Researchers in Prof. Julia Salzman's laboratory have developed an efficient statistically driven tool to improve the accuracy of biomolecules in samples that have a wide range of concentrations.
Stanford inventors have developed a deep learning framework that is able to label individual points from 3D Point Clouds that are acquired by various sensors (RGBD sensors, LIDAR sensors, etc.). This framework obtains a point-level fine-grained labeling of 3D Scenes.
Researchers in the Collaborative Haptics and Robotics in Medicine Lab at Stanford University have patented a haptic device that simulates a stroking sensation.
Researchers in Prof. Juan Santiago's laboratory have developed a technique to rapidly preconcentrate and capture biological targets with high specificity and efficiency. The process can be used to reduce reaction times for microarray analyses and affinity chromatography.
Engineers in Prof. Amin Arababian's laboratory have developed a microfluidics system for ultra high-throughput, low-cost, label-free cell detection in liquid biopsies, fetal cell analysis and other applications.
Stanford researchers have developed a wirelessly powered, fully internal implant which allows for optogenetic control of neurons throughout the nervous system in mammals, and in particular, mice.
A team of Stanford engineers has developed an efficient battery that can convert salinity gradient power (a.k.a. “blue energy”) into electricity using low-cost, non-toxic electrode materials.
Stanford researchers have developed a process to simultaneously determine the true qualities of products based on ratings as well as the biases of individuals who provide ratings.
Engineers in Prof. Fritz Prinz's laboratory have developed a low cost, scalable method to fabricate anti-reflective, highly conductive metal silicide nanowires electrodes for photovoltaic cells.
Engineers in Prof. Krishna Saraswat's laboratory have developed a patented heterostructure channel transistor based on III-V semiconductor materials and designed for optimum hole transport.
Stanford researchers at the Dahl Lab have developed a method to reduce artifacts in ultrasound image reconstruction using a trained convolutional neural network (CNN).
Stanford researchers have designed a tunable wedge-based phase mask for 3D super-resolution imaging that can simultaneously determine both the position and rotational mobility of individual light-emitting molecules from a single camera image.
Engineers in Prof. Yi Cui's laboratory have developed a stretchable, stable, high energy density anode to be used in lithium ion batteries that power stretchable electronic devices (e.g., wearable electronics, bendable phones or flexible displays).
Researchers in Prof. Brian Feldman's laboratory have developed a patented drug screen to identify compounds that could potentially treat obesity and metabolic disease by converting cells to calorie-burning brown fat.