But a recent discovery by a multi-university collaboration of researchers, led by Drexel University researcher Yury Gogotsi, PhD, and Drexel alumnus Babak Anasori, PhD, who is now an associate professor at Purdue University, that sheds light on the thermodynamics undergirding the materials’ unique structure and behavior, could be the key to supercharging the development of two-dimensaional materials with artificial intelligence technology. The discovery was recently reported in the journal Science.

Maintaining the order and stability that is vital for more widespread and predictably reliable nanomaterial applications is finicky. Researchers have succeeded in putting up to nine transition metals from the periodic table into a single 2D sheet of an ultrathin material called MXene. By completing this complicated task, they were able to evaluate the true role of entropy versus enthalpy in these high-entropy materials, which is critical to the successful design and implementation of nanomaterials in use cases.

Genetically engineered cell lines used in biomedical research have long been prone to misidentification and unauthorized use, wasting billions of dollars each year and jeopardizing critical scientific discoveries. These problems not only undermine reproducibility of research results, but also put valuable intellectual property at risk.

Now, researchers at The University of Texas at Dallas have developed a novel method to embed unique genetic identifiers in engineered cell lines, eliminating identification errors and safeguarding innovations with tamper-proof genomic tags.

Penn Engineering researchers use light-triggered tissue stiffening to uncover cellular transitions that may drive fibrosis

Engineers were able to develop materials with fine control over material properties like stiffness. For example, a prosthetic leg built with this system can stiffen to provide support while walking on a flat surface but reconfigure into a more flexible state for climbing stairs. The designers could also create adjustable metasurfaces that are used in antennas and optics.

Researchers at Caltech and Gran Sasso Science Institute in Italy teamed up with Google DeepMind to develop a new AI method–called Deep Loop Shaping–that can better hush unwanted noise in LIGO's detectors.

The University of Michigan has received a five-year, $15 million grant to fund the Center for Land Surface Hazards. CLaSH will draw together expertise from dozens of academic, governmental and community partners to study fundamental science processes that cause landsliding, river erosion, debris flows and flooding which can occur after other natural disasters.  

It comes from a University at Buffalo-led team that introduced a new and rigorous experimental method to determine the acidity of 10 types of PFAS and three of their common breakdown products.