Recipe for Neuromorphic Processing Systems?

The field of “brain-mimicking” neuromorphic electronics shows great potential for basic research and commercial applications, and researchers in Germany and Switzerland recently explored the possibility of reproducing the physics of real neural circuits by using the physics of silicon. In Applied Physics Letters, they present their work to understand neural processing systems, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials.

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Adjusting Processing Temperature Results in Better Hydrogels for Biomedical Applications

Biohydrogels have been studied closely for their potential use in biomedical applications, but they often move between sols and gels, depending on their temperature, changes that can pose issues depending on the intended use. In Physics of Fluids, researchers discuss their work studying the effect of temperature on hydrogels. They found that creating hydrogels at room temperature or below results in more robust materials that function more effectively when used in the body.

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Berkeley Lab Cosmologists Are Top Contenders in Machine Learning Challenge

In a machine learning challenge dubbed the 2020 Large Hadron Collider Olympics, a team of cosmologists from Berkeley Lab developed a code that best identified a mock signal hidden in simulated particle-collision data.

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Physicists test coronavirus particles against temperature, humidity

One of the biggest unknowns about coronavirus is how changing seasons will affect its spread. Physicists from the University of Utah have received a NSF grant to create individual coronavirus particles without a genome. They’ll test how the structure of the coronavirus withstands changes in humidity and temperature.

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Composing New Proteins with Artificial Intelligence

Proteins are the building blocks of life and scientists have long studied how to improve them or design new ones. Traditionally, new proteins are created by mimicking existing proteins or manually editing their amino acids. This process is time-consuming, and it is difficult to predict the impact of changing an amino acid. In APL Bioengineering, researchers explore how to create new proteins by using machine learning to translate protein structures into musical scores, presenting an unusual way to translate physics concepts across domains.

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Last Call for Entries: AIP’s 2020 Science Communication Awards

The American Institute of Physics is accepting nominations for the 2020 AIP Science Communication Awards through March 31, 2020. Four awards will be given for the best science writing in books; magazine, newspaper or online articles; children’s books and other works intended for children; and broadcast and online. Works should be intended for a general audience and will be judged on their ability to enhance the public’s understanding and appreciation of physics and related fields.

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Inverse Design Software Automates Design Process for Optical, Nanophotonic Structures

Stanford University researchers created an inverse design codebase called SPINS that can help researchers explore different design methodologies to find fabricable optical and nanophotonic structures. In the journal Applied Physics Reviews, Logan Su and colleagues review inverse design’s potential for optical and nanophotonic structures, as well as present and explain how to use their own inverse design codebase.

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Crosstalk Captured Between Muscles, Neural Networks in Biohybrid Machines

Researchers created a platform to observe stem cell-derived neurons grow toward muscle cells, representing a critical milestone towards the realization of future biohybrid machines. In tiny biorobots using muscle cells as actuators, the ability to tune parameters would allow more precise designs with desirable characteristics and predictable behaviors for intelligent drug delivery, environment sensing, biohybrid blood circulation pumps and other uses. But big questions remain about future experiments.

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