Deep learning streamlines identification of 2D materials
Researchers have developed a deep learning-based approach that significantly streamlines the accurate identification and classification of two-dimensional (2D) materials through Raman spectroscopy. In comparison, traditional Raman analysis methods ar.....»»
Nest Thermostat (4th Gen) Review: A Stunner of a Smart Home Device
The new Nest Learning Thermostat (4th Gen) arrived in late August as a major upgrade to the previous “Learning” model, as well as the mid-range Nest Thermostat from 2020. The new model brings a striking new design, full compatibility with.....»»
Latest Pixel 9a Specs Include Screen Size, Cameras, and Charging Speeds
Pixel 9a launch is expected next March, but we’ve already learned so much about the device. We’ve heard about its processor, plus we know what it looks like from all angles. This week, we’re learning even more, thanks to yet another.....»»
Single-molecule tracking technology streamlines drug discovery
New drug discovery is a critical step for improving patients' lives. First, researchers must identify molecules in the body's cells that help drive disease, as these are potential targets for new drugs. The next step is to screen candidate drugs that.....»»
Machine-learning analysis tracks the evolution of 16th-century European astronomical thought
A team of computer scientists, astronomers and historians in Berlin has used machine-learning applications to learn more about the evolutionary history of European astronomical thought in the 15th and 16th centuries. In their study published in the j.....»»
Using machine learning to identify bacterial resistance genes and the drugs to block them
Antibiotic resistance is a growing public health problem around the world. When bacteria like E. coli no longer respond to antibiotics, infections become harder to treat......»»
Scientists develop starch nanocomposite films that pave the way for green electronics
Queen Mary University of London researchers have developed new nanocomposite films using starch instead of petroleum-based materials, marking a significant advancement in the field of sustainable electronics......»»
Deep learning enhances accuracy and efficiency in protein structure prediction
In the rapidly advancing field of computational biology, a review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction. The review, published in MedComm—Future Medicine, is led by Dr. Xi Yu an.....»»
Accelerating 3D nanofabrication using a sensitive cationic photoresist
Two-photon laser direct writing lithography or TPL is a cutting-edge technique used for creating nanoscale structures. It works by leveraging specific materials known as photoresists, which change their chemical properties when exposed to light. Thes.....»»
Novel polypeptide-based molecules could pave the way for enhanced polymer design
A research study describes a systematic high-throughput design approach for virtual screening and creation of novel polypeptide-based molecules that form regular secondary structures that can be used in biology or materials science research. The stud.....»»
Discovery challenges existing theories of magnetism in kagome metals
A discovery by Rice University physicists and collaborators is unlocking a new understanding of magnetism and electronic interactions in cutting-edge materials, potentially revolutionizing technology fields such as quantum computing and high-temperat.....»»
Quantum simulator could help uncover materials for high-performance electronics
Quantum computers hold the promise to emulate complex materials, helping researchers better understand the physical properties that arise from interacting atoms and electrons. This may one day lead to the discovery or design of better semiconductors,.....»»
Volcanic "cryptic carbon" emissions may be a hidden driver of Earth"s past climate
An international team of geoscientists led by a volcanologist at Rutgers University-New Brunswick has discovered that, contrary to present scientific understanding, ancient volcanoes continued to spew carbon dioxide into the atmosphere from deep with.....»»
Sinuses prevented prehistoric crocodile relatives from deep diving, paleobiologists suggest
An international team of paleobiologists have found that the sinuses of ocean-dwelling relatives of modern-day crocodiles prevented them from evolving into deep divers like whales and dolphins......»»
AI algorithm accurately detects heart disease in dogs
Researchers have developed a machine learning algorithm to accurately detect heart murmurs in dogs, one of the main indicators of cardiac disease, which affects a large proportion of some smaller breeds such as King Charles Spaniels......»»
Graphene-enhanced ceramic tiles make striking art
Adding a bit of graphene oxide to slurry and zapping with ultrasound for 10 minutes yields best tiles. In recent years, materials scientists experimenting with ceramics have start.....»»
Putin says Moscow will respond if West helps Ukraine to strike deep into Russia
Putin says Moscow will respond if West helps Ukraine to strike deep into Russia.....»»
Graphene oxide and chitosan sponge found to be ten times more efficient at removing gold from e-waste
A team of chemists and materials scientists at the National University of Singapore, working with colleagues from Manchester University, in the U.K., and Guangdong University of Technology, in China, has developed a type of sponge made of graphene ox.....»»
Huddled with his generals deep in the night, Benjamin Netanyahu finally hit the button
Huddled with his generals deep in the night, Benjamin Netanyahu finally hit the button.....»»
New deep ultraviolet micro-LED array advances maskless photolithography
A team led by Prof. Sun Haiding from the University of Science and Technology of China (USTC) developed a vertically integrated micro-scale light-emitting diode (micro-LED) array which was then applied in deep ultraviolet (DUV) maskless photolithogra.....»»
New machine learning model quickly and accurately predicts dielectric function
Researchers Tomohito Amano and Shinji Tsuneyuki of the University of Tokyo with Tamio Yamazaki of CURIE (JSR-UTokyo Collaboration Hub) have developed a new machine learning model to predict the dielectric function of materials, rather than calculatin.....»»