Key Highlights
•
Researchers have developed a new machine learning framework that uses Nuclear Magnetic Resonance (NMR) data to accurately predict the permeability of geothermal rocks, a critical factor for efficient energy extraction. This provides a faster, more cost-effective alternative to traditional lab methods, helping to better assess and develop geothermal reservoirs.
Source →
•
A new review argues that for CO2 electrolyzers to be practical, we must view their stability as a dynamic, system-wide property that can be managed and recovered during operation, rather than just a measure of how long they last. This shift in thinking is crucial for moving this promising technology from the lab to large-scale industrial use, where consistent performance is key.
Source →
•
Scientists have created a system that uses auto-regressive modeling and machine learning to recognize volcanic microseisms—tiny ground tremors—in real time. This advancement allows for faster and more accurate monitoring of volcanic activity, which is essential for early warning systems and geothermal energy exploration near volcanoes.
Source →
Stay curious. Stay informed — with
Science Briefing.
Always double check the original article for accuracy.
