skip to content

Winton Programme for the Physics of Sustainability

Department of Physics
 

Dr Mete Atatüre (Atomic, Mesoscopic and Optical Physics) and Dr Jason Robinson (Department of Materials Science & Metallurgy)

This project aims to utilize a new magnetometry technique based on diamond spins in order to provide answers to the following open questions in the field of oxide interface magnetism: 

  • How does charge localization control spin-dependent interface symmetries, such as magnetism?
  • What is the origin of the magnetism and is it genuinely localised to the interface?
  • What is the characteristic length scale of the magnetic domains that form?

A direct understanding and a demonstrated control of the emergent phenomena at the oxide interfaces using a brand new diamond-based magnetic sensing will impact the current approach to manufacturing designer materials in transport and storage devices utilizing both charge and spin properties.

Project Completed May 2014

This project demonstrated that there is emergent magnetic ordering around 40 K above which the usual metallic nature of the interface electron gas is recovered. Below this critical temperature the NV centre spin was found to be coupled to the electron gas. The transition takes place within 1 K accuracy, which is the system resolution. Since the support of the Winton pump prime award, funds have been attracted from the Leverhulme Trust for a three-year project worth £250,000 funding two postdoctoral fellows and consumables to continue on the nano-MRI programme with oxide interface magnetism until May 2017.

Latest news

Manipulation of Quantum Entangled Triplet Pairs

7 January 2021

Researchers have uncovered a new technique to create and manipulate pairs of particle-like excitations in organic semiconductors that carry non-classical spin information across space, much like the entangled photon pairs in the famous Einstein-Podolsky-Roden “paradox”.

Machine learning algorithm helps in the search for new drugs

20 March 2019

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard.