As lithium-ion batteries (LIBs) continue to power electric vehicles and energy storage systems, their long-term health remains a critical challenge. A groundbreaking new method combines deep learning with physical modeling to deliver rapid, accurate degradation diagnosis at the electrode level. This innovative approach requires only 11 data points from a charging cycle, achieving reliable predictions in just 2.5 minutes. By simplifying the data needed and eliminating the need for specialized equipment, this technique offers a faster, more accessible solution to battery health monitoring—paving the way for safer, more efficient battery management in large-scale applications.

- Mass spectrometer identifies pathogens directly in tissue and stool samples

- So far 232 medically important bacterial species detectable

- Database must now be further expanded

Speed and reliability are crucial in the diagnosis of diseases. Researchers at the Technical University of Munich (TUM) and Imperial College London have developed a new method to identify bacteria with unprecedented speed. This means that the waiting time can be reduced from several days to just a few minutes.

Florida State University Assistant Professor of Physics Zhengguang Lu and fellow researchers have discovered new states of matter in graphene — a form of carbon made from a single layer of atoms — with unusual electrical properties that could make them a valuable tool for building more powerful electronics and quantum computers.

Hybridizing tropical and hardy water lilies holds great potential for creating new varieties with both cold tolerance and vibrant colors.