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A unique quantum-mechanical interaction between electrons and topological defects in layered materials

An international team led by EPFL scientists, has unveiledthat has only been observed in engineered atomic thin layers. The phenomenon can be reproduced by the native defects of lab grown large crystals, making future investigation of Kondo systems and quantum electronic devices more accessible.

The properties of materials that are technologically interesting often originate from defects on their atomic structure. For example, changing the optical properties of rubies with chrome inclusions has helped develop lasers, while nitrogen-vacancy in diamonds are paving the way for applications such as quantum magnetometers. Even in the metallurgical industry, atomic-scale defects like dislocation enhances the strength of forged steel.

Another manifestation of atomic-scale defects is the Kondo effect, which affects a metal’s conduction properties by scattering and slowing the electrons and changing the flow of electrical current through it. This Kondo effect was first observed in metals with very few magnetic defects, e.g. gold with few parts per million of iron inclusions. When the diluted magnetic atoms align all the electrons spin around them, this slows the electrical current motion inside the material, equally along every direction.

Can The Sun Power The Earth? Will Solar Energy Cope Or Will The Lights Go Out?

So can solar energy cut it?

Can we really move to a society not harnessed to the unsustainable practices of the old way.

I look at exactly how much land might be required & whether the lights will be able to stay on in the future as they have in the past.

I’m sure most know the answer, but this gives real facts and figures that can be used to defend against the fossil fuel apologists, and shared with friends, family and colleagues who are still learning.

Have an amazing day wherever you are…


‘Transformational’ approach to machine learning could accelerate search for new disease treatments

Researchers have developed a new approach to machine learning that ‘learns how to learn’ and out-performs current machine learning methods for drug design, which in turn could accelerate the search for new disease treatments.

The method, called transformational machine learning (TML), was developed by a team from the UK, Sweden, India and Netherlands. It learns from multiple problems and improves performance while it learns.

TML could accelerate the identification and production of new drugs by improving the machine learning systems which are used to identify them. The results are reported in the Proceedings of the National Academy of Sciences.

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