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Light reads electron spins inside porous crystals, opening path to quantum chemical sensors

University of Glasgow researchers are part of an international collaboration that could lead to a new generation of quantum sensors. The team, which included researchers from the University of Tokyo, University of Glasgow, University of Sheffield and Kobe University, has for the first time used light to read out the magnetic spin of electrons trapped inside a porous crystalline material known as…

Light reads electron spins inside porous crystals, opening path to quantum chemical sensors

Researchers at the University of Glasgow have collaborated with colleagues from the University of Tokyo, University of Sheffield, and Kobe University to develop a groundbreaking method for detecting electron spins within porous crystals called metal-organic frameworks (MOFs). This development, which involves using light to read out the magnetic spin of electrons trapped inside MOFs, could pave the way for a new generation of quantum sensors with unprecedented sensitivity and precision.

Dr. Sam Bayliss and Dr. Alistair Inglis from the University of Glasgow led the research, which utilized a technique known as optically detected magnetic resonance (ODMR) to achieve the first-ever optical readout of electron spins in MOFs. MOFs are rigid, porous structures with great potential for sensing applications due to their molecular scaffolds and chemically tunable spin properties.

The team's work demonstrates that MOFs could be used as sensors to detect chemical substances with extraordinary sensitivity, as their spins can be chemically controlled and their structures can be designed to interact with target molecules. The next steps for the researchers involve pushing the technology to work at more practical temperatures and tuning the chemistry to enhance the signal strength.

This breakthrough could lead to the creation of a "quantum nose" - a library of MOFs, each responding differently to specific chemicals, which could be used to identify substances based on their unique response patterns.

Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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