Polycentric puzzles – emerging mega-city regions seen through the lens of advanced producer services

Polycentric puzzles – emerging mega-city regions seen through the lens of advanced producer service

Rapid, Brushless Self-assembly of a PS-b-PDMS Block Copolymer for Nanolithography

AbstractBlock copolymers (BCP) are highly promising self-assembling precursors for scalable nanolithography. Very regular BCP nanopatterns can be used as on-chip etch masks. The first step in the processing of BCP thin films is usually the chemical modification of the substrate surface, typically by grafting of a brush layer that renders the surface energy neutral relative to the constituent blocks. We provide here a first study on rapid, low temperature self-assembly of PS-b-PDMS (polystyrene-block-polydimethylsiloxane) on silicon substrates without a brush layer. We show that it forms line and antidot patterns after short solvo-thermal annealing. Unlike previous reports on this system, low temperature and short annealing time provide self-assembly in homogeneous thin films covering large substrate areas. This on-chip mask was then used for pattern transfer to the underlying silicon substrate. SEM (scanning electron microscope) images reveal silicon nanowires relative to the PDMS patterns of the BCP mask

Sub-15nm Silicon Lines Fabrication via PS-b-PDMS Block Copolymer Lithography

This paper describes the fabrication of nanodimensioned silicon structures on silicon wafers from thin films of a poly(styrene)-block-poly(dimethylsiloxane) (PS-b-PDMS) block copolymer (BCP) precursor self-assembling into cylindrical morphology in the bulk. The structure alignment of the PS-b-PDMS (33 k–17 k) was conditioned by applying solvent and solvothermal annealing techniques. BCP nanopatterns formed after the annealing process have been confirmed by scanning electron microscope (SEM) after removal of upper PDMS wetting layer by plasma etching. Silicon nanostructures were obtained by subsequent plasma etching to the underlying substrate by an anisotropic dry etching process. SEM images reveal the formation of silicon nanostructures, notably of sub-15 nm dimensions

Enhancing Programming Learning with AI-Generated Contextual Examples in Digital Creativity

Programming concepts are challenging for new learners to grasp. This is especially the case for creative arts students who are typically unfamiliar with computing concepts and the associated vocabulary at enrolment. One means to enhance their learning is to situate examples in a relatable disciplinary context and to adapt learning material accordingly. However, this can be onerous and time-consuming to prepare; particularly in diverse modules that include learners from a wide range of different disciplines. This position paper proposes the use of large language models to automate tailoring the content of adaptive hypermedia systems such as personalised wikis. These tools can re-situate examples into many contexts that learners are already familiar with. A pilot study using ChatGPT (using GPT-4) for a first-stage undergraduate Digital Creativity module is presented. Generative artificial intelligence changes the examples used to illustrate programming concepts according to a student’s course. These examples are evaluated by academic colleagues drawn from the different course teams to rate the generated analogies. Initial results are encouraging, illustrating a high degree of face validity. Further work in 2023-24 will evaluate whether this improves learning during the module

Morphology of Cu clusters supported on reconstructed polar ZnO (0001) and (000[1]) surfaces†

Unbiased Monte Carlo procedures are applied to investigate the structure of Cu clusters of various sizes deposited over reconstructed polar ZnO surfaces. Four distinct reconstructed polar ZnO surfaces (two Zn terminated (0001) reconstructions and two O terminated (000[1 with combining macron]) reconstructions) were investigated, having previously been determined to be the most stable under typical conditions, as revealed by the grand canonical ensemble studies. Random sampling was performed considering ∼400 000 random initial structural configurations of Cu atoms over the ZnO surfaces, with each structure being optimised using interatomic potential techniques, and the most stable resultant structures being refined using a plane-wave DFT approach. The investigation reveals the key role of surface adatoms and vacancies arising from the reconstruction of the polar ZnO surface in determining the morphology of deposited Cu clusters. Strong Cu–Zn interactions play an essential role in Cu cluster growth, with reconstructed polar ZnO surfaces featuring sites with undercoordinated Zn surface atoms promoting highly localised three dimensional Cu cluster morphologies, whist reconstructions featuring undercoordinated O atoms tend to result in more planar Cu clusters, in order to maximise the favourable Cu–Zn interaction. This is the first study that evaluates the thermodynamically most stable Cu/ZnO structures using realistic reconstructed ZnO polar surfaces, and thus provides valuable insights into the factors affecting Cu cluster growth over ZnO surfaces, as well as model catalyst surfaces that can be utilised in future computational studies to explore catalytic activity for key processes such as CO2 and CO hydrogenation to methanol