A transmission electron microscopy and X-ray photoelectron spectroscopy study of annealing induced gamma-phase nucleation, clustering, and interfacial dynamics in reactively sputtered amorphous alumina thin films

Pure alpha-Al2O3 exhibits a very high degree of thermodynamical stability among all metal oxides and forms an inert oxide scale in a range of structural alloys at high temperatures. We report that amorphous Al2O3 thin films sputter deposited over crystalline Si instead show a surprisingly active interface. On annealing, crystallization begins with nuclei of a phase closely resembling gamma-Alumina forming almost randomly in an amorphous matrix, and with increasing frequency near the substrate/film interface. This nucleation is marked by the signature appearance of sharp (400) and (440) reflections and the formation of a diffuse diffraction halo with an outer maximal radius of approximate to 0.23 nm enveloping the direct beam. The microstructure then evolves by a cluster-coalescence growth mechanism suggestive of swift nucleation and sluggish diffusional kinetics, while locally the Al ions redistribute slowly from chemisorbed and tetrahedral sites to higher anion coordinated sites. Chemical state plots constructed from XPS data and simple calculations of the diffraction patterns from hypothetically distorted lattices suggest that the true origins of the diffuse diffraction halo are probably related to a complex change in the electronic structure spurred by the a-gamma transformation rather than pure structural disorder. Concurrent to crystallization within the film, a substantially thick interfacial reaction zone also builds up at the film/substrate interface with the excess Al acting as a cationic source. (C) 2015 AIP Publishing LLC.

Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory

We propose a new approach to clustering. Our idea is to map cluster formation to coalition formation in cooperative games, and to use the Shapley value of the patterns to identify clusters and cluster representatives. We show that the underlying game is convex and this leads to an efficient biobjective clustering algorithm that we call BiGC. The algorithm yields high-quality clustering with respect to average point-to-center distance (potential) as well as average intracluster point-to-point distance (scatter). We demonstrate the superiority of BiGC over state-of-the-art clustering algorithms (including the center based and the multiobjective techniques) through a detailed experimentation using standard cluster validity criteria on several benchmark data sets. We also show that BiGC satisfies key clustering properties such as order independence, scale invariance, and richness.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.

Spectral Clustering with Jensen-type kernels and their multi-point extensions

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multipoint' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point extension of the linear (dot-product) kernel and show the existence of cubic time tensor flattening algorithm in this case. Finally, we illustrate the usefulness of our contributions using standard data sets and image segmentation tasks.

Bimodality and re-entrant behaviour in the hierarchical self-assembly of polymeric nanoparticles

We show that a film of a suspension of polymer grafted nanoparticles on a liquid substrate can be employed to create two-dimensional nanostructures with a remarkable variation in the pattern length scales. The presented experiments also reveal the emergence of concentration-dependent bimodal patterns as well as re-entrant behaviour that involves length scales due to dewetting and compositional instabilities. The experimental observations are explained through a gradient dynamics model consisting of coupled evolution equations for the height of the suspension film and the concentration of polymer. Using a Flory-Huggins free energy functional for the polymer solution, we show in a linear stability analysis that the thin film undergoes dewetting and/or compositional instabilities depending on the concentration of the polymer in the solution. We argue that the formation via `hierarchical self-assembly' of various functional nanostructures observed in different systems can be explained as resulting from such an interplay of instabilities.

Delineation of homogeneous temperature regions: a two-stage clustering approach

Homogeneous temperature regions are necessary for use in hydrometeorological studies. The regions are often delineated by analysing statistics derived from time series of maximum, minimum or mean temperature, rather than attributes influencing temperature. This practice cannot yield meaningful regions in data-sparse areas. Further, independent validation of the delineated regions for homogeneity in temperature is not possible, as temperature records form the basis to arrive at the regions. To address these issues, a two-stage clustering approach is proposed in this study to delineate homogeneous temperature regions. First stage of the approach involves (1) determining correlation structure between observed temperature over the study area and possible predictors (large-scale atmospheric variables) influencing the temperature and (2) using the correlation structure as the basis to delineate sites in the study area into clusters. Second stage of the approach involves analysis on each of the clusters to (1) identify potential predictors (large-scale atmospheric variables) influencing temperature at sites in the cluster and (2) partition the cluster into homogeneous fuzzy temperature regions using the identified potential predictors. Application of the proposed approach to India yielded 28 homogeneous regions that were demonstrated to be effective when compared to an alternate set of 6 regions that were previously delineated over the study area. Intersite cross-correlations of monthly maximum and minimum temperatures in the existing regions were found to be weak and negative for several months, which is undesirable. This problem was not found in the case of regions delineated using the proposed approach. Utility of the proposed regions in arriving at estimates of potential evapotranspiration for ungauged locations in the study area is demonstrated.

