Structure and Bonding in Stannadiphospholes and their Dianions SnC(2)P(2)R(2)(m) (R=H, tBu m=0,-2): A Comparative Study with C(5)H(5)(+) and C(5)H(5)(-) Analogues

The potential energy surfaces of both neutral and dianionic SnC(2)P(2)R(2) (R=H, tBu) ring systems have been explored at the B3PW91/LANL2DZ (Sn) and 6-311 + G* (other atoms) level. In the neutral isomers the global minimum is a nido structure in which a 1,2-diphosphocyclobutadiene ring (1,2-DPCB) is capped by the Sn. Interestingly, the structure established by Xray diffraction analysis, for R=tBu, is a 1,3-DPCB ring capped by Sn and it is 2.4 kcal mol(-1) higher in energy than the 1,2-DPCB ring isomer. This is possibly related to the kinetic stability of the 1,3-DPCB ring, which might originate from the synthetic precursor ZrCp(2)tBu(2)C(2)P(2). In the case of the dianionic isomers we observe only a 6 pi-electron aromatic structure as the global minimum, similarly to the cases of our previously reported results with other types of heterodiphospholes.([1,4,19]) The existence of large numbers of cluster-type isomers in neutral and 6 pi-planar structures in the dianions SnC(2)P(2)R(2)(2-) (R=H, tBu) is due to 3D aromaticity in neutral clusters and to 2D pi aromaticity of the dianionic rings. Relative energies of positional isomers mainly depend on: 1) the valency and coordination number of the Sn centre, 2) individual bond strengths, and 3) the steric effect of tBu groups. A comparison of neutral stannadiphospholes with other structurally related C(5)H(5)(+) analogues indicates that Sn might be a better isolobal analogue to P(+) than to BH or CH(+). The variation in global minima in these C(5)H(5)(+) analogues is due to characteristic features such as 1) the different valencies of C, B, P and Sn, 2) the electron deficiency of B, 3) weaker p pi-p pi bonding by P and Sn atoms, and 4) the tendency of electropositive elements to donate electrons to nido clusters. Unlike the C5H5+ systems, all C(5)H(5)(-) analogues have 6 pi-planar aromatic structures as global minima. The differences in the relative ordering of the positional isomers and ligating properties are significant and depend on 1) the nature of the pi orbitals involved, and 2) effective overlap of orbitals.

Scalable Context-Sensitive Points-To Analysis Using Multi-Dimensional Bloom Filters

Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointer-pointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.

Thermally induced phase separation in P alpha MSAN/PMMA blends in presence of functionalized multiwall carbon nanotubes: Rheology, morphology and electrical conductivity

The focus of this work is the evaluation and analysis of the state of dispersion of functionalized multiwall carbon nanotubes (CNTs), within different morphologies formed, in a model LCST blend (poly[(alpha-methylstyrene)-co-(acrylonitrile)]/poly(methyl-methacryla te), P alpha MSAN/PMMA). Blend compositions that are expected to yield droplet-matrix (85/15 P alpha MSAN/PMMA and 15/85 P alpha MSAN/PMMA, wt/wt) and co-continuous morphologies (60/40 P alpha MSAN/PMMA, wt/wt) upon phase separation have been combined with two types of CNTs; carboxylic acid functionalized (CNTCOOH) and polyethylene modified (CNTPE) up to 2 wt%. Thermally induced phase separation in the blends has been studied in-situ by rheology and dielectric (conductivity) spectroscopy in terms of morphological evolution and CNT percolation. The state of dispersion of CNTs has been evaluated by transmission electron microscopy. The experimental results indicate that the final blend morphology and the surface functionalization of CNT are the main factors that govern percolation. In presence of either of the CNTs, 60/40 P alpha MSAN/PMMA blends yield a droplet-matrix morphology rather than co-continuous and do not show any percolation. On the other hand, both 85/15 P alpha MSAN/PMMA and 15/85 P alpha MSAN/PMMA blends containing CNTPEs show percolation in the rheological and electrical properties. Interestingly, the conductivity spectroscopy measurements demonstrate that the 15/85 P alpha MSAN/PMMA blends with CNTPEs that show insulating properties at room temperature for the miscible blends reveal highly conducting properties in the phase separated blends (melt state) as a result of phase separation. By quenching this morphology, the conductivity can be retained in the blends even in the solid state. (C) 2011 Elsevier Ltd. All rights reserved.

Model track studies on fouled ballast using ground penetrating radar and multichannel analysis of surface wave

Ballast fouling is created by the breakdown of aggregates or outside contamination by coal dust from coal trains, or from soil intrusion beneath rail track. Due to ballast fouling, the conditions of rail track can be deteriorated considerably depending on the type of fouling material and the degree of fouling. So far there is no comprehensive guideline available to identify the critical degree of fouling for different types of fouling materials. This paper presents the identification of degree of fouling and types of fouling using non-destructive testing, namely seismic surface-wave and ground penetrating radar (GPR) survey. To understand this, a model rail track with different degree of fouling has been constructed in Civil engineering laboratory, University of Wollongong, Australia. Shear wave velocity obtained from seismic survey has been employed to identify the degree of fouling and types of fouling material. It is found that shear wave velocity of fouled ballast increases initially, reaches optimum fouling point (OFP), and decreases when the fouling increases. The degree of fouling corresponding after which the shear wave velocity of fouled ballast will be smaller than that of clean ballast is called the critical fouling point (CFP). Ground penetrating radar with four different ground coupled antennas (500 MHz, 800 MHz, 1.6 GHz and 2.3 GHz) was also used to identify the ballast fouling condition. It is found that the 800 MHz ground coupled antenna gives a better signal in assessing the ballast fouling condition. Seismic survey is relatively slow when compared to GPR survey however it gives quantifiable results. In contrast, GPR survey is faster and better in estimating the depth of fouling. (C) 2011 Elsevier B.V. All rights reserved.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.