Synthesis of Robust Hierarchical Silica Monoliths by Surface-Mediated Solution/Precipitation Reactions over Different Scales: Designing Capillary Microreactors for Environmental Applications

A synthetic procedure to prepare novel materials (surface-mediated fillings) based on robust hierarchical monoliths is reported. The methodology includes the deposition of a (micro- or mesoporous) silica thin film on the support followed by growth of a porous monolithic SiO2 structure. It has been demonstrated that this synthesis is viable for supports of different chemical nature with different inner diameters without shrinkage of the silica filling. The formation mechanism of the surface-mediated fillings is based on a solution/precipitation process and the anchoring of the silica filling to the deposited thin film. The interaction between the two SiO2 structures (monolith and thin film) depends on the porosity of the thin film and yields composite materials with different mechanical stability. By this procedure, capillary microreactors have been prepared and have been proved to be highly active and selective in the total and preferential oxidation of carbon monoxide (TOxCO and PrOxCO).

Core-shell nanocomposites prepared by hierarchical co-assembly: the role of the carbon shell in catalytic wet peroxide oxidation processes

The diffraction pattern of Fe3O4 (not shown) confirmed the presence of only one phase, corresponding to magnetite with a lattice parameter a = 8.357 Å and a crystallite size of 16.6 ± 0.2 nm. The diffraction pattern of MGNC (not shown) confirmed the presence of a graphitic phase, in addition to the metal phase, suggesting that Fe3O4 nanoparticles were successfully encapsulated within a graphitic structure during the synthesis of MGNC. The core-shell structure of MGNC is unequivocally demonstrated in the TEM micrograph shown in Fig. 1b. Characterization of the MGNC textural and surface chemical properties revealed: (i) stability up to 400 oC under oxidizing atmosphere; (ii) 27.3 wt.% of ashes (corresponding to the mass fraction of Fe3O4); (iii) a micro-mesoporous structure with a fairly well developed specific surface area (SBET = 330 m2 g-1); and (iv) neutral character (pHPZC = 7.1). In addition, the magnetic nature of MGNC (Fig. 2) is an additional advantage for possible implementation of in situ magnetic separation systems for catalyst recovery.

Revising the theoretical structure of personality and applying it to the world of work

Most modern models of personality are hierarchical, perhaps as a result of their development by means of exploratory factor analysis. Based on new ideas about the structure of personality and how it divides into biologically based and sociocognitively based components (as proposed by Carver, Cloninger, EUiot and Thrash, and ReveUe), I develop a series of rules that show how scales of personality may be linked from those that are most distal to those which are most proximal. I use SEM to confirm the proposed structure in scales of the Temperament Character Inventory (TCI) and the Eysenck Personality Profiler. Good fit is achieved and all proposed paths are significant. The model is then used to predict work performance, deviance and job satisfacdon.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Clustering web documents using hierarchical representation with multi-granularity

Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with “false correlation”. In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a twophase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problemresulted from the sparse term-paragraphmatrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerancerough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score.

Hierarchical nanoporous solid base catalysts for biofuels

Novel macroporous solid bases have been developed as alternative clean technologies to existing commercial homogeneous catalysts for the production of biodiesel from triglycerides; the latter suffer process disadvantages including complex separation and associated saponification and engine corrosion, and are unsuitable for continuous operation. To this end, tuneable macroporous MgAl hydrotalcites have been prepared by an alkali-free route and characterised by TGA, XRD, SEM and XPS. The macropore architecture improves diffusion of bulky triglyceride molecules to the active base sites, increasing activity. Lamellar and macroporous hydrotalcites will be compared for the transesterification of both model and plant oil feedstocks, and structure-reactivity relations identified.

Systems Analysis: the Structure-and-Purpose Approach Based on Logic-linguistic Formalisation

Systems analysis (SA) is widely used in complex and vague problem solving. Initial stages of SA are analysis of problems and purposes to obtain problems/purposes of smaller complexity and vagueness that are combined into hierarchical structures of problems(SP)/purposes(PS). Managers have to be sure the PS and the purpose realizing system (PRS) that can achieve the PS-purposes are adequate to the problem to be solved. However, usually SP/PS are not substantiated well enough, because their development is based on a collective expertise in which logic of natural language and expert estimation methods are used. That is why scientific foundations of SA are not supposed to have been completely formed. The structure-and-purpose approach to SA based on a logic-and-linguistic simulation of problems/purposes analysis is a step towards formalization of the initial stages of SA to improve adequacy of their results, and also towards increasing quality of SA as a whole. Managers of industrial organizing systems using the approach eliminate logical errors in SP/PS at early stages of planning and so they will be able to find better decisions of complex and vague problems.

