This new spin-measuring device could help scientists - particularly in chemistry and biology - understand the structure and purpose of materials better.

In a paper published over the weekend in the journal Science Advances, Associate Professor Jarryd Pla and his team from UNSW School of Electrical Engineering and Telecommunications, together with colleague Scientia Professor Andrea Morello, described a new device that can measure the spins in materials with high precision. The University of New South Wales, also known as UNSW Sydney, is a public research university based in Sydney, New South Wales, Australia. It is one of the founding members of Group of Eight, a coalition of Australian research-intensive universities.

“The spin of an electron – whether it points up or down – is a fundamental property of nature,” says A/Prof. Pla. “It is used in magnetic hard disks to store information, MRI machines use the spins of water molecules to create images of the inside of our bodies, and spins are even being used to build quantum supercomputers.

“Being able to detect the spins inside materials is therefore important for a whole range of applications, particularly in chemistry and biology where it can be used to understand the structure and purpose of materials, allowing us to design better chemicals, drugs, and so on.”

In fields of research such as chemistry, biology, physics, and medicine, the tool that is used to measure spins is called a spin resonance spectrometer. Normally, commercially produced spectrometers require billions to trillions of spins to get an accurate reading, but A/Prof. Pla and his colleagues were able to measure spins of electrons in the order of thousands, meaning the new tool was about a million times more sensitive.

This is quite a feat, as there is a whole range of systems that cannot be measured with commercial tools, such as microscopic samples, two-dimensional materials, and high-quality solar cells, which simply have too few spins to create a measurable signal.

The breakthrough came about almost by chance, as the team was developing a quantum memory element for a superconducting quantum computer. The objective of the memory element was to transfer quantum information from a superconducting electrical circuit to an ensemble of spins placed beneath the circuit.

“We noticed that while the device didn’t quite work as planned as a memory element, it was extremely good at measuring the spin ensemble,” says Wyatt Vine, a lead author on the study. “We found that by sending microwave power into the device as the spins emitted their signals, we could amplify the signals before they left the device. What’s more, this amplification could be performed with very little added noise, almost reaching the limit set by quantum mechanics.”

While other highly sensitive spectrometers using superconducting circuits had been developed in the past, they required multiple components, were incompatible with magnetic fields, and had to be operated in very cold environments using expensive “dilution refrigerators”, which reach temperatures down to 0.01 Kelvin.

In this new development, A/Prof. Pla says he and the team managed to integrate the components on a single chip.

“Our new technology integrates several important parts of the spectrometer into one device and is compatible with relatively large magnetic fields. This is important since measuring the spins they need to be placed in a field of about 0.5 Tesla, which is ten thousand times stronger than the earth’s magnetic field.

“Further, our device operated at a temperature more than 10 times higher than previous demonstrations, meaning we don’t need to use a dilution refrigerator.”

A/Prof. Pla says the UNSW team has patented the technology with a view to potentially commercialize it but stresses that there is still work to be done.

“There is potential to package this thing up and commercialize it which will allow other researchers to plug it into their existing commercial systems to give them a sensitivity gain.

“If this new technology was properly developed, it could help chemists, biologists, and medical researchers, who currently rely on tools made by these large tech companies that work, but which could do something orders of magnitude better.”

Despite the remarkable progress in artificial intelligence (AI), several studies show that AI systems do not improve radiologists' diagnostic performance. Diagnostic errors contribute to 40,000 - 80,000 deaths annually in U.S. hospitals. This lapse creates a pressing need: Build next-generation computer-aided diagnosis algorithms that are more interactive to fully realize the benefits of AI in improving medical diagnosis. 

That’s just what Hien Van Nguyen, the University of Houston associate professor of electrical and computer engineering, is doing with a new $933,812 grant from the National Cancer Institute. He will focus on lung cancer diagnostics. 

“Current AI systems focus on improving stand-alone performances while neglecting team interaction with radiologists,” said Van Nguyen. “This project aims to develop a computational framework for AI to collaborate with human radiologists on medical diagnosis tasks.” 

That framework uses a unique combination of eye-gaze tracking, intention reverse engineering, and reinforcement learning to decide when and how an AI system should interact with radiologists. 

To maximize time efficiency and minimize the amount of distraction on clinical work, Van Nguyen is designing a user-friendly and minimally interfering interface for radiologist-AI interaction.  

The project evaluates the approaches for two clinically important applications: lung nodule detection and pulmonary embolism. Lung cancer is the second most common cancer, and pulmonary embolism is the third most common cause of cardiovascular death.  

“Studying how AI can help radiologists reduce these diseases' diagnostic errors will have significant clinical impacts,” said Van Nguyen. “This project will significantly advance the knowledge of the field by addressing important, but largely under-explored questions.”  

The questions include when and how AI systems should interact with radiologists and how to model radiologists' visual scanning process. 

“Our approaches are creative and original because they represent a substantive departure from the existing algorithms. Instead of continuously providing AI predictions, our system uses a gaze-assisted reinforcement learning agent to determine the optimal time and type of information to present to radiologists,” said Van Nguyen.  

“Our project will advance the strategies for designing user interfaces for doctor-AI interaction by combining gaze-sensing and novel AI methodologies.”