May 2, 2018

Stanford, SLAC Researchers Have Developed a Water-Based Battery to Store Solar and Wind Energy

SIMES scientists have developed a manganese-hydrogen battery that could fill a missing piece in the nation’s energy puzzle by storing wind and solar energy for when it is needed, lessening the need to burn carbon-emitting fossil fuels.

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Devereaux was honored for contributions to materials science and was among seven Stanford-affiliated researchers named AAAS Fellows this year.

Thomas Devereaux
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Seen in atomic detail, the seemingly smooth flow of ions through a battery’s electrolyte is a lot more complicated.

Photo of the laser lab apparatus used in the hopping ions experiment.
News Feature

Researchers have discovered that crystals can twist when they are sandwiched between two substrates – a critical step toward exploring new material properties for...

This image shows a diffraction pattern of gold nanodics between substrates.
News Brief

Devereaux was honored for contributions to materials science and was among seven Stanford-affiliated researchers named AAAS Fellows this year.

Thomas Devereaux
News Feature

Seen in atomic detail, the seemingly smooth flow of ions through a battery’s electrolyte is a lot more complicated.

Photo of the laser lab apparatus used in the hopping ions experiment.
News Feature

Researchers have discovered that crystals can twist when they are sandwiched between two substrates – a critical step toward exploring new material properties for...

This image shows a diffraction pattern of gold nanodics between substrates.
News Feature

Strongly interacting electrons in quantum materials carry heat and charge in a way that’s surprisingly similar to what individual electrons do in normal metals...

An illustration shows electrons transporting heat from a warmer to a cooler area of a material.
News Brief

The American Physical Society recognized the SLAC and Stanford physicist for decades of groundbreaking work studying the strange behavior of electrons at the interfaces...

Photo - Harold Hwang
News Feature

The research reveals the potential for machine learning in understanding the complex behavior of quantum materials.

machine learning