Category Physics

The three-fingered robotic gripper can ‘feel’ with great sensitivity along the full length of each finger — not just at the tips

Inspired by the human finger, MIT researchers have developed a robotic hand that uses high-resolution touch sensing to accurately identify an object after grasping it just one time.

Many robotic hands pack all their powerful sensors into the fingertips, so an object must be in full contact with those fingertips to be identified, which can take multiple grasps...

A research team develops semiconductor devices for high-performance AI operations by applying IGZO materials widely used in OLED displays. ChatGPT’s impact extends beyond the education sector and is causing significant changes in other areas. The AI language model is recognized for its ability to perform various tasks, including paper writing, translation, coding, and more, all through question-and-answer-based interactions. The AI system relies on deep learning, which requires extensive training to minimize errors, resulting in frequent data transfers between memory and processors...

In recent years, researchers have been trying to create increasingly efficient solar technologies and more sustainable battery designs. Among emerging sustainable energy solutions are solar batteries, systems that can store the energy collected by solar cells or photovoltaic (PV) systems.

A research group at the Max Planck Institute for Solid State Research, supervised by Prof. Bettina Lotsch, recently introduced a sustainable integrated solar battery design based on materials that abundant on Earth. Their design, presented in a paper published in Energy & Environmental Science, is based on a bi-functional carbon nitride (K-PHI) photoanode that can both absorb light and store electric charge.

“The solar battery research field is still young and as such very diverse in concepts a...

The transformative changes brought by deep learning and artificial intelligence are accompanied by immense costs. For example, OpenAI’s ChatGPT algorithm costs at least $100,000 every day to operate. This could be reduced with accelerators, or computer hardware designed to efficiently perform the specific operations of deep learning. However, such a device is only viable if it can be integrated with mainstream silicon-based computing hardware on the material level.

This was preventing the implementation of one highly promising deep learning accelerator—arrays of electrochemical random-access memory, or ECRAM—until a research team at the University of Illinois Urbana-Champaign achieved...