Differential binding of ppGpp and pppGpp to E-coli RNA polymerase: photo-labeling and mass spectral studies

(p) ppGpp, a secondary messenger, is induced under stress and shows pleiotropic response. It binds to RNA polymerase and regulates transcription in Escherichia coli. More than 25 years have passed since the first discovery was made on the direct interaction of ppGpp with E. coli RNA polymerase. Several lines of evidence suggest different modes of ppGpp binding to the enzyme. Earlier cross-linking experiments suggested that the beta-subunit of RNA polymerase is the preferred site for ppGpp, whereas recent crystallographic studies pinpoint the interface of beta'/omega-subunits as the site of action. With an aim to validate the binding domain and to follow whether tetra-and pentaphosphate guanosines have different location on RNA polymerase, this work was initiated. RNA polymerase was photo-labeled with 8-azido-ppGpp/8-azido-pppGpp, and the product was digested with trypsin and subjected to mass spectrometry analysis. We observed three new peptides in the trypsin digest of the RNA polymerase labeled with 8-azido-ppGpp, of which two peptides correspond to the same pocket on beta'-subunit as predicted by X-ray structural analysis, whereas the third peptide was mapped on the beta-subunit. In the case of 8-azido-pppGpp-labeled RNA polymerase, we have found only one cross-linked peptide from the beta'-subunit. However, we were unable to identify any binding site of pppGpp on the beta-subunit. Interestingly, we observed that pppGpp at high concentration competes out ppGpp bound to RNA polymerase more efficiently, whereas ppGpp cannot titrate out pppGpp. The competition between tetraphosphate guanosine and pentaphosphate guanosine for E. coli RNA polymerase was followed by gel-based assay as well as by a new method known as DRaCALA assay.

Nanomechanical Mapping, Hierarchical Polymer Dynamics, and Miscibility in the Presence of Chain-End Grafted Nanoparticles

To improve the spatial distribution of nano particles in a polymeric host and to enhance the interfacial interaction with the host, the use of chain-end grafted nanoparticle has gained popularity in the field of polymeric nanocomposites. Besides changing the material properties of the host, these grafted nanoparticles strongly alter the dynamics of the polymer chain at both local and cooperative length scales (relaxations) by manipulating the enthalpic and entropic interactions. It is difficult to map the distribution of these chain-end grafted nanoparticles in the blend by conventional techniques, and herein, we attempted to characterize it by unique technique(s) like peak force quantitative nanomechanical mapping (PFQNM) through AFM (atomic force microscopy) imaging and dielectric relaxation spectroscopy (DRS). Such techniques, besides shedding light on the spatial distribution of the nanoparticles, also give critical information on the changing elasticity at smaller length scales and hierarchical polymer chain dynamics in the vicinity of the nanoparticles. The effect of one-dimensional rodlike multiwall carbon nanotubes (MWNTs), with the characteristic dimension of the order of the radius of gyration of the polymeric chain, on the phase miscibility and chain dynamics in a classical LCST mixture of polystyrene/ poly(vinyl methyl ether) (PS/PVME) was examined in detail using the above techniques. In order to tune the localization of the nanotubes, different molecular weights of PS (13, 31, and 46 kDa), synthesized using RAFT (reversible addition fragmentation chain transfer) polymerization, was grafted onto MWNTs in situ. The thermodynamic miscibility in the blends was assessed by low-amplitude isochronal temperature sweeps, the spatial distribution of MWNTs in the blends was evaluated by PFQNM, and the hierarchical polymer chain dynamics was studied by DRS. It was observed that the miscibility, concentration fluctuation, and cooperative relaxations of the PS/PVME blends are strongly governed by the spatial distribution of MWNTs in the blends. These findings should help guide theories and simulations of hierarchical chain dynamics in LCST mixtures containing rodlike nanoparticles.

Nanomechanical Mapping, Hierarchical Polymer Dynamics, and Miscibility in the Presence of Chain-End Grafted Nanoparticles

To improve the spatial distribution of nano particles in a polymeric host and to enhance the interfacial interaction with the host, the use of chain-end grafted nanoparticle has gained popularity in the field of polymeric nanocomposites. Besides changing the material properties of the host, these grafted nanoparticles strongly alter the dynamics of the polymer chain at both local and cooperative length scales (relaxations) by manipulating the enthalpic and entropic interactions. It is difficult to map the distribution of these chain-end grafted nanoparticles in the blend by conventional techniques, and herein, we attempted to characterize it by unique technique(s) like peak force quantitative nanomechanical mapping (PFQNM) through AFM (atomic force microscopy) imaging and dielectric relaxation spectroscopy (DRS). Such techniques, besides shedding light on the spatial distribution of the nanoparticles, also give critical information on the changing elasticity at smaller length scales and hierarchical polymer chain dynamics in the vicinity of the nanoparticles. The effect of one-dimensional rodlike multiwall carbon nanotubes (MWNTs), with the characteristic dimension of the order of the radius of gyration of the polymeric chain, on the phase miscibility and chain dynamics in a classical LCST mixture of polystyrene/ poly(vinyl methyl ether) (PS/PVME) was examined in detail using the above techniques. In order to tune the localization of the nanotubes, different molecular weights of PS (13, 31, and 46 kDa), synthesized using RAFT (reversible addition fragmentation chain transfer) polymerization, was grafted onto MWNTs in situ. The thermodynamic miscibility in the blends was assessed by low-amplitude isochronal temperature sweeps, the spatial distribution of MWNTs in the blends was evaluated by PFQNM, and the hierarchical polymer chain dynamics was studied by DRS. It was observed that the miscibility, concentration fluctuation, and cooperative relaxations of the PS/PVME blends are strongly governed by the spatial distribution of MWNTs in the blends. These findings should help guide theories and simulations of hierarchical chain dynamics in LCST mixtures containing rodlike nanoparticles.