Composite materials consisting of nanoparticles incorporated within organic matrices offer a diverse range of possible applications, from toughened polymers to cosmetics and sun screens. The inherent ability of some organic materials to self-assemble into larger structures, in particular block copolymers, can be used to achieve well-defined and tuneable morphologies. We intend to exploit this control to achieve hierarchical ordering of endohedral fullerene species within an organic matrix. If the embedded nanostructures have sufficiently well-defined and robust quantum properties, they may be capable of storing and processing quantum information, thus offering the prospect of outperforming classical computation at a fundamental level. The implementation of a basic quantum logic gate in our structures will require control of the interactions between fullerenes, which in turn depend on their alignment within the organic matrix, and thus serves as a demanding test of the success of our project. We propose to achieve controlled alignment of spin-active fullerene species with well defined morphologies, by exploiting self-assembly in organic matrices, within the general context of controlling the hierarchical morphology of polymer nanocomposites. To achieve this, we shall use block copolymers, cyclodextrins and calixarenes. Block copolymers have well-defined nanophase behaviour which has already led to their use in fabricating nanopatterns by lithographic templates. They are also being investigated as systems capable of ordering nanoparticulate inclusions. To achieve controlled alignment of the fullerenes, it is essential that they become fully integrated into the self-assembled structure of the matrix material. We shall follow two approaches: the first is engineering a segregation of the fullerenes into a defined phase, as are often present in block copolymers. The second is to encapsulate fullerene dimers within smaller organic units such as bis-cyclodextrins and bis-calixarenes, which subsequently self-assemble into ordered structures. We shall use a range of techniques to evaluate the development of these techniques, including nuclear magnetic resonance (NMR) and low-voltage (LV) and high resolution (HR) transmission electron microscopy (TEM), and electron spin resonance (ESR) of spin active fullerene dimer molecules acting as alignment probes. Once our alignment strategy has been optimised, we shall demonstrate the exquisite control we have achieved in the resulting nanocomposite by using it to show coherent manipulation of interacting spin systems. The electron spin within certain endohedral fullerenes is an ideal manifestation of quantum information, due to its extremely robust nature and ability to be accurately manipulated. The dipolar interaction between such spins can then be exploited to demonstrate fundamental concepts such as entanglement, and a controlled-NOT operation between spins. Such an interaction is dependent on the orientation of the spin pair with respect to an applied external field. Using an asymmetric fullerene dimer with an individually addressable electron spin trapped in each fullerene unit, we shall have full control over a system of two coupled electron/nuclear spin pairs, capable of embodying up to four or more quantum bits (qubits). We intend to demonstrate quantum entanglement between the electron spins, and consequently a simple quantum computation such as the Deutsch-Josza algorithm. Finally, we shall attempt the same demonstration with the longer lived nuclear spins, in this case using the electron spins to distribute the entanglement. This ambitious experiment places strong demands on our ability to fabricate oriented arrays of functional nanocomposites, and thus forms a compelling demonstration of our new technology.

Overview: Understanding the state of soil and key soil parameters (stress level, stiffness, permeability, strength) is essential to inform effective and efficient decisions about how humans should interact with soil deposits. Challenges associated with obtaining undisturbed samples mean that probes that can measure these properties in-situ are incredibly useful. Informed by recent prototyping work at the Georgia Institute of Technology, the team will develop a self-propelled Burrowing Robot with an Integrated Sensor System (BRISS). The BRISS design will build upon the strength of the well-established cone penetration in-situ test and exploit recent developments in robotics, bio-inspired engineering, numerical modeling and machine learning. The research objectives identified as necessary to achieve this goal are to: (i) Design, build and deploy a robotized sensor delivery system in the soil, and model the borrowing process; (ii) Sense mechanical and physical signals during the burrowing process and adapt the soil exploration using machine-learning; (iii) Interpret the recorded signals with innovative particulate mechanics, tribology, large deformation continuum mechanics models and feature selection algorithms. An inter-disciplinary team of scholars from the Georgia Institute of Technology (GT) and Imperial College London (ICL) will collaborate to achieve these objectives. The team will co-advise a cohort of graduate students and postdoctoral researchers. They will actively engage with each other via video conferencing, workshops and mutual visits.

Many networks such as communication, social media, covert and criminal networks have event-driven dynamics where the intensity rate of the events is changed according to the historical number of events in the network. In particular, events generated by a node of the network may increase or decrease the intensity of other nodes depending on their causal relationship. Such a network structure is called a causal or influence network of which the links represent the directional influence between nodes. The proposed research offers the prospect of a sequential data assimilation tool for inference of influence network from a time-series of count data. The outcome will provide an insight into the complex interaction in which events generated by a node in the network could change the intensity rate of other nodes. For example, in the context of crime hot spots, crime occurrences in some spatial locations could increase the crime rate in others through complex reactions of criminals in each area over crime events. Linkages between crime occurrences in spatial locations can be represented as a complex network where each link is weighted by the strength of the influence of crime events from one location to another. The structure of the influence network will inform rationale strategies for proactive policing. The research outcome can also be used in other similar application contexts (e.g. opinion networks in social science, earthquake networks, terrorist networks, or healthcare networks) where complex influence structure is of interest.

Please, see the attachment with the entire proposal.