Hierarchical, ultrathin single-crystal nanowires of CdS conveniently produced in laser-induced thermal field

Hierarchical nanowires (HNWs) exhibit unique properties and have wide applications, while often suffering from imperfect structure. Herein, we report a facile strategy toward ultrathin CdS HNWs with monocrystal structure, where a continuous-wave (CW) Nd:YAG laser is employed to irradiate an oleic acid (OA) solution containing precursors and a light absorber. The high heating rate and large temperature gradient generated by the CW laser lead to the rapid formation of tiny zinc-blende CdS nanocrystals which then line up into nanowires with the help of OA molecules. Next, the nanowires experience a phase transformation from zinc-blende to wurtzite structure, and the transformation-induced stress creates terraces on their surface, which promotes the growth of side branches and eventually results in monocrystal HNWs with an ultrathin diameter of 24 nm. The one-step synthesis of HNWs is conducted in air and completes in just 40 s, thus being very simple and rapid. The prepared CdS HNWs display photocatalytic performance superior to their nanoparticle counterparts, thus showing promise for catalytic applications in the future.

Modifying the hierarchical porosity of SBA-15 via mild-detemplation followed by secondary treatments

Fenton-chemistry-based detemplation combined with secondary treatments offers options to tune the hierarchical porosity of SBA-15. This approach has been studied on a series of SBA-15 mesophases and has been compared to the conventional calcination. The as-synthesized and detemplated materials were studied with regard to their template content (TGA, CHN), structure (SAXS, TEM), surface hydroxylation (Blin-Carterets approach), and texture (high-resolution argon physisorption). Fenton detemplation achieves 99% of template removal, leading to highly hydroxylated materials. The structure is better preserved when a secondary treatment is applied after the Fenton oxidation, due to the intense capillary forces during drying in water. Two successful approaches are presented: drying in a low-surface-tension solvent (such as n-BuOH) and a hydrothermal stabilization to further condense the structure and make it structurally more robust. Both approaches give rise to remarkably low structural shrinkage, lower than calcination and the direct water-dried Fenton. Interestingly, the derived textural features are remarkably different. The n-BuOH exchange route gives rise to highly hierarchical structures with enhanced interconnecting pores and the highest surface areas. The hydrothermal stabilization produces large-pore SBA-15 structures with high pore volume, intermediate interconnectivity, and minimal micropores. Therefore, the hierarchical texture can be fine-tuned in these two fashions while the template is removed under mild conditions.

Tobacco use prevention education (TUPE) programs in the state of Florida: Teachers' perceptions of program structure and effectiveness

This study described teacher perceptions of TUPE program effectiveness in Florida in an attempt to improve programs by identifying factors that might influence teacher motivation and performance. Very little work has been done to examine how teachers' perceptions are related to the effectiveness of TUPE programs. A statewide survey provided information about how teachers' perceptions of program effectiveness are affected by variables such as: program structure, barriers, tobacco use norms, and training variables. Data were obtained from a telephone survey conducted in Florida as part of the Tobacco Pilot Project (TPP). The sample included 296 middle school teachers and 282 high school teachers as well as 193 middle school principals and 190 high school principals. Correlational and hierarchical regression analyses identified correlates and predictors of teachers' ratings of effectiveness. Results suggest that use of peer leaders, more frequent evaluations, a higher degree of parent involvement, fewer barriers, greater student interest, and lower tolerance for tobacco use were correlated with higher ratings of program effectiveness. Furthermore, student interest, peer, staff, and community tolerance norms, peer leaders, program evaluation, and parent involvement predicted middle school teachers' perceptions. Parent tolerance, student interest, number of barriers, and more frequent program evaluation predicted high school teachers' perceptions. In addition, middle school teachers who reported a lower number of factors negatively associated with teacher receptivity were more likely to view TUPE programs more favorably than teachers who reported a greater number of these risk factors. This relationship was not as robust among the high school teacher sample. Differences between the middle and high school sample were found in the magnitude and number of significant correlations, the proportion of variance accounted for by predictor variables, and the strength of the relationship between the number of factors negatively associated with teacher receptivity and teachers' perceptions of TUPE effectiveness. These findings highlighted the importance of the timing, program features, and the external environment for enhancing or minimizing teachers' ratings of TUPE program effectiveness. In conclusion, significant increases in TUPE teachers' self-efficacy will occur through the participation of peers, parents, staff, and community leaders in different aspects of TUPE programs. ^

Applying stable isotopes to examine food-web structure: an overview of analytical tools

