New research from India reveals that, contrary to long-held scientific beliefs, feeding on trees and shrubs does not provide Asian elephants with more protein than grazing on grass.
Combining two electrochemical techniques, hydrogen permeation-based potentiometry (HPP) and electrochemical impedance spectroscopy (EIS), the researchers efficiently measured the coating degradation rates on the industrially relevant metal.
Researchers have used machine learning and optimization tools to create new super-alloys without any rare and expensive materials.
On 17th January 2025, Satya Nadella, Microsoft's Chairman and CEO, took to X to announce a research paper published in Nature that caught our attention.
Imagine if we could predict the properties of materials without having to test them in a lab. This would save a lot of time and money, and it could help scientists discover new materials with amazing properties, like super-strong metals or super-efficient semiconductors. This is exactly what researchers at the Indian Institute of Science (IISc) and University College London are working on. They are using machine learning tools to predict material properties, even when there is limited data available.
Two new studies from the Indian Institute of Bombay (IIT Bombay), Mumbai, show the importance of defects in the arrangement of atoms in a crystal, called dislocations, in shaping the physical properties of metallic alloys.
Researchers probe properties of the new material, say better for micro- and nanodevices.
The field of material science has become exciting in the recent past with scientists discovering some remarkable properties and behaviour of novel materials. In one such study, researchers from the National Institute of Technology, Meghalaya, India, and the New York University Abu Dhabi, UAE have designed a versatile crystal material that can be twisted with heat, bent with light, is elastic and can heal itself by heating or cooling.