Researchers from the Low Energy Electronic Systems (LEES) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT's research enterprise in Singapore, together with Massachusetts ...
QUT researchers have used carbon dots, created from human hair waste sourced from a Brisbane barbershop, to create a kind of "armor" to improve the performance of cutting-edge solar technology.
Nature produces a startling array of patterned materials, from the sensitive ridges on a person's fingertip to a cheetah's camouflaging spots. Although nature's patterns arise spontaneously during development, creating patterns ...
Scientists from the Graduate School of Science at Osaka University created superabsorbent polymer (SAP) microparticles that self-assemble into structures that can be modified by adjusting the proportion of particle type. ...
Smart glass can change its color quickly through electricity. A new material developed by chemists of Ludwig-Maximilians-Universität (LMU) in Munich has now set a speed record for such a change.
Finding the best materials for tomorrow's electronics is the goal of Professor Emanuele Orgiu of the Institut national de la recherche scientifique (INRS). Among the materials in which Professor Orgiu is interested are some ...
By using laser spectroscopy in a photophysics experiment, Clemson University researchers have broken new ground that could result in faster and cheaper energy to power electronics.
Researchers at Tufts University School of Engineering have created light-activated composite devices able to execute precise, visible movements and form complex three-dimensional shapes without the need for wires or other ...
Electronic textiles offer revolutionary new opportunities in various fields, in particular healthcare. But to be sustainable, they need to be made of renewable materials. A research team led by Chalmers University of Technology, ...
Just as James Cameron's Terminator-800 was able to discriminate between "clothes, boots, and a motorcycle," machine-learning could identify different areas of interest on 2-D materials.
