Experimental and theoretical investigation of ligand effects on the synthesis of ZnO nanoparticles

ZnO nanoparticles with highly controllable particle sizes(less than 10 nm) were synthesized using organic capping ligands in Zn(Ac)2 ethanolic solution. The molecular structure of the ligands was found to have significant influence on the particle size. The multi-functional molecule tris(hydroxymethyl)-aminomethane (THMA) favoured smaller particle distributions compared with ligands possessing long hydrocarbon chains that are more frequently employed. The adsorption of capping ligands on ZnnOn crystal nuclei (where n = 4 or 18 molecular clusters of(0001) ZnO surfaces) was modelled by ab initio methods at the density functional theory (DFT) level. For the molecules examined, chemisorption proceeded via the formation of Zn...O, Zn...N, or Zn...S chemical bonds between the ligands and active Zn2+ sites on ZnO surfaces. The DFT results indicated that THMA binds more strongly to the ZnO surface than other ligands, suggesting that this molecule is very effective at stabilizing ZnO nanoparticle surfaces. This study, therefore, provides new insight into the correlation between the molecular structure of capping ligands and the morphology of metal oxide nanostructures formed in their presence

Surfactant-Assisted in situ Chemical Etching for the General Synthesis of ZnO Nanotubes Array

In this paper, a general low-cost and substrate-independent chemical etching strategy is demonstrated for the synthesis of ZnO nanotubes array. During the chemical etching, the nanotubes array inherits many features from the preformed nanorods array, such as the diameter, size distribution, and alignment. The preferential etching along c axis and the surfactant protection to the lateral surfaces are considered responsible for the formation of ZnO nanotubes. This surfactant-assisted chemical etching strategy is highly expected to advance the research in the ZnO nanotube-based technology

Synthesis and characterization of hybrid nanostructures

There has been significant interest in the development of multicomponent nanocrystals formed by the assembly of two or more different materials with control over size, shape, composition, and spatial orientation. In particular, the selective growth of metals on the tips of semiconductor nanorods and wires can act to couple the electrical and optical properties of semiconductors with the unique properties of various metals. Here, we outline our progress on the solution-phase synthesis of metal-semiconductor heterojunctions formed by the growth of Au, Pt, or other binary catalytic metal systems on metal (Cd, Pb, Cu)-chalcogenide nanostructures. We show the ability to grow the metal on various shapes (spherical, rods, hexagonal prisms, and wires). Furthermore, manipulating the composition of the metal nanoparticles is also shown, where PtNi and PtCo alloys are our main focus. The magnetic and electrical properties of the developed hybrid nanostructures are shown

Selective Synthesis of Fe2O3 and Fe3O4 Nanowires Via a Single Precursor: A General Method for Metal Oxide Nanowires

Hematite (α-Fe2O3) and magnetite (Fe3O4) nanowires with the diameter of about 100 nm and the length of tens of micrometers have been selectively synthesized by a microemulsion-based method in combination of the calcinations under different atmosphere. The effects of the precursors, annealing temperature, and atmosphere on the morphology and the structure of the products have been investigated. Moreover, Co3O4 nanowires have been fabricated to confirm the versatility of the method for metal oxide nanowires

Insulin Glargine in the Intensive Care Unit: A Model-Based Clinical Trial Design

Online 4 Oct 2012Introduction: Current succesful AGC (Accurate Glycemic Control) protocols require extra clinical effort and are impractical in less acute wards where patients are still susceptible to stress-induced hyperglycemia. Long-acting insulin Glargine has the potential to be used in a low effort controller. However, potential variability in efficacy and length of action, prevent direct in-hospital use in an AGC framework for less acute wards. Method: Clinically validated virtual trials based on data from stable ICU patients from the SPRINT cohort who would be transferred to such an approach are used to develop a 24-hour AGC protocol robust to different Glargine potencies (1.0x, 1.5x and 2.0x regular insulin) and initial dose sizes (dose = total insulin over prior 12, 18 and 24 hours). Glycemic control in this period is provided only by varying nutritional inputs. Performance is assessed as %BG in the 4.0-8.0mmol/L band and safety by %BG<4.0mmol/L. Results: The final protocol consisted of Glargine bolus size equal to insulin over the previous 18 hours. Compared to SPRINT there was a 6.9% - 9.5% absolute decrease in mild hypoglycemia (%BG<4.0mmol/L) and up to a 6.2% increase in %BG between 4.0 and 8.0mmol/L. When the efficacy is known (1.5x assumed) there were reductions of: 27% BG measurements, 59% insulin boluses, 67% nutrition changes, and 6.3% absolute in mild hypoglycemia. Conclusion: A robust 24-48 clinical trial has been designed to safely investigate the efficacy and kinetics of Glargine as a first step towards developing a Glargine-based protocol for less acute wards. Ensuring robustness to variability in Glargine efficacy significantly affects the performance and safety that can be obtained

Ordered Sets in the Calculus of Data Structures

Our goal is to identify families of relations that are useful for reasoning about software. We describe such families using decidable quantifier-free classes of logical constraints with a rich set of operations. A key challenge is to define such classes of constraints in a modular way, by combining multiple decidable classes. Working with quantifierfree combinations of constraints makes the combination agenda more realistic and the resulting logics more likely to be tractable than in the presence of quantifiers. Our approach to combination is based on reducing decidable fragments to a common class, Boolean Algebra with Presburger Arithmetic (BAPA). This logic was introduced by Feferman and Vaught in 1959 and can express properties of uninterpreted sets of elements, with set algebra operations and equicardinality relation (consequently, it can also express Presburger arithmetic constraints on cardinalities of sets). Combination by reduction to BAPA allows us to obtain decidable quantifierfree combinations of decidable logics that share BAPA operations. We use the term Calculus of Data Structures to denote a family of decidable constraints that reduce to BAPA. This class includes, for example, combinations of formulas in BAPA, weak monadic second-order logic of k-successors, two-variable logic with counting, and term algebras with certain homomorphisms. The approach of reduction to BAPA generalizes the Nelson-Oppen combination that forms the foundation of constraint solvers used in software verification. BAPA is convenient as a target for reductions because it admits quantifier elimination and its quantifier-free fragment is NP-complete. We describe a new member of the Calculus of Data Structures: a quantifier-free fragment that supports 1) boolean algebra of finite and infinite sets of real numbers, 2) linear arithmetic over real numbers, 3) formulas that can restrict chosen set or element variables to range over integers (providing, among others, the power of mixed integer arithmetic and sets of integers), 4) the cardinality operators, stating whether a given set has a given finite cardinality or is infinite, 5) infimum and supremum operators on sets. Among the applications of this logic are reasoning about the externally observable behavior of data structures such as sorted lists and priority queues, and specifying witness functions for the BAPA synthesis problem. We describe an abstract reduction to BAPA for our logic, proving that the satisfiability of the logic is in NP and that it can be combined with the other fragments of the Calculus of Data Structures