Point defects govern many important functional properties of two - dimensional ( 2D) materials. However, resolving the three - dimensional (3D) arrangement of these defects in multi - layer 2D materials remains a fundamental challenge, hindering rational defect engineering . Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their dist ribution that can be correlated with specinullic synthesis pathways. This large - scale data enables us to classify a hierarchy of defect structures -- from isolated vacancies to nanopores -- revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations . This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect - engineered functional 2D materials. Keywords: 2D materials, point defects, autonomous materials science, electron microscopy, machine learning 2 Two - dimensional (2D) materials have become a major nullield of modern research in materials science after the discovery of graphene in 2004 . The challenge of characterizing point defects is signinullicantly amplinullied in few - layered 2D materials. For instance, MXenes -- a class of 2D transition metal carbides, carbonitrides, and nitrides -- consist of nanosheets containing two to nullive layers of metal ato ms, which complicates defect analysis compared to single - layer materials .

This study introduces an innovative 3D printed dry electrode tailored for biosensing in postoperative recovery scenarios. Fabricated through a drop coating process, the electrode incorporates a novel 2D material.

Researchers from KTH Royal Institute of Technology and Stanford University have now fabricated a material for computer components that enable the commercial viability of computers that mimic the human brain. Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward commercialization of powerful computers that are modeled after the brain's neural network. Such neuromorphic computers can be thousands times more energy efficient than today's computers. These advances in computing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi. "Instead of transistors that are either on or off, and the need for information to be carried back and forth between the processor and memory -- these new computers rely on components that can have multiple states, and perform in-memory computation," Hamedi says.

Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.

Researchers at the Korea Advanced Institute of Science and Technology, or KAIST, have developed an ultra-thin actuator for soft robotics. The artificial muscles, recently reported in the journal Science Robotics, were demonstrated with a robotic blooming flower brooch, dancing robotic butterflies, and fluttering tree leaves on a kinetic art piece. Actuators are the robotic equivalents of muscles, expanding, contracting, or rotating like muscle fibers in response to a stimulus such as electricity. Engineers around the world are striving to develop more dynamic actuators that respond quickly, can bend without breaking, and are very durable. Soft robotic muscles could have a wide variety of applications, from wearable electronics to advanced prosthetics.