Researchers at Stanford have developed a device to monitor environmental exposure in personal (wearable) or public (stationary) settings. Human health can be viewed as the interactive outcome between inherited traits and environmental risks.
Stanford researchers in Zhenan Bao and Yi Cui's labs have developed an organic redox mediator that could make Lithium Sulfur batteries charge faster with less energy.
Stanford researchers have developed a Data-driven Urban Energy Benchmarking (DUE-B) methodology that uses readily available building energy consumption data to help municipalities design and develop energy efficiency policies and programs.
Stanford researchers have developed a method which can simultaneously observe two positron emitting isotopes using two distinct molecular probes and a modified PET scanner. This system enables the simultaneous observation of two different molecular processes.
Engineers in Prof. Shanhui Fan's laboratory have developed an efficient, scalable, in-situ method to train, configure and tune complex photonic circuits for artificial intelligence and machine learning.
Engineers in Prof. Zhenan Bao's lab have developed highly conductive, stretchable composite hydrogel materials that can be used as soft electrodes that match the mechanical properties of a range of biological tissues.
Stanford researchers have patented a hydrogel system which allows for the easy encapsulation of cells and biomolecules without requiring external changes in environmental conditions or exposure to chemical crosslinkers.
Stanford researchers have designed and successfully tested two prototype dynamic surface grasping devices. These devices use opposed pairs of gecko-inspired directional adhesives to attach to any smooth surface.
With energy costs rising and environmental problems worsening, there's a growing need for efficient, scalable, alternative energy. A team of researchers at Stanford University led by Prof.
Stanford researchers at the Vuckovic Lab have created a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (Spins).
A Stanford researcher has developed two advanced approaches for the positron sensitive high-energy photon sensor technology for Positron Emission Tomography (PET).
Current techniques for reconstructing images in positron emission tomography (PET) cannot correctly use events in which at least one photon of a pair has scattered in tissue (also known as scatter coincidence events).