CMU

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

United States

Magnetic hyperthermia is a promising treatment for brain and prostate cancers due to the localised nature of the treatment compared to chemo or radiotherapy. Brain cancer in particular is difficult to treat with conventional therapies due to the sensitivity of the surrounding tissue with only a 14% survival rate after 10 years in the UK. Magnetic nanoparticles used in magnetic hyperthermia must be biocompatible and provide efficient and reliable heating, yet their physical complexity is limiting progress towards their clinical use. Complexity arises due to the small size of the particles (10-100 nm) leading to a range of physical properties such as surface and bulk atomic defects, finite size and thermal effects, multiple oxide phases and surface functionalization. All of these properties contribute to the overall magnetic properties but are extremely difficult to predict theoretically or with simple model approaches. Previous simulations have considered only simple approaches to the magnetic properties of individual magnetic nanoparticles and give limited insight into the properties of real nanoparticles. Yet there is an urgent need to understand the relative importance of these effects so that experimental effort can be focused on their control and optimisation to accelerate development of this potentially life saving treatment. This proposal will address this challenge by developing a realistic model of magnetic nanoparticles to understand the role of the surface on the particle properties and the resulting magnetization dynamics used to generate heat during magnetic hyperthermia. The aim of the project is to develop a novel atomic scale magnetic model of magnetite nanocrystals to understand the effects of size, shape and the surface on their equilibrium and dynamic magnetic properties. We will use this information to model and understand how the magnetic particles reverse in an applied magnetic field which is directly related to the amount of heat generated during magnetic hyperthermia. Using atomistic spin dynamics we will be able to simulate the effects of thermal fluctuations at the surface on the effective magnetic properties and their importance in determining the reversal mechanism. The interactions between particles can also play a critical role in the overall magnetic properties, and so we will use our model to simulate the interaction of small clusters of particles with atomic resolution giving new insight into their importance. Finally, we will develop an atomistic model of functional core-shell oxide nanoparticles to determine the optimal magnetic properties for magnetic hyperthermia. The computational methods developed in this project will significantly advance the ability to accurately model magnetic composite materials with wide application in the fields of magnetism and spintronics and made freely available to the community within the open source vampire software package. The results from this project will improve our understanding of the properties of magnetite nanocrystals, guide future research on magnetic hyperthermia and accelerate the development of this critical treatment.

United States of America

Change point analysis is a well-established topic in statistics that is concerned with detecting and localizing abrupt changes in the data generating distribution in time series and, more broadly, stochastic and spatial processes. A long-studied subject with a rich literature, change point analysis has produced a host of well-established methods for statistical inference available to practitioners. These techniques are widely used in many, diverse applications to address important real life problems, such as security monitoring, neuroimaging, financial trading, ecological statistics, climate change, medical condition monitoring, sensor networks, risk assessment for disease outbreak, flu trend analysis, genetics and many others. However, existing frameworks for statistical analysis of change point problems often rely on traditional modeling assumptions of parametric nature that are inadequate to capture the inherent complexity of modern, high-dimensional datasets. The broad goal of this proposal is to develop novel theories and methods for change point analysis in high-dimensional and nonparametric settings, for both offline and sequential problems.