Despite widespread adoption of stationary wireless charging, dynamic wireless power transfer suffers from a sensitivity to relative movement of the device with respect to the power source.
Stanford engineers at Zhenan Bao's laboratory have designed a compliance sensor which can identify softness (compliance) of touched objects and provide human-like sensation to robots and prosthetics.
Stanford researchers have developed a damage free method for activating buried p-type or Mg-doped epitaxial layers in III-nitride devices that improves performance and can reduce device cost when used as edge termination.
Researchers from Stanford and UC Santa Barbara have created a novel robot that blends traditional and soft robotics. This human-scale pneumatic robot can change shape and move independently once inflated, without needing a constant power or air source.
Stanford researchers at the Xing Lab have developed a novel technique to enable retrospective tuning of soft tissue contrast in MRI (i.e. adjusting the contrast after the image acquisition) using a deep learning-based strategy.
An interdisciplinary team of Stanford engineers have developed a low-cost, patented, in situ method to efficiently produce electricity from organic matter such as wastewater.
Stanford researchers have designed a high-voltage cascode GaN/SiC device combining the advantages of both a GaN and an SiC device (i.e. reduced gate loss/simple gate drive requirements)
Researchers at Stanford are advancing a new treatment for heart failure based on the transfer of mitochondria-rich extracellular vesicles from iPSC-derived cardiomyocytes. Heart failure is the leading cause of hospital admission in the U.S.
Engineers at Stanford have invented a smart toilet platform that will autonomously monitor excreted waste from humans. We describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health.
RNA replication and amplification have broad applications across biomedicine, but current methods are limited by a reliance on inefficient, multi-step protocols.
Stanford researchers have developed three novel human reference genome sequences, which will significantly improve the interpretation of the growing genetic data stemming from the human genome project and other related draft sequences.
Researchers at Stanford University have developed a quick, robust, machine learning based method for linear accelerator (LINAC) commissioning and beam data modeling.