Carbon release by selective alloying of transition metal carbides

We have performed first principles density functional theory calculations on TiC alloyed on the Ti sublattice with 3d transition metals ranging from Sc to Zn. The theory is accompanied with experimental investigations, both as regards materials synthesis as well as characterization. Our results show that by dissolving a metal with a weak ability to form carbides, the stability of the alloy is lowered and a driving force for the release of carbon from the carbide is created. During thin film growth of a metal carbide this effect will favor the formation of a nanocomposite with carbide grains in a carbon matrix. The choice of alloying elements as well as their concentrations will affect the relative amount of carbon in the carbide and in the carbon matrix. This can be used to design the structure of nanocomposites and their physical and chemical properties. One example of applications is as low-friction coatings. Of the materials studied, we suggest the late 3d transition metals as the most promising elements for this phenomenon, at least when alloying with TiC.Comment: 9 pages, 6 figure

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences

Development of Grb2 SH2 Domain Signaling Antagonists: A Potential New Class of Antiproliferative Agents

Aberrant signaling through protein-tyrosine kinase (PTK)-dependent pathways is associated with several proliferative diseases. Accordingly, PTK inhibitors are being developed as new approaches for the treatment of certain cancers. Growth factor receptor bound protein 2 (Grb2) is an important downstream mediator of PTK signaling that serves obligatory roles in many pathogenic processes. One of the primary functions of Grb2 is to bind to specific phosphotyrosyl (pTyr)-containing sequences through its Src homology 2 (SH2) domain. Agents that bind to the Grb2 SH2 domain and prevent its normal function could disrupt associated PTK signaling and serve as alternatives to kinase-directed inhibitors. Starting from the X-ray crystal structure of a lead peptide bound to the Grb2 SH2 domain, this review will summarize important contributions to these efforts. The presentation will be thematically arranged according to the region of peptide modified, proceeding from the N-terminus to the C-terminus, with a special section devoted to aspects of conformational constraint

An ionicity rationale to design solid phase metal nitride reactants for solar ammonia production

Ammonia is an important fertilizer component and could be used as a convenient hydrogen carrier. This work studies a solar thermochemical reaction cycle that separates the reductive N[subscript 2] cleavage from the hydrogenation of nitrogen ions to NH[subscript 3] without using electricity or fossil fuel. The hydrolysis of binary metal nitrides of magnesium, aluminum, calcium, chromium, manganese, zinc, or molybdenum at 0.1 MPa and 200-1000°C recovered up to 100 mol% of the lattice nitrogen with up to 69.9 mol% as NH[subscript 3] liberated at rates of up to 1.45 x 10ˉ³ mol NH[subscript 3] (mol metal)ˉ¹ sˉ¹ for ionic nitrides. These rates and recoveries are encouraging when extrapolated to a full scale process. However, nitrides with lower ionicity are attractive due to simplified reduction conditions to recycle the oxidized reactant after NH[subscript 3] formation. For these materials diffusion in the solid limits the rate of NH3 liberation. The nitride ionicity (9.96-68.83% relative to an ideal ionic solid) was found to correlate with the diffusion constants (6.56 x 10[superscript -14] to 4.05 x 10[superscript -7] cm² sˉ¹) suggesting that the reduction of H[subscript 2]O over nitrides yielding NH[subscript 3] is governed by the activity of the lattice nitrogen or ion vacancies, respectively. The ionicity appears to be a useful rationale when developing an atomic-scale understanding of the solid-state reaction mechanism and when designing prospectively optimized ternary nitrides for producing NH[subscript 3] more sustainably and at mild conditions compared to the Haber Bosch process