Biomimetic and bioinspired silica : recent developments and applications

In a previous review of biological and bioinspired silica formation (S. V. Patwardhan et al., Chem. Commun., 2005, 1113 [ref. 1]), we have identified and discussed the roles that organic molecules (additives) play in silica formation in vitro. Tremendous progress has been made in this field since and this review attempts to capture, with selected examples from the literature, the key advances in synthesising and controlling properties of silica-based materials using bioinspired approaches, i.e. conditions of near-neutral pH, all aqueous environments and room temperature. One important reason to investigate biosilicifying systems is to be able to develop novel materials and/or technologies suitable for a wide range of applications. Therefore, this review will also focus on applications arising from research on biological and bioinspired silica. A range of applications such as in the areas of sensors, coatings, hybrid materials, catalysis and biocatalysis and drug delivery have started appearing. Furthermore, scale-up of this technology suitable for large-scale manufacturing has proven the potential of biologically inspired synthesis

Discovery, applications and scale-up of bioinspired nanomaterials

Inorganic nanomaterials are widely used in industry and in consumer products with a global production of the order of several million tons per annum and worth several $billions. Current methods for nanomaterials synthesis or manufacturing suffer from significant environmental burden leading to high costs and unsustainable production. In contrast, biological organisms, through biomineralisation, produce elaborate and ordered nanomaterials under physiological conditions. Learning from organisms, we have developed green nanomaterials (GN) synthesis (Figure 1).1 This green method (mild, one-pot and rapid synthesis in water, at room temperature and neutral pH) offers substantial reductions in resources, time and energy usage when compared to traditional routes, yet offers excellent control over the properties and function of the materials. This presentation will illustrate how key synthetic parameters were identified systematically using Design of Experiments in order to modulate silica formation, its physicochemical properties and its function. Furthermore, experimental results and techno-economic analysis of manufacturing using this new process will be discussed.2-4 This includes our systematic approach in terms of both process scale-up and process intensification. These results suggested that the process operates well in both batch and continuous mode in tank and tubular reactors. We have also focused on some aspects of downstream processing, in particular, purification of the products, allowing a complete removal of organics, with an added possibility of composition and porosity control. Given that this is a non-destructive method, \u3e90% water and additive can be recycled, further improving the sustainability and economics.4 Please click Additional Files below to see the full abstract

Unified model of phrasal and sentential evidence for information extraction

Journal ArticleInformation Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extraction that jointly considers both the local context around a phrase along with the wider sentential context in a probabilistic framework. Our approach uses a sentential event recognizer and a plausible role-filler recognizer that is conditioned on event sentences. We evaluate our system on two IE data sets and show that our model performs well in comparison to existing IE systems that rely on local phrasal context

Feature subsumption for opinion analysis

Journal ArticleLexical features are key to many approaches to sentiment analysis and opinion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexicosyntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and performance. We use the subsumption hierarchy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing unnecessary features. We show that reducing the feature set improves performance on three opinion classification tasks, especially when combined with traditional feature selection

Effective information extraction with semantic affinity patterns and relevant regions

Journal ArticleWe present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction patterns. We create a self-trained relevant sentence classifier to identify relevant regions, and use a semantic affinity measure to automatically learn domain-relevant extraction patterns. We then distinguish primary patterns from secondary patterns and apply the patterns selectively in the relevant regions. The resulting IE system achieves good performance on the MUC-4 terrorism corpus and ProMed disease outbreak stories. This approach requires only a few seed extraction patterns and a collection of relevant and irrelevant documents for training

Reconstruction of multiplex networks via graph embeddings

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.Comment: 12 pages, 10 figures, 2 table