Molecular materials for computing progress intensively but the performance and reliability still lag behind. Here the authors assess the current state of computing with molecular-based materials and describe two issues as the basis of a new computing technology: continued exploration of molecular electronic properties and process development for on-chip integration.
Photonic crystals are materials with repeating internal structures that interact with light in unique ways. We can find natural examples in opals and the vibrant colored shells of some insects. Even though these crystals are made of transparent materials, they exhibit a “photonic bandgap” that blocks light at certain wavelengths and directions.
Understanding how light travels through various materials is essential for many fields, from medical imaging to manufacturing. However, due to their structure, materials often show directional differences in how they scatter light, known as anisotropy. This complexity has traditionally made it difficult to accurately measure and model their optical properties. Recently, researchers have developed a new technique that could transform how we study these materials.
Getting tips from the design of the human body.
Scientists create bone-inspired cement, over five times stronger than concrete.
Researchers at the University of Princeton have developed a cement paste that is 5.6 times stronger than cement, mortar, and other conventional cement-based construction materials.
The paste features a tubular architecture inspired by the structure of human cortical bone, which forms the outer layer of the femur (thigh bone).
“Cement paste deployed with a tube-like architecture can significantly increase resistance to crack propagation and improve the ability to deform without sudden failure,” according to the researchers.
The widespread use of artificial intelligence (AI) tools designed to process large amounts of data has increased the need for better performing memory devices. The data storage solutions that could help to meet the computational demands of AI include so-called high-bandwidth memories, technologies that can increase the memory bandwidth of computer processors, speeding up the transfer of data and reducing power consumption.
Currently, flash memories are the most prominent memory solutions capable of storing information when a device is turned off (i.e., non-volatile memories). Despite their widespread use, the speed of most existing flash memories is limited and does not best support the operation of AI.
In recent years, some engineers have thus been trying to develop ultrafast flash memories that could transfer data faster and more efficiently. Two-dimensional (2D) materials have shown promise for fabricating these better performing memory devices.