Stable isotope analysis has emerged as one of the primary means for examining the structure and dynamics of food webs, and numerous analytical approaches are now commonly used in the field. Techniques range from simple, qualitative inferences based on the isotopic niche, to Bayesian mixing models that can be used to characterize food-web structure at multiple hierarchical levels. We provide a comprehensive review of these techniques, and thus a single reference source to help identify the most useful approaches to apply to a given data set. We structure the review around four general questions: (1) what is the trophic position of an organism in a food web?; (2) which resource pools support consumers?; (3) what additional information does relative position of consumers in isotopic space reveal about food-web structure?; and (4) what is the degree of trophic variability at the intrapopulation level? For each general question, we detail different approaches that have been applied, discussing the strengths and weaknesses of each. We conclude with a set of suggestions that transcend individual analytical approaches, and provide guidance for future applications in the field.

The role of impurities in the shape, structure and physical properties of semiconducting oxide nanostructures grown by thermal evaporation

A thermal evaporation method developed in the research group enables to grow and design several morphologies of semiconducting oxide nanostructures, such as Ga_2O_3, GeO_2 or Sb_2O_3, among others, and some ternary oxide compounds (ZnGa_2O_4, Zn_2GeO_4). In order to tailor physical properties, a successful doping of these nanostructures is required. However, for nanostructured materials, doping may affect not only their physical properties, but also their morphology during the thermal growth process. In this paper, we will show some examples of how the addition of impurities may result into the formation of complex structures, or changes in the structural phase of the material. In particular, we will consider the addition of Sn and Cr impurities into the precursors used to grow Ga_2O_3, Zn_2GeO_4 and Sb_2O_3 nanowires, nanorods or complex nanostructures, such as crossing wires or hierarchical structures. Structural and optical properties were assessed by electron microscopy (SEM and TEM), confocal microscopy, spatially resolved cathodoluminescence (CL), photoluminescence, and Raman spectroscopies. The growth mechanisms, the luminescence bands and the optical confinement in the obtained oxide nanostructures will be discussed. In particular, some of these nanostructures have been found to be of interest as optical microcavities. These nanomaterials may have applications in optical sensing and energy devices.

E-service quality:development of a hierarchical model

Using survey data from 358 online customers, the study finds that the e-service quality construct conforms to the structure of a third-order factor model that links online service quality perceptions to distinct and actionable dimensions, including (1) website design, (2) fulfilment, (3) customer service, and (4) security/privacy. Each dimension is found to consist of several attributes that define the basis of e-service quality perceptions. A comprehensive specification of the construct, which includes attributes not covered in existing scales, is developed. The study contrasts a formative model consisting of 4 dimensions and 16 attributes against a reflective conceptualization. The results of this comparison indicate that studies using an incorrectly specified model overestimate the importance of certain e-service quality attributes. Global fit criteria are also found to support the detection of measurement misspecification. Meta-analytic data from 31,264 online customers are used to show that the developed measurement predicts customer behavior better than widely used scales, such as WebQual and E-S-Qual. The results show that the new measurement enables managers to assess e-service quality more accurately and predict customer behavior more reliably.

A hierarchical approach to defining marine heatwaves

Marine heatwaves (MHWs) have been observed around the world and are expected to increase in intensity and frequency under anthropogenic climate change. A variety of impacts have been associated with these anomalous events, including shifts in species ranges, local extinctions and economic impacts on seafood industries through declines in important fishery species and impacts on aquaculture. Extreme temperatures are increasingly seen as important influences on biological systems, yet a consistent definition of MHWs does not exist. A clear definition will facilitate retrospective comparisons between MHWs, enabling the synthesis and a mechanistic understanding of the role of MHWs in marine ecosystems. Building on research into atmospheric heatwaves, we propose both a general and specific definition for MHWs, based on a hierarchy of metrics that allow for different data sets to be used in identifying MHWs. We generally define a MHW as a prolonged discrete anomalously warm water event that can be described by its duration, intensity, rate of evolution, and spatial extent. Specifically, we consider an anomalously warm event to be a MHW if it lasts for five or more days, with temperatures warmer than the 90th percentile based on a 30-year historical baseline period. This structure provides flexibility with regard to the description of MHWs and transparency in communicating MHWs to a general audience. The use of these metrics is illustrated for three 21st century MHWs; the northern Mediterranean event in 2003, the Western Australia ‘Ningaloo Niño’ in 2011, and the northwest Atlantic event in 2012. We recommend a specific quantitative definition for MHWs to facilitate global comparisons and to advance our understanding of these phenomena.