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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.

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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.

Continue reading “Can The Sun Power The Earth? Will Solar Energy Cope Or Will The Lights Go Out?” | >

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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There’s also been a lot of interest in creating more versatile “living inks” made up of bacteria, which can be genetically engineered to do everything from deliver drugs to clean up pollutants. But so far, approaches have relied on mixing microbes with polymers that help provide the ink with some structural integrity.

Now, researchers have developed a new living ink that more closely lives up to the name by replacing the polymers with a protein made by genetically engineered E. coli bacteria. The researchers say this opens the door to seeding large-scale, living structures from nothing more than a simple cell culture.

The key to the breakthrough was to repurpose the proteins that E. coli cells secrete to stick together and form hard-to-shift biofilms. In a paper in Nature Communications, the researchers describe how they genetically engineered bacteria to produce two different versions of this protein known as a “knob” and a “hole,” which then lock together to form a robust cross-linked mesh.

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Solar car can be better.

The cost of electric vehicle battery packs has fallen to $132 per kWh – continuing decades of cost improvements. However, it might go up over the next year as increased material prices are catching up to incremental cost improvements.

Price per kWh is the metric used to track the price of batteries. It can be used to talk about the cost of battery packs or battery cells.

For example, if Tesla were achieving a cost per kWh of $150 for its Model S battery pack, it would mean that the battery pack costs $15,000 since it has a capacity of 100 kWh.

Continue reading “Electric vehicle battery cost falls to 2 per kWh, but it might go up from there” | >

Expect the semiconductor shortage to last until early 2023, Deloitte said in a new report released Wednesday. By the end of 2022, customers will still be waiting 10 to 20 weeks for multiple kinds of chips, the consulting firm predicts.

While the shortage will continue, it will be less severe, Deloitte says in its Technology, Media & Telecommunications (TMT) 2022 Predictions report. The shortage is also driving fresh investment in the industry, as demand continues to grow. Deloitte predicts that venture capital (VC) firms globally will invest more than US$6 billion in semiconductor companies in 2022. That’s more than 3x larger than VC investment in semiconductors every year between 2000 and 2016.

The ongoing shortage won’t hit the industry evenly, Deloitte notes. Chips made on the most advanced process nodes (3-, 5-, and 7-nanometer) will continue to be in short supply — they’re in high demand and the hardest to make. At the end of the day, Deloitte predicts the shortage will last 24 months before it recedes, similar to the duration of the 2008–2009 chip shortage.

B-SURE program aims to develop fundamental understanding of microbial capabilities for bioproduction in space.

DARPA announced yesterday it is taking an initial step to explore and de-risk manufacturing capabilities in space with its Biomanufacturing: Survival, Utility, and Reliability beyond Earth (B-SURE) program. https://www.darpa.mil/news-events/2021-11-22

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Research has long strived to develop computers to work as energy efficiently as our brains. A study, led by researchers at the University of Gothenburg, has succeeded for the first time in combining a memory function with a calculation function in the same component. The discovery opens the way for more efficient technologies, everything from mobile phones to self-driving cars.

In recent years, computers have been able to tackle advanced cognitive tasks, like language and image recognition or displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the is still unmatched in its ability to perform tasks effectively and energy efficiently.

“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades. Cognitive tasks, like image and voice recognition, require significant computer power, and mobile applications, in particular, like mobile phones, drones and satellites, require energy efficient solutions,” says Johan Åkerman, professor of applied spintronics at the University of Gothenburg.

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