Power/Performance Bits: Dec. 18


Solar storage Engineers at MIT, Georgia Institute of Technology, and the National Renewable Energy Laboratory designed a system to store renewable energy in vast amounts and deliver it back to the grid when power generation is low. The system stores excess electricity from solar or wind installations as heat using tanks of white-hot molten silicon, and then converts the light from the glowi... » read more

Power/Performance Bits: Nov. 27


Hybrid solar for hydrogen and electricity Researchers at the Lawrence Berkeley National Laboratory developed an artificial photosynthesis solar cell capable of both storing the sun's energy as hydrogen through water splitting and outputting electricity directly. The hybrid photoelectrochemical and voltaic (HPEV) cell gets around a limitation of other water splitting devices that shortchange... » read more

Power/Performance Bits: Oct. 30


Long-term solar energy storage Researchers from Chalmers University of Technology and Universidad de La Rioja created a system capable of storing solar energy for extended periods of time. The system, called Molecular Solar Thermal Energy Storage (MOST), hinges on a molecular photoswitch made from carbon, hydrogen and nitrogen. When the molecule is hit by sunlight, it turns into an energy-rich... » read more

Power/Performance Bits: Oct. 23


Integrated solar battery Researchers at the University of Wisconsin–Madison and King Abdullah University of Science and Technology (KAUST) built a unified solar cell-liquid battery device capable of returning more than 14% of the incoming solar energy as electricity. The device is capable of both converting solar energy to electricity for immediate use or storing it as chemical energy in ... » read more

Power/Performance Bits: Oct. 16


On-chip modulator Researchers at Harvard SEAS and Nokia Bell Labs boosted shrunk down an important component of optoelectronics with an on-chip modulator that is 100 times smaller and 20 times more efficient than current lithium niobite (LN) modulators. Lithium niobate modulators form the basis of modern telecommunications, converting electronic data to optical information in fiber optic ca... » read more

Power/Performance Bits: Sept. 18


Etching photovoltaics Researchers at Michigan Technological University and Aalto University found a way to reduce production costs of black silicon solar cells by more than 10%. The first prototype modules have been manufactured on an industrial production line. Typically, the silicon used for solar cells is etched to reduce reflected light, although some light is still lost. Nano-texturing... » read more

Power/Performance Bits: Sept. 11


Non-toxic photoluminescent nanoparticles Researchers from Osaka University developed a way to improve display technologies using non-toxic light-emitting nanoparticles. In trying to replace cadmium and other toxic materials used in quantum dots, scientists have turned to non-toxic nanoparticles that emit light in an efficient manner by creating I–III–VI semiconductors, such as silver in... » read more

Power/Performance Bits: Aug. 21


Physical neural network Engineers at UCLA built a physical artificial neural network capable of identifying objects as light passes through a series of 3D printed polymer layers. Called a "diffractive deep neural network," it uses the light bouncing from the object itself to identify that object, a process that consumes no energy and is faster than traditional computer-based methods of imag... » read more

Power/Performance Bits: Aug. 7


Optical neural network Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature brain-like grid, showcasing a potential new design for neural networks. Using light would eliminate interference due to electrical charge and the signals would travel faster and farther, said the researchers. "... » read more

Power/Performance Bits: July 31


Training optical neural networks Researchers from Stanford University used an optical chip to train an artificial neural network, a step that could lead to faster, more efficient AI tasks. Although optical neural networks have been recently demonstrated, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical... » read more

← Older posts