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- Using liquid air for grid-scale energy storage - MIT News
Liquid air energy storage could be the lowest-cost solution for ensuring a reliable power supply on a future grid dominated by carbon-free yet intermittent energy sources, according to a new model from MIT researchers
- Explained: Generative AI’s environmental impact - MIT News
MIT News explores the environmental and sustainability implications of generative AI technologies and applications
- Ensuring a durable transition - MIT News
At the MIT Energy Initiative’s Annual Research Conference, speakers highlighted the need for collective action in a durable energy transition capable of withstanding obstacles
- Transforming fusion from a scientific curiosity into a powerful clean . . .
Transforming fusion from a scientific curiosity into a powerful clean energy source Driven to solve hard problems, Associate Professor Zachary Hartwig is advancing a new approach to commercial fusion energy
- MIT Climate and Energy Ventures class spins out entrepreneurs — and . . .
In MIT course 15 366 (Climate and Energy Ventures) student teams select a technology and determine the best path for its commercialization in the energy sector
- Study shows how households can cut energy costs - MIT News
Giving people better data about their energy use, plus some coaching, can help them substantially reduce their consumption and costs, according to a study by MIT researchers in Amsterdam
- A nonflammable battery to power a safer, decarbonized future
Now Alsym Energy has developed a nonflammable, nontoxic alternative to lithium-ion batteries to help renewables like wind and solar bridge the gap in a broader range of sectors The company’s electrodes use relatively stable, abundant materials, and its electrolyte is primarily water with some nontoxic add-ons
- Photonic processor could enable ultrafast AI computations . . . - MIT News
Researchers developed a fully integrated photonic processor that can perform all the key computations of a deep neural network on a photonic chip, using light This advance could improve the speed and energy-efficiency of running intensive deep learning models for applications like lidar, astronomical research, and navigation
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