Sensors manufactured with carbon-based materials can provide uniquely accurate and real-time information on hereditary diseases or the concentrations of drugs in the body. In addition to medicine, carbonaceous materials are used in batteries, solar cells and water purification.

Other elements, such as hydrogen and oxygen, are almost always present in carbon-based materials, which alters the materials’ properties. Therefore, modifying materials for desired applications requires atomic-level knowledge on carbon surface structures and their chemistry. Researchers at Aalto University, the University of Cambridge, the University of Oxford and Stanford University have now taken a significant new step forward in describing the atomic nature of carbonaceous materials.

Detailed information on carbon surfaces can be obtained by X-ray spectroscopy, but the spectrum it produces is challenging to interpret because it summarises information from several local chemical environments of the surface. The researchers have developed a new systematic analysis method that uses machine learning to integrate the computational model (density functional theory) with the experimental results of the carbon sample. The new methodology allows the experimental spectrum produced by X-ray spectroscopy to be separated into atomic-level data