associated with the unfolded protein response. Analysis of CLN5 and CLN6 sheep model urine showed subtle changes. To confirm and investigate the relevance of candidate biomarkers a targeted LC-MS/MS proteomic assay was created. We applied this assay to additional CLN2 samples as well as other NCL patients, (CLN1, CLN3, CLN5, CLN6 and CLN7) and demonstrated that Hexosaminidase-A, Aspartate Aminotransferase-1 and LAMP1, are increased in NCL samples and betaine-homocysteine S-methyltransferase-1 was specifically increased in CLN2 patients. These proteins could be used to monitor effectiveness of future therapies aimed at treating systemic NCL disease

Mapping Materials and Molecules.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Active control of strong plasmon-exciton coupling in biomimetic pigment-polymer antenna complexes grown by surface-initiated polymerisation from gold nanostructures

Plexcitonic antenna complexes, inspired by photosynthetic light-harvesting complexes, are formed by attachment of chlorophylls (Chl) to poly(cysteine methacrylate) (PCysMA) scaffolds grown by atom-transfer radical polymerisation from gold nanostructure arrays. In these pigment-polymer antenna complexes, localised surface plasmon resonances on gold nanostructures are strongly coupled to Chl excitons, yielding hybrid light-matter states (plexcitons) that are manifested in splitting of the plasmon band. Modelling of the extinction spectra of these systems using a simple coupled oscillator model indicates that their coupling energies are up to twice as large as those measured for LHCs from plants and bacteria. Coupling energies are correlated with the exciton density in the grafted polymer layer, consistent with the collective nature of strong plasmon-exciton coupling. Steric hinderance in fully-dense PCysMA brushes limits binding of bulky chlorophylls, but the chlorophyll concentration can be increased to ~2M, exceeding that in biological light-harvesting complexes, by controlling the grafting density and polymerisation time. Moreover, synthetic plexcitonic antenna complexes display pH- and temperature-responsiveness, facilitating active control of plasmon-exciton coupling. Because of the wide range of compatible polymer chemistries and the mild reaction conditions, plexcitonic antenna complexes may offer a versatile route to programmable molecular photonic materials

Suppression of electron-electron repulsion and superconductivity in Ultra Small Carbon Nanotubes

Recently, ultra-small-diameter Single Wall Nano Tubes with diameter of $\sim 0.4 nm$ have been produced and many unusual properties were observed, such as superconductivity, leading to a transition temperature $T_c\sim 15^oK$, much larger than that observed in the bundles of larger diameter tubes. By a comparison between two different approaches, we discuss the issue whether a superconducting behavior in these carbon nanotubes can arise by a purely electronic mechanism. The first approach is based on the Luttinger Model while the second one, which emphasizes the role of the lattice and short range interaction, is developed starting from the Hubbard Hamiltonian. By using the latter model we predict a transition temperature of the same order of magnitude as the measured one.Comment: 7 pages, 3 figures, to appear in J. Phys.-Cond. Ma

Local Oxidative and Nitrosative Stress Increases in the Microcirculation during Leukocytes-Endothelial Cell Interactions

Leukocyte-endothelial cell interactions and leukocyte activation are important factors for vascular diseases including nephropathy, retinopathy and angiopathy. In addition, endothelial cell dysfunction is reported in vascular disease condition. Endothelial dysfunction is characterized by increased superoxide (O2•−) production from endothelium and reduction in NO bioavailability. Experimental studies have suggested a possible role for leukocyte-endothelial cell interaction in the vessel NO and peroxynitrite levels and their role in vascular disorders in the arterial side of microcirculation. However, anti-adhesion therapies for preventing leukocyte-endothelial cell interaction related vascular disorders showed limited success. The endothelial dysfunction related changes in vessel NO and peroxynitrite levels, leukocyte-endothelial cell interaction and leukocyte activation are not completely understood in vascular disorders. The objective of this study was to investigate the role of endothelial dysfunction extent, leukocyte-endothelial interaction, leukocyte activation and superoxide dismutase therapy on the transport and interactions of NO, O2•− and peroxynitrite in the microcirculation. We developed a biotransport model of NO, O2•− and peroxynitrite in the arteriolar microcirculation and incorporated leukocytes-endothelial cell interactions. The concentration profiles of NO, O2•− and peroxynitrite within blood vessel and leukocytes are presented at multiple levels of endothelial oxidative stress with leukocyte activation and increased superoxide dismutase accounted for in certain cases. The results showed that the maximum concentrations of NO decreased ∼0.6 fold, O2•− increased ∼27 fold and peroxynitrite increased ∼30 fold in the endothelial and smooth muscle region in severe oxidative stress condition as compared to that of normal physiologic conditions. The results show that the onset of endothelial oxidative stress can cause an increase in O2•− and peroxynitrite concentration in the lumen. The increased O2•− and peroxynitrite can cause leukocytes priming through peroxynitrite and leukocytes activation through secondary stimuli of O2•− in bloodstream without endothelial interaction. This finding supports that leukocyte rolling/adhesion and activation are independent events