Graphene-Wrapped Sulfur Particles as a Rechargeable Lithium-Sulfur-Battery Cathode Material with High Capacity and Cycling Stability

We report the synthesis of a graphene-sulfur composite material by wrapping polyethyleneglycol (PEG) coated submicron sulfur particles with mildly oxidized graphene oxide sheets decorated by carbon black nanoparticles. The PEG and graphene coating layers are important to accommodating volume expansion of the coated sulfur particles during discharge, trapping soluble polysulfide intermediates and rendering the sulfur particles electrically conducting. The resulting graphene-sulfur composite showed high and stable specific capacities up to ~600mAh/g over more than 100 cycles, representing a promising cathode material for rechargeable lithium batteries with high energy density.Comment: published in Nano Letter

Glass polymorphism in glycerol–water mixtures: I. A computer simulation study

We perform out-of-equilibrium molecular dynamics (MD) simulations of water–glycerol mixtures in the glass state. Specifically, we study the transformations between low-density (LDA) and high-density amorphous (HDA) forms of these mixtures induced by compression/decompression at constant temperature. Our MD simulations reproduce qualitatively the density changes observed in experiments. Specifically, the LDA–HDA transformation becomes (i) smoother and (ii) the hysteresis in a compression/ decompression cycle decreases as T and/or glycerol content increase. This is surprising given the fast compression/decompression rates (relative to experiments) accessible in MD simulations. We study mixtures with glycerol molar concentration wg = 0–13% and find that, for the present mixture models and rates, the LDA–HDA transformation is detectable up to wg E 5%. As the concentration increases, the density of the starting glass (i.e., LDA at approximately wg r 5%) rapidly increases while, instead, the density of HDA remains practically constant. Accordingly, the LDA state and hence glass polymorphism become inaccessible for glassy mixtures with approximately wg 4 5%. We present an analysis of the molecular-level changes underlying the LDA–HDA transformation. As observed in pure glassy water, during the LDA-to- HDA transformation, water molecules within the mixture approach each other, moving from the second to the first hydration shell and filling the first interstitial shell of water molecules. Interestingly, similar changes also occur around glycerol OH groups. It follows that glycerol OH groups contribute to the density increase during the LDA–HDA transformation. An analysis of the hydrogen bond (HB)-network of the mixtures shows that the LDA–HDA transformation is accompanied by minor changes in the number of HBs of water and glycerol. Instead, large changes in glycerol and water coordination numbers occur. We also perform a detailed analysis of the effects that the glycerol force field (FF) has on our results. By comparing MD simulations using two different glycerol models, we find that glycerol conformations indeed depend on the FF employed. Yet, the thermodynamic and microscopic mechanisms accompanying the LDA–HDA transformation and hence, our main results, do not. This work is accompanied by an experimental report where we study the glass polymorphism in glycerol–water mixtures prepared by isobaric cooling at 1 ba

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed