Soft robot detects damage and heals itself

Researchers combined optical sensors with a composite material to create a soft robot that can detect when and where it was damaged – and then heal itself on the spot.

Sustainability students bring dead solar panels back to life

Using polyurethane, resin, epoxy – and gallons of wit – the Solar Panel Reboot student team, part of the Cornell University Sustainability Design, provides an afterlife to old, broken photovoltaic boards.

Students design robot to collect microplastics from beaches

The robot’s layered filtration system will gather tiny bits of plastic the size of a sesame seed and smaller, which contaminate ecosystems and damage human and animal health.

Warming climate prompts harmful oxygen loss in lakes

Unrelenting climate change is leading to extended, late-summer weeks of water stratification, which prompts varying degrees of oxygen deprivation in lakes, says new Cornell research.

Data science, wave interaction, semiconductors earn engineering research awards

Data science, molecular mechanisms, unconventional computing for optimization and machine learning, wave interaction with engineered materials, electrocatalysis, and compound semiconductor devices are among some of the research themes that helped six faculty members earn Cornell Engineering Research Excellence Awards.

Around Cornell

Earthquake lab experiments produce aftershock-like behavior

Associate Professor Greg McLaskey ’05 and members of his Cornell Engineering research group have developed a method for mimicking aftershocks, findings that eventually could help scientists better predict earthquakes.

Physicist identifies how electron crystals melt

New research describes a phase in between the liquid and the solid for electron crystals – a liquid crystal state.

Economist Tom Davis dies at 93

Tom E. Davis, professor emeritus of economics in the College of Arts and Sciences who was an expert on economic development in Latin America, died Oct. 27 in Ithaca. He was 93.

Machine learning gives nuanced view of Alzheimer’s stages

A Cornell-led collaboration used machine learning to pinpoint the most accurate means, and timelines, for anticipating the advancement of Alzheimer’s disease.