Science Brief - April 2024

Science Brief - April 2024

A sustainable solution to produce fertilisers probed at the ESRF

Researchers from the Technical University Denmark (DTU) and Stanford University have come to the ESRF to study the processes taking place during a new, sustainable way of producing ammonia for fertilisers.

Journal: Energy & Environmental Science

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Insights into magic mushrooms' hallucinogen

Recent clinical trials have shown promising results of the use of the natural hallucinogen found in magic mushrooms, psilocybin, to treat severe depression.

Now scientists led by the Medical University of Innsbruck, with the ESRF, have elucidated the structure of a crucial enzyme in psilocybin biosynthesis at various stages of the reaction cycle.

Journal: Nature Communications

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Citrate synthase spontaneously assembles into a fractal pattern known as the Sierpinski triangle. Credits: MPI f. Terrestrial Microbiology/ Hochberg.

The first fractal molecule in nature discovered

Scientists led by Max Planck Institute and the Philipps University in Marburg have discovered the first fractal molecule found in nature partly using X-rays at the ESRF. The microbial enzyme spontaneously assembles into a pattern known as the Sierpinski triangle.

“The ESRF's experiments were very important because it helped us show that these assemblies can keep growing infinitely”, Georg Hochberg, MPI.

Journal: Nature

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Graphical abstract of the experiment. Credits:?B. O. Joensen et al, Joule, In Press, Corrected Proof, 2024.

Water and cations play tag in electrolysers

Zero-gap CO2 electrolysers have the ability to produce desired high-value products from CO2, one of the main contributors to climate change.

The precise functioning of these electrolysers is key to the design more efficient devices.

Researchers led by the Technical University of Denmark, with the ESRF, have unveiled how water and Cessium+ ions move inside a membrane electrode assembly in a zero-gap CO2 electrolyser.

Journal: Joule

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Examination and application of the trained AM-SegNet: (a) Grad-CAM results of keyhole and pore associated with the trained AM-SegNet. The blue regime corresponds to a condition of low influence, whereas the red regime indicates high impact; and (b) comparison between ground-truth and AM-SegNet segmentation results of time-series X-ray images in the LPBF experiments.

New deep learning model boosts additive manufacturing X-ray analysis

Researchers have created a deep learning model called AM-SegNet to identify critical features within molten pools of material during the additive manufacturing process.

It draws on a database of over 10 000 X-ray images of additive manufacturing from ESRF beamline ID19 and other synchrotron facilities.

Journal: Virtual and Physical Prototyping

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