Decision-making often involves trial and error, but conventional models assume we always act optimally based on past experience.

After the successful separation of a monolayer of carbon atoms with honeycomb lattice known as graphene in 2004, a large group of 2D materials known as TMDCs and MXenes were discovered and studied. The realm of 2D materials and their heterostructures has created new opportunities for the development of various types of advanced rigid, flexible and stretchable biosensors, and chemical, optoelectronic and electrical sensors due to their unique and versatile electrical, chemical, mechanical and optical properties. The high surface to volume ratio and quantum confinement in 2D materials make them strong candidates for the development of sensors with improved sensitivity and performance. This group of atomically thin material also offer mechanical flexibility and limited stretchability harvested towards making flexible and stretchable sensors that can be used at the interface with soft tissues and in soft robotics. However, challenges remain in fully realizing their potential in practical applications.
The aim of this collection is to highlight the current progress in the research of 2D materials, focusing on their integration into sensing technologies. We seek to provide a comprehensive overview of the advancements made in this area while addressing the challenges faced in developing practical applications.
Stanford HAI offers critical resources for faculty and students to continue groundbreaking research across the vast AI landscape.
Researchers from Aarhus University—in a major international collaboration—have developed a groundbreaking method that can provide more information from the tissue samples doctors take from patients every day.
The new technique, called Pathology-oriented multiPlexing or PathoPlex, can look under a microscope at over 100 different proteins in the same small piece of tissue—instead of just 1–2 at a time, as is done now.
The technology, which has just been published in the journal Nature, combines advanced image processing with machine learning to map complex disease processes in detail.
Bedrock co-founder Geoff Lewis has posted increasingly troubling content on social media, drawing concern from friends in the industry.
They found the photos in a thrift store.
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