Shape-controlled continuous synthesis of metal nanostructures

A segmented flow-based microreactor is used for the continuous production of faceted nanocrystals. Flow segmentation is proposed as a versatile tool to manipulate the reduction kinetics and control the growth of faceted nanostructures; tuning the size and shape. Switching the gas from oxygen to carbon monoxide permits the adjustment in nanostructure growth from 1D (nanorods) to 2D (nanosheets). CO is a key factor in the formation of Pd nanosheets and Pt nanocubes; operating as a second phase, a reductant, and a capping agent. This combination confines the growth to specific structures. In addition, the segmented flow microfluidic reactor inherently has the ability to operate in a reproducible manner at elevated temperatures and pressures whilst confining potentially toxic reactants, such as CO, in nanoliter slugs. This continuous system successfully synthesised Pd nanorods with an aspect ratio of 6; thin palladium nanosheets with a thickness of 1.5 nm; and Pt nanocubes with a 5.6 nm edge length, all in a synthesis time as low as 150 s.

Collabowrighting the hyper(play)text: a postdramatic digital poetics

The aim of this research is to exhibit how literary playtexts can evoke multisensory trends prevalent in 21st century theatre. In order to do so, it explores a range of practical forms and theoretical contexts for creating participatory, site-specific and immersive theatre. With reference to literary theory, specifically to semiotics, reader-response theory, postmodernism and deconstruction, it attempts to revise dramatic theory established by Aristotle’s Poetics. Considering Gertrude Stein’s essay, Plays (1935), and relevant trends in theatre and performance, shaped by space, technology and the everchanging role of the audience member, a postdramatic poetics emerges from which to analyze the plays of Mac Wellman and Suzan-Lori Parks. Distinguishing the two textual lives of a play as the performance playtext and the literary playtext, it examines the conventions of the printed literary playtext, with reference to models of practice that radicalize the play form, including works by Mabou Mines, The Living Theatre and Fiona Templeton. The arguments of this practice-led Ph.D. developed out of direct engagement with the practice project, which explores the multisensory potential of written language when combined with hypermedia. The written thesis traces the development process of a new play, Rumi High, which is presented digitally as a ‘hyper(play)text,’ accessible through the Internet at www.RumiHigh.org. Here, ‘playwrighting’ practice is expanded spatially, collaboratively and textually. Plays are built, designed and crafted with many layers of meaning that explore both linguistic and graphic modes of poetic expression. The hyper(play)text of Rumi High establishes playwrighting practice as curatorial, where performance and literary playtexts are in a reciprocal relationship. This thesis argues that digital writing and reading spaces enable new approaches to expressing the many languages of performance, while expanding the collaborative network that produces the work. It questions how participatory forms of immersive and site-specific theatre can be presented as interactive literary playtexts, which enable the reader to have a multisensory experience. Through a reflection on process and an evaluation of the practice project, this thesis problematizes notions of authorship and text.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.