Researchers in Prof. Paul George's laboratory have patented a conductive polymer scaffold designed to electrically stimulate neural progenitor cells (NPCs) for enhanced neural regeneration.
Stanford researchers in the Kanan Lab have developed a scalable method for achieving verifiable, safe, and permanent carbon removal at relatively low energy demand.
SPEED+ is an advanced dataset for vision-based spacecraft pose estimation with specific emphasis on evaluating the robustness of Machine Learning (ML) models across the domain gap.
Stanford researchers in the Simon Lab have proposed integrating nonlinear optics within optical resonators in general, and within their small waist resonators in particular.
This approach is feasible because:
Researchers at Stanford University have developed a novel 3D printing method, enabling multiple printheads to collaboratively pattern materials from multiple directions, an 'inwards-out' approach that overcomes previous limitations.
Early detection of ovarian cancer is crucial, with a 5-year survival rate exceeding 90%. Once this early window has been missed, the 5-year survival rate precipitously drops below 50%.
Researchers at Stanford have developed force sensors that can operate on very small physical scales without the need for an external connection or power supply.
Stanford researchers have developed a mechanistic guideline for lithium metal battery electrolyte and separator design to mitigate lithium dendrite growth.
Inventors at Stanford have developed a novel strategy to perform concurrent fluorescence measurements of multiple biological parameters in freely moving and head-restrained animals.
Fiber photometry, a measurement technique that aggregates fluorescence signal using a fiber optic, is a highly pervasive approach in the field of systems neuroscience to study in vivo brain tissue dynamics during ecologically relevant behavior.
Stanford researchers have developed a method for manufacturing high quality multifunctional soft electronic fibers based on conventional microfabrication techniques.
